Pub Date : 2024-10-24DOI: 10.1016/j.pce.2024.103790
Bing Bai, Bixia Zhang, Jing Chen, Hanxiang Feng
Guided by the solidification of loess contaminated with heavy metal ions (HMs), a natural inorganic diatomite (NID) was developed as curing agent under an alkaline activator (AA). The curing time, NID content and AA type on the mechanical properties of contaminated soil and solidification effect of HMs were investigated. The solidification source was analysed by microstructure measurement. As curing time increased, the solidification effect increased, with an optimum curing time of 28 days. The higher the content of NID, the stronger the solidification ability. Nevertheless, the strength showed a tendency of initial increase and subsequent decrease. The strength was maximum when NID content reached 10%. The AA created an alkaline environment to promote solidification. In comparison to Na2SiO3 solution, NaOH solution is more effective in the adsorption of HMs. The larger ionic radius of Pb2+ relative to Cu2+, limited HMs migration ability, thereby facilitating solidification.
以重金属离子（HMs）污染黄土的固化为导向，开发了一种天然无机硅藻土（NID）作为碱性活化剂（AA）下的固化剂。研究了固化时间、NID 含量和 AA 类型对污染土壤力学性能的影响以及 HMs 的固化效果。通过微观结构测量分析了固化源。随着固化时间的增加，固化效应也随之增加，最佳固化时间为 28 天。NID 含量越高，凝固能力越强。然而，强度却呈现出先上升后下降的趋势。当 NID 含量达到 10%时，强度最大。AA 创造了一个促进凝固的碱性环境。与 Na2SiO3 溶液相比，NaOH 溶液对 HMs 的吸附更为有效。相对于 Cu2+，Pb2+ 的离子半径更大，这限制了 HMs 的迁移能力，从而促进了凝固。
{"title":"Development of a natural inorganic diatomite curing agent on heavy metal-contaminated loess","authors":"Bing Bai, Bixia Zhang, Jing Chen, Hanxiang Feng","doi":"10.1016/j.pce.2024.103790","DOIUrl":"10.1016/j.pce.2024.103790","url":null,"abstract":"<div><div>Guided by the solidification of loess contaminated with heavy metal ions (HMs), a natural inorganic diatomite (NID) was developed as curing agent under an alkaline activator (AA). The curing time, NID content and AA type on the mechanical properties of contaminated soil and solidification effect of HMs were investigated. The solidification source was analysed by microstructure measurement. As curing time increased, the solidification effect increased, with an optimum curing time of 28 days. The higher the content of NID, the stronger the solidification ability. Nevertheless, the strength showed a tendency of initial increase and subsequent decrease. The strength was maximum when NID content reached 10%. The AA created an alkaline environment to promote solidification. In comparison to Na<sub>2</sub>SiO<sub>3</sub> solution, NaOH solution is more effective in the adsorption of HMs. The larger ionic radius of Pb<sup>2+</sup> relative to Cu<sup>2+</sup>, limited HMs migration ability, thereby facilitating solidification.</div></div>","PeriodicalId":54616,"journal":{"name":"Physics and Chemistry of the Earth","volume":"136 ","pages":"Article 103790"},"PeriodicalIF":3.0,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142539590","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-24DOI: 10.1016/j.pce.2024.103794
Ali El Bilali , Abdeslam Taleb
The use of brackish water resources in agriculture is a promising alternative to overcome water scarcity issues under global change and to implement Sustainable Development Goal (SDG) target 6.3. Meanwhile, according to the World Bank report in 2020, bad water quality can lead to the worldwide loss of food up to 9.54 trillion kilocalories per year. The rapid development of Artificial Intelligence-based technologies is a promising opportunity to modernize irrigation water quality (IWQ) management. This review endeavors to provide a comprehensive overview of the extent to which Machine Learning (ML) models overcome the limitations of conventional methods. This paper began with an introduction section focusing on the background research, followed by a bibliometric analysis of IWQ. Subsequently, a comprehensive review is presented, including discussions on model performances, data availability, and existing limitations. The review revealed that there is a potential accuracy of the ML models to develop ML-based sensor technologies for monitoring IWQ. However, it highlights the need to improve the applicability of ML models through selecting appropriate input and output variables, as it was approved that the efficiency of ML models not only depends on the prediction accuracy but also on the used variables. Overall, this review presents prospective directions to overcome the current limitations with a particular focus on the practical application and integration of the ML models into innovative technologies to manage IWQ.
在农业中利用微咸水资源是解决全球变化带来的水资源短缺问题和实现可持续发展目标（SDG）第 6.3 项具体目标的一种有前途的替代方法。同时，根据世界银行 2020 年的报告，糟糕的水质每年可导致全球粮食损失高达 9.54 万亿千卡。以人工智能为基础的技术的快速发展为灌溉水质量（IWQ）管理的现代化带来了大好机会。本综述旨在全面概述机器学习（ML）模型在多大程度上克服了传统方法的局限性。本文首先介绍了背景研究，然后对灌溉水质量进行了文献计量分析。随后，对模型的性能、数据可用性和现有局限性进行了讨论。综述显示，ML 模型在开发基于 ML 的传感器技术以监测 IWQ 方面具有潜在的准确性。然而，综述强调需要通过选择适当的输入和输出变量来提高 ML 模型的适用性，因为综述认为 ML 模型的效率不仅取决于预测精度，还取决于所使用的变量。总之，本综述提出了克服当前局限性的前瞻性方向，尤其侧重于将 ML 模型实际应用和集成到创新技术中，以管理 IWQ。
{"title":"State-of-the art-on irrigation water quality management using data-driven methods: Practical application, limitations, and prospective directions","authors":"Ali El Bilali , Abdeslam Taleb","doi":"10.1016/j.pce.2024.103794","DOIUrl":"10.1016/j.pce.2024.103794","url":null,"abstract":"<div><div>The use of brackish water resources in agriculture is a promising alternative to overcome water scarcity issues under global change and to implement Sustainable Development Goal (SDG) target 6.3. Meanwhile, according to the World Bank report in 2020, bad water quality can lead to the worldwide loss of food up to 9.54 trillion kilocalories per year. The rapid development of Artificial Intelligence-based technologies is a promising opportunity to modernize irrigation water quality (IWQ) management. This review endeavors to provide a comprehensive overview of the extent to which Machine Learning (ML) models overcome the limitations of conventional methods. This paper began with an introduction section focusing on the background research, followed by a bibliometric analysis of IWQ. Subsequently, a comprehensive review is presented, including discussions on model performances, data availability, and existing limitations. The review revealed that there is a potential accuracy of the ML models to develop ML-based sensor technologies for monitoring IWQ. However, it highlights the need to improve the applicability of ML models through selecting appropriate input and output variables, as it was approved that the efficiency of ML models not only depends on the prediction accuracy but also on the used variables. Overall, this review presents prospective directions to overcome the current limitations with a particular focus on the practical application and integration of the ML models into innovative technologies to manage IWQ.</div></div>","PeriodicalId":54616,"journal":{"name":"Physics and Chemistry of the Earth","volume":"136 ","pages":"Article 103794"},"PeriodicalIF":3.0,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142553950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-23DOI: 10.1016/j.pce.2024.103786
Muhammad Farhan Ul Moazzam , Abhishek Banerjee , Ghani Rahman , Byung Gul Lee
In the present study, Improved Moderate Resolution Imaging Spectro-radiometer (MODIS) snow cover product (MOYDGL06∗) has been used to evaluate the snow cover area (SCA) in Kabul, Jhelum, and Indus river basins for the time period of 2003–2020 with available MODIS land surface temperature (LST), and CHIRPS (precipitation) with objectives to evaluate the spatio-temporal SCA, and climate variables with respect to different elevations analyzed from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Model Version 3 (GDEM v3) and also to correlate the climatic variables with SCA. The results presented average annual SCA is around 50.7%–64.7% in sub-basins of UIB, further it has been observed that SCA is decreasing on annual and seasonal timescale in all three basins. Elevation-dependent SCA, temperature, and precipitation presented a mix of trend on annual, seasonal, and monthly timescale at lower and higher altitude in all selected basins. Moreover, it was noticed that topography (slope, & aspect) also influences the SCA in the region. Furthermore, it has been examined that temperature has significant inverse relationship with SCA at middle and higher altitude in Indus, while in Kabul, and Jhelum no significant relationship observed at extreme lower and higher altitudes. It is also evident from relationship between SCA and climate variable that temperature is significantly responsible for decreasing trend of SCA rather than intense precipitation in all three river basins. Thus, all these elevation-dependent changes can improve our hydrological understanding which can have a considerable implication for hydrology, climate science, water resource management and socio-economic activities.
{"title":"Elevation-dependent snow cover dynamics and associated topo-climate impacts in upper Indus River basin","authors":"Muhammad Farhan Ul Moazzam , Abhishek Banerjee , Ghani Rahman , Byung Gul Lee","doi":"10.1016/j.pce.2024.103786","DOIUrl":"10.1016/j.pce.2024.103786","url":null,"abstract":"<div><div>In the present study, Improved Moderate Resolution Imaging Spectro-radiometer (MODIS) snow cover product (MOYDGL06∗) has been used to evaluate the snow cover area (SCA) in Kabul, Jhelum, and Indus river basins for the time period of 2003–2020 with available MODIS land surface temperature (LST), and CHIRPS (precipitation) with objectives to evaluate the spatio-temporal SCA, and climate variables with respect to different elevations analyzed from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Model Version 3 (GDEM v3) and also to correlate the climatic variables with SCA. The results presented average annual SCA is around 50.7%–64.7% in sub-basins of UIB, further it has been observed that SCA is decreasing on annual and seasonal timescale in all three basins. Elevation-dependent SCA, temperature, and precipitation presented a mix of trend on annual, seasonal, and monthly timescale at lower and higher altitude in all selected basins. Moreover, it was noticed that topography (slope, & aspect) also influences the SCA in the region. Furthermore, it has been examined that temperature has significant inverse relationship with SCA at middle and higher altitude in Indus, while in Kabul, and Jhelum no significant relationship observed at extreme lower and higher altitudes. It is also evident from relationship between SCA and climate variable that temperature is significantly responsible for decreasing trend of SCA rather than intense precipitation in all three river basins. Thus, all these elevation-dependent changes can improve our hydrological understanding which can have a considerable implication for hydrology, climate science, water resource management and socio-economic activities.</div></div>","PeriodicalId":54616,"journal":{"name":"Physics and Chemistry of the Earth","volume":"136 ","pages":"Article 103786"},"PeriodicalIF":3.0,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142578566","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The Madhumati River, located on the lower course of the Gorai River, experiences significant erosion and accretion, leading to annual changes in its morphological characteristics within the surrounding catchment area. Our study utilized Landsat satellite data and the ArcGIS platform to investigate the morpho-dynamic alterations and meander-bend formation mechanisms of the Madhumati River. Over a period of 43 years, from 1980 to 2023, we collected cloud-free images from Landsat 3, Landsat 5, Landsat 8, and Landsat 9 using the USGS Earth Explorer. River masks were then generated using the Water Ratio Index (WRI) and Sinuosity Index (SI) methods. In addition, each bend of the river was individually digitized to understand the bend development process, rate of movement, erosion and accretion, changes in river width, and sinuosity. Our findings reveal a gradual increase in river migration over the study period, attributed to significant erosion and accretion occurring at each bend. This research indicates a greater amount of erosion and accretion in river bends, with total sediment deposition exceeding net erosion throughout the study period. Most meandering bends have experienced considerable narrowing, indicating progressive river constriction over time. The construction of the Farakka Barrage contributed to higher sediment deposition from 1980 to 1990, whereas the Kamarkhali Bridge construction provoked an increasing amount of erosion from 1990 to 2010. Sediment deposition increased between 2010 and 2020. The erosion around the downstream bends grew once again when the investigation was carried up until 2023, proving beyond a doubt that the Kalna Bridge construction had an effect on this erosion rise. The increased sinuosity index of bends suggests heightened meandering. These findings have significant implications for engineering and geological practices, including infrastructure maintenance, expansion planning, riverbank protection measures, and agricultural and land management strategies concerning the Madhumati River.
{"title":"Unraveling meandering river morphodynamics: A geospatial investigation of the Madhumati river in Bangladesh","authors":"Muhtasim Shahriar Mostafa , Md. Jahir Uddin , Md. Nazmul Haque , Muhammad Tauhidur Rahman","doi":"10.1016/j.pce.2024.103788","DOIUrl":"10.1016/j.pce.2024.103788","url":null,"abstract":"<div><div>The Madhumati River, located on the lower course of the Gorai River, experiences significant erosion and accretion, leading to annual changes in its morphological characteristics within the surrounding catchment area. Our study utilized Landsat satellite data and the ArcGIS platform to investigate the morpho-dynamic alterations and meander-bend formation mechanisms of the Madhumati River. Over a period of 43 years, from 1980 to 2023, we collected cloud-free images from Landsat 3, Landsat 5, Landsat 8, and Landsat 9 using the USGS Earth Explorer. River masks were then generated using the Water Ratio Index (WRI) and Sinuosity Index (SI) methods. In addition, each bend of the river was individually digitized to understand the bend development process, rate of movement, erosion and accretion, changes in river width, and sinuosity. Our findings reveal a gradual increase in river migration over the study period, attributed to significant erosion and accretion occurring at each bend. This research indicates a greater amount of erosion and accretion in river bends, with total sediment deposition exceeding net erosion throughout the study period. Most meandering bends have experienced considerable narrowing, indicating progressive river constriction over time. The construction of the Farakka Barrage contributed to higher sediment deposition from 1980 to 1990, whereas the Kamarkhali Bridge construction provoked an increasing amount of erosion from 1990 to 2010. Sediment deposition increased between 2010 and 2020. The erosion around the downstream bends grew once again when the investigation was carried up until 2023, proving beyond a doubt that the Kalna Bridge construction had an effect on this erosion rise. The increased sinuosity index of bends suggests heightened meandering. These findings have significant implications for engineering and geological practices, including infrastructure maintenance, expansion planning, riverbank protection measures, and agricultural and land management strategies concerning the Madhumati River.</div></div>","PeriodicalId":54616,"journal":{"name":"Physics and Chemistry of the Earth","volume":"136 ","pages":"Article 103788"},"PeriodicalIF":3.0,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142539591","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-21DOI: 10.1016/j.pce.2024.103785
Ajay Kumar Taloor , Shiwalika Sambyal , Ravi Sharma , Surya Dev , Sourabh Shastri , Rakesh Kumar
Water is an important natural resource and clean water is vital for maintaining health and hygiene of all living organisms. Estimating and classifying water quality facies is a critical way to analyse water quality and proper water management. The present study underlines the applicability of Machine Learning (ML) models to assess water quality by classifying hydrogeochemical facies within the Tawi basin of the Jammu region. This study employs a range of ML algorithms, including Decision Tree (DT), XGBoost, Random Forest (RF), K-Nearest Neighbors (KNN), and Artificial Neural Network (ANN), to evaluate their effectiveness in accurately classifying hydrogeochemical facies derived from Piper's diagram. The dataset, consisting of chemical parameters extracted from water samples collected from the Tawi basin, was initially imbalanced, with a large majority of samples belonging to a single facies. To address this, we applied the Synthetic Minority Over-sampling Technique (SMOTE), ensuring balanced class distributions for more reliable model training and evaluation. The classification results demonstrate high accuracy across the models, with DT achieving 93%, RF 99%, XGBoost 96%, KNN 81%, and ANN 96%. In addition to overall accuracy, we employed other evaluation metrics such as precision, recall, F1-score, and the precision-recall curve to provide a more comprehensive assessment of model performance. The results underscore the potential of ML in automating water quality assessment based on hydrogeochemical parameters. The findings of the study provide a robust framework for using ML models in determining water quality, particularly in regions where data is scarce and conventional analysis is limited.
水是一种重要的自然资源，清洁的水对维持所有生物的健康和卫生至关重要。对水质面进行估计和分类是分析水质和进行适当水管理的重要方法。本研究通过对查谟地区塔维盆地的水文地质化学面进行分类，强调了机器学习（ML）模型在评估水质方面的适用性。本研究采用了一系列 ML 算法，包括决策树 (DT)、XGBoost、随机森林 (RF)、K-近邻 (KNN) 和人工神经网络 (ANN)，以评估这些算法在对从派珀图中得出的水文地质化学面进行准确分类方面的有效性。该数据集由从塔维盆地采集的水样中提取的化学参数组成，起初并不平衡，绝大多数水样都属于单一水文地质化学面。为解决这一问题，我们采用了合成少数群体过度采样技术（SMOTE），确保类别分布均衡，以进行更可靠的模型训练和评估。分类结果表明，各种模型的准确率都很高，其中 DT 的准确率为 93%，RF 为 99%，XGBoost 为 96%，KNN 为 81%，ANN 为 96%。除总体准确率外，我们还采用了其他评估指标，如精确度、召回率、F1-分数和精确度-召回率曲线，以便对模型性能进行更全面的评估。研究结果凸显了基于水文地质化学参数的 ML 在水质自动评估方面的潜力。研究结果为使用 ML 模型确定水质提供了一个稳健的框架，特别是在数据稀缺和常规分析有限的地区。
{"title":"Advanced hydrogeochemical facies classification: A comparative analysis of Machine Learning models with SMOTE in the Tawi basin","authors":"Ajay Kumar Taloor , Shiwalika Sambyal , Ravi Sharma , Surya Dev , Sourabh Shastri , Rakesh Kumar","doi":"10.1016/j.pce.2024.103785","DOIUrl":"10.1016/j.pce.2024.103785","url":null,"abstract":"<div><div>Water is an important natural resource and clean water is vital for maintaining health and hygiene of all living organisms. Estimating and classifying water quality facies is a critical way to analyse water quality and proper water management. The present study underlines the applicability of Machine Learning (ML) models to assess water quality by classifying hydrogeochemical facies within the Tawi basin of the Jammu region. This study employs a range of ML algorithms, including Decision Tree (DT), XGBoost, Random Forest (RF), K-Nearest Neighbors (KNN), and Artificial Neural Network (ANN), to evaluate their effectiveness in accurately classifying hydrogeochemical facies derived from Piper's diagram. The dataset, consisting of chemical parameters extracted from water samples collected from the Tawi basin, was initially imbalanced, with a large majority of samples belonging to a single facies. To address this, we applied the Synthetic Minority Over-sampling Technique (SMOTE), ensuring balanced class distributions for more reliable model training and evaluation. The classification results demonstrate high accuracy across the models, with DT achieving 93%, RF 99%, XGBoost 96%, KNN 81%, and ANN 96%. In addition to overall accuracy, we employed other evaluation metrics such as precision, recall, F1-score, and the precision-recall curve to provide a more comprehensive assessment of model performance. The results underscore the potential of ML in automating water quality assessment based on hydrogeochemical parameters. The findings of the study provide a robust framework for using ML models in determining water quality, particularly in regions where data is scarce and conventional analysis is limited.</div></div>","PeriodicalId":54616,"journal":{"name":"Physics and Chemistry of the Earth","volume":"137 ","pages":"Article 103785"},"PeriodicalIF":3.0,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142657411","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-20DOI: 10.1016/j.pce.2024.103783
Afnan Al-Hunaiti , Zaid Bakri , Xinyang Li , Lian Duan , Asal Al-Abdallat , Andres Alastuey , Mar Viana , Sharif Arar , Tuukka Petäjä , Tareq Hussein
The urban particulate matter (PM) carbonaceous and water-soluble ions were investigated in Amman, Jordan during May 2018–March 2019. The PM2.5 total carbon (TC) annual mean was 7.6 ± 3.6 μg/m3 (organic carbon (OC) 5.9 ± 2.8 μg/m3 and elemental carbon (EC) 1.7 ± 1.1 μg/m3), which was about 16.3% of the PM2.5. The PM10 TC annual mean was 8.4 ± 3.9 μg/m3 (OC 6.5 ± 3.1 μg/m3 and elemental carbon (EC) 1.9 ± 1.1 μg/m3), about 13.3% of the PM10. The PM2.5 total water-soluble ions annual mean was 7.9 ± 1.9 μg/m3 (about 16.9%), and that of the PM10 was 10.1 ± 2.8 μg/m3 (about 16.0%). The minor ions (F−, NO2−, Br−, and PO43−) constituted less than 1% in the PM fractions. The significant fraction was for SO42− (PM2.5 4.7 ± 1.6 μg/m3 (10.0%) and PM10 5.3 ± 1.9 μg/m3 (8.3%)). The NH4+ had higher amounts of PM2.5 (1.3 ± 0.6 μg/m3; 2.7%) than that PM10 (0.9 ± 0.4 μg/m3; 1.4%). During sand and dust storm (SDS) events, TC, Cl−, and NO3− were doubled in PM, SO42− did not increase significantly, and NH4+ slightly decreased. Regression analysis revealed: (1) carbonaceous aerosols come equally from primary and secondary sources, (2) about 50% of the OC came from non-combustion sources, (3) traffic emissions dominate the PM, (4) agricultural sources have a negligible effect, (5) SO42− is completely neutralized by NH4+ in the PM2.5 but there could be additional reactions involved in the PM10, and (6) (NH4)2SO4, was the major species formed by SO42−and NH4+ instead of NH4HSO4. It is recommended to perform long-term sampling and chemical speciation for the urban atmosphere in Jordan.
{"title":"Characterization of water-soluble inorganic ions and carbonaceous aerosols in the urban atmosphere in Amman, Jordan","authors":"Afnan Al-Hunaiti , Zaid Bakri , Xinyang Li , Lian Duan , Asal Al-Abdallat , Andres Alastuey , Mar Viana , Sharif Arar , Tuukka Petäjä , Tareq Hussein","doi":"10.1016/j.pce.2024.103783","DOIUrl":"10.1016/j.pce.2024.103783","url":null,"abstract":"<div><div>The urban particulate matter (PM) carbonaceous and water-soluble ions were investigated in Amman, Jordan during May 2018–March 2019. The PM<sub>2.5</sub> total carbon (TC) annual mean was 7.6 ± 3.6 μg/m<sup>3</sup> (organic carbon (OC) 5.9 ± 2.8 μg/m<sup>3</sup> and elemental carbon (EC) 1.7 ± 1.1 μg/m<sup>3</sup>), which was about 16.3% of the PM<sub>2.5</sub>. The PM<sub>10</sub> TC annual mean was 8.4 ± 3.9 μg/m<sup>3</sup> (OC 6.5 ± 3.1 μg/m<sup>3</sup> and elemental carbon (EC) 1.9 ± 1.1 μg/m<sup>3</sup>), about 13.3% of the PM<sub>10</sub>. The PM<sub>2.5</sub> total water-soluble ions annual mean was 7.9 ± 1.9 μg/m<sup>3</sup> (about 16.9%), and that of the PM<sub>10</sub> was 10.1 ± 2.8 μg/m<sup>3</sup> (about 16.0%). The minor ions (F<sup>−</sup>, NO<sub>2</sub><sup>−</sup>, Br<sup>−</sup>, and PO<sub>4</sub><sup>3−</sup>) constituted less than 1% in the PM fractions. The significant fraction was for SO<sub>4</sub><sup>2−</sup> (PM<sub>2.5</sub> 4.7 ± 1.6 μg/m<sup>3</sup> (10.0%) and PM<sub>10</sub> 5.3 ± 1.9 μg/m<sup>3</sup> (8.3%)). The NH<sub>4</sub><sup>+</sup> had higher amounts of PM<sub>2.5</sub> (1.3 ± 0.6 μg/m3; 2.7%) than that PM<sub>10</sub> (0.9 ± 0.4 μg/m<sup>3</sup>; 1.4%). During sand and dust storm (SDS) events, TC, Cl<sup>−</sup>, and NO<sub>3</sub><sup>−</sup> were doubled in PM, SO<sub>4</sub><sup>2−</sup> did not increase significantly, and NH<sub>4</sub><sup>+</sup> slightly decreased. Regression analysis revealed: (1) carbonaceous aerosols come equally from primary and secondary sources, (2) about 50% of the OC came from non-combustion sources, (3) traffic emissions dominate the PM, (4) agricultural sources have a negligible effect, (5) SO<sub>4</sub><sup>2−</sup> is completely neutralized by NH<sub>4</sub><sup>+</sup> in the PM<sub>2.5</sub> but there could be additional reactions involved in the PM<sub>10</sub>, and (6) (NH<sub>4</sub>)<sub>2</sub>SO<sub>4</sub>, was the major species formed by SO<sub>4</sub><sup>2−</sup>and NH<sub>4</sub><sup>+</sup> instead of NH<sub>4</sub>HSO<sub>4</sub>. It is recommended to perform long-term sampling and chemical speciation for the urban atmosphere in Jordan.</div></div>","PeriodicalId":54616,"journal":{"name":"Physics and Chemistry of the Earth","volume":"136 ","pages":"Article 103783"},"PeriodicalIF":3.0,"publicationDate":"2024-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142539589","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Air pollution is an important worldwide issue, especially pronounced in metropolitan and suburban regions, significantly affecting both public health and surroundings. This study investigates the particles' morphology and elemental analysis in Varanasi, a highly inhabited metropolis in the Indo-Gangetic Plain. The research was conducted over a year, from April 2019 to March 2020, utilizing Scanning Electron Microscopy with Energy Dispersive X-ray Spectroscopy, Ion Chromatography, and Atomic Absorption Spectroscopy to analyse particulate matter. Results indicated that mean values of PM2.5 and PM10 were 106.5 ± 67.2μg/m³ and 180.8 ± 71.4 μg/m³, respectively. Often, these amounts exceeded the National Ambient Air Quality Standards. SEM-EDX analysis revealed diverse particle morphologies, with significant contributions from both manmade sources including industrial activities and vehicle emissions, and natural sources, like soil dust. Elemental analysis identified major components, including Carbon, Oxygen, Fluorine, Aluminium, and Silicon. IC analysis highlighted dominant ionic species, such as Ca++, SO4−-, NO3−, and Cl−, with monthly variations reflecting different emission sources. Heavy metals concentrations such as Ni, Cd, Cr, Mn, Cu, Pb, Zn, and Fe were quantified, with concentrations varying significantly across months. The findings underscore the complex nature of aerosols in Varanasi and highlight the immediate need for targeted control over air quality measures to minimize the particulate matter's detrimental effects on the local population and ecosystem.
{"title":"Characterization and impact of airborne particulate matter over Varanasi: A year-long study on concentration, morphology, and elemental composition","authors":"Prashant Kumar Chauhan , Dileep Kumar Gupta , Abhay Kumar Singh","doi":"10.1016/j.pce.2024.103782","DOIUrl":"10.1016/j.pce.2024.103782","url":null,"abstract":"<div><div>Air pollution is an important worldwide issue, especially pronounced in metropolitan and suburban regions, significantly affecting both public health and surroundings. This study investigates the particles' morphology and elemental analysis in Varanasi, a highly inhabited metropolis in the Indo-Gangetic Plain. The research was conducted over a year, from April 2019 to March 2020, utilizing Scanning Electron Microscopy with Energy Dispersive X-ray Spectroscopy, Ion Chromatography, and Atomic Absorption Spectroscopy to analyse particulate matter. Results indicated that mean values of PM<sub>2.5</sub> and PM<sub>10</sub> were 106.5 ± 67.2μg/m³ and 180.8 ± 71.4 μg/m³, respectively. Often, these amounts exceeded the National Ambient Air Quality Standards. SEM-EDX analysis revealed diverse particle morphologies, with significant contributions from both manmade sources including industrial activities and vehicle emissions, and natural sources, like soil dust. Elemental analysis identified major components, including Carbon, Oxygen, Fluorine, Aluminium, and Silicon. IC analysis highlighted dominant ionic species, such as Ca<sup>++</sup>, SO<sub>4</sub><sup>−-</sup>, NO<sub>3</sub><sup>−</sup>, and Cl<sup>−</sup>, with monthly variations reflecting different emission sources. Heavy metals concentrations such as Ni, Cd, Cr, Mn, Cu, Pb, Zn, and Fe were quantified, with concentrations varying significantly across months. The findings underscore the complex nature of aerosols in Varanasi and highlight the immediate need for targeted control over air quality measures to minimize the particulate matter's detrimental effects on the local population and ecosystem.</div></div>","PeriodicalId":54616,"journal":{"name":"Physics and Chemistry of the Earth","volume":"136 ","pages":"Article 103782"},"PeriodicalIF":3.0,"publicationDate":"2024-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142539535","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study was conducted to assess the abundance of microplastics and associated metal contamination at selected beaches in the Western Province of Sri Lanka. Beach sand samples were collected from four beaches: Modera, Negombo, Mount Lavinia, and Panadura. Microplastics were extracted from dried sand samples using a saturated NaCl solution, followed by sieving. Particles were identified using Fourier Transform InfraRed Spectrophotometer, and associated heavy metals; Cr, Pb, Cu, Zn, and Ni were subjected to acid digestion for 24 h before analysis using Microwave Plasma Atomic Emission Spectrometry. More than half of the extracted plastics (56.31%) were identified as microplastics. The average microplastic abundance in beach sand samples ranged from 42.0 to 91.3 items/kg. The sand collected at Mount Lavinia exhibited the lowest sbundance, whereas those from Panadura beach revealed the highest. Hydrodynamic factors like ocean currents, wave patterns, associated with Southwest monsoon period, and human activities may have caused the variability in microplastic abundances and metal contamination. The majority of the microplastics (52.29%) were polyethylene, followed by polypropylene (35.18%), resembling the records of the most common plastic waste types in the country. Most of the microplastics were found to be fragments (87.95%), while white being the prominent color (53.49%). The toxic trace element concentration ranged from 5.0 × 10−3 to 1.8 × 102 μg/g in beaches. This study establishes a baseline for the west coastline prior to the X-press Pearl Ship Disaster in 2021. Future studies are encouraged to assess the beach microplastic pollution across the- Sri Lankan coastline.
{"title":"Microplastic abundance, characteristics, and heavy metal contamination in coastal environments of Western Sri Lanka","authors":"Hansika Piyumali , Madushika Sewwandi , Thilakshani Atugoda , Hasintha Wijesekara , Kushani Mahatantila , Meththika Vithanage","doi":"10.1016/j.pce.2024.103770","DOIUrl":"10.1016/j.pce.2024.103770","url":null,"abstract":"<div><div>This study was conducted to assess the abundance of microplastics and associated metal contamination at selected beaches in the Western Province of Sri Lanka. Beach sand samples were collected from four beaches: Modera, Negombo, Mount Lavinia, and Panadura. Microplastics were extracted from dried sand samples using a saturated NaCl solution, followed by sieving. Particles were identified using Fourier Transform InfraRed Spectrophotometer, and associated heavy metals; Cr, Pb, Cu, Zn, and Ni were subjected to acid digestion for 24 h before analysis using Microwave Plasma Atomic Emission Spectrometry. More than half of the extracted plastics (56.31%) were identified as microplastics. The average microplastic abundance in beach sand samples ranged from 42.0 to 91.3 items/kg. The sand collected at Mount Lavinia exhibited the lowest sbundance, whereas those from Panadura beach revealed the highest. Hydrodynamic factors like ocean currents, wave patterns, associated with Southwest monsoon period, and human activities may have caused the variability in microplastic abundances and metal contamination. The majority of the microplastics (52.29%) were polyethylene, followed by polypropylene (35.18%), resembling the records of the most common plastic waste types in the country. Most of the microplastics were found to be fragments (87.95%), while white being the prominent color (53.49%). The toxic trace element concentration ranged from 5.0 × 10<sup>−3</sup> to 1.8 × 10<sup>2</sup> μg/g in beaches. This study establishes a baseline for the west coastline prior to the X-press Pearl Ship Disaster in 2021. Future studies are encouraged to assess the beach microplastic pollution across the- Sri Lankan coastline.</div></div>","PeriodicalId":54616,"journal":{"name":"Physics and Chemistry of the Earth","volume":"136 ","pages":"Article 103770"},"PeriodicalIF":3.0,"publicationDate":"2024-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142553952","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Frequent flooding has become a persistent issue in floodplain regions, causing significant disasters during each rainy season due to insufficient disaster management planning. This study proposes a methodology to prioritize flood susceptibility areas at the district level and identify suitable sites for flood shelters using a combination of machine learning algorithms and multi-criteria analysis, supported by geospatial technology. Flood shelter suitability mapping was conducted using the Analytical Hierarchy Process (AHP), while flood susceptibility zones were assessed using four different machine learning models: Support Vector Machine (SVM), Random Forest, Decision Tree, and Naive Bayes. The integration of machine learning models with the AHP technique is vital in situations where conventional numerical models face challenges due to limited data, such as river discharge and water levels. The methodology includes a multicollinearity assessment to ensure the independence of selected flood-causing factors, information gain ratio to identify the most influential factors, Spearman's rho test to verify correlations between the machine learning models, and ROC-AUC along with statistical regression for validating the accuracy of the flood susceptibility maps. The findings indicate that the SVM algorithm, given its strong performance and effective training datasets, is recommended for areas with similar physical characteristics. The district-wise priority map generated from the weighted results of flood susceptibility assessments will be useful for flood management and mitigation strategies. Additionally, the study found that applying the AHP technique to determine flood shelter suitability, after assessing flood-prone areas, enhanced the efficiency of the flood management process. This research offers valuable insights for authorities to better address flooding and improve flood prevention and management efforts in floodplain regions, contributing to broader climate change adaptation strategies.
{"title":"Assessing critical flood-prone districts and optimal shelter zones in the Brahmaputra Valley: Strategies for effective flood risk management","authors":"Jatan Debnath , Dhrubajyoti Sahariah , Gowhar Meraj , Kesar Chand , Suraj Kumar Singh , Shruti Kanga , Pankaj Kumar","doi":"10.1016/j.pce.2024.103772","DOIUrl":"10.1016/j.pce.2024.103772","url":null,"abstract":"<div><div>Frequent flooding has become a persistent issue in floodplain regions, causing significant disasters during each rainy season due to insufficient disaster management planning. This study proposes a methodology to prioritize flood susceptibility areas at the district level and identify suitable sites for flood shelters using a combination of machine learning algorithms and multi-criteria analysis, supported by geospatial technology. Flood shelter suitability mapping was conducted using the Analytical Hierarchy Process (AHP), while flood susceptibility zones were assessed using four different machine learning models: Support Vector Machine (SVM), Random Forest, Decision Tree, and Naive Bayes. The integration of machine learning models with the AHP technique is vital in situations where conventional numerical models face challenges due to limited data, such as river discharge and water levels. The methodology includes a multicollinearity assessment to ensure the independence of selected flood-causing factors, information gain ratio to identify the most influential factors, Spearman's rho test to verify correlations between the machine learning models, and ROC-AUC along with statistical regression for validating the accuracy of the flood susceptibility maps. The findings indicate that the SVM algorithm, given its strong performance and effective training datasets, is recommended for areas with similar physical characteristics. The district-wise priority map generated from the weighted results of flood susceptibility assessments will be useful for flood management and mitigation strategies. Additionally, the study found that applying the AHP technique to determine flood shelter suitability, after assessing flood-prone areas, enhanced the efficiency of the flood management process. This research offers valuable insights for authorities to better address flooding and improve flood prevention and management efforts in floodplain regions, contributing to broader climate change adaptation strategies.</div></div>","PeriodicalId":54616,"journal":{"name":"Physics and Chemistry of the Earth","volume":"136 ","pages":"Article 103772"},"PeriodicalIF":3.0,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142539533","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-18DOI: 10.1016/j.pce.2024.103778
Kholiswa Yokwana , Hideaki Nagare , Bulelwa Ntsendwana , Adeniyi S. Ogunlaja , Sabelo D. Mhlanga
Industrialization has led to generation of large quantities of waste which constitutes various toxic heavy metals such as lead (Pb). In this work, novel bio-nanostructured graphene-based microalgae nanohybrid adsorbents, using three different cell types of Haematococcus lacustris (i.e., flagella (flg-C), palmella (Pal-C) and cyst (Cyst-C)) to introduce more functional moieties and enhance the surface properties of the nanohybrids. The nanostructured graphene oxide-activated carbon modified with algae cells (GO-AC@algae) and graphene nanoplatelets-activated carbon modified with algae cells (GNPs-AC@algae) nanohybrids were characterized and used for the removal of Pb ions. The GO-AC@algae nanohybrids demonstrated a high lead removal efficiency of over 98.0%, whereas the GNPs-AC@algae nanohybrids achieved more than 85.0%. Among the GO-AC@algae nanohybrids, the nanohybrid with cyst cell (GO-AC@Cyst-C) shown remarkable efficacy as an adsorbent for the removal of Pb2+ ions from aqueous solutions due to its high specific area, abundance of oxygen-nitrogen-based functional moieties, hydrophilicity, and pore structure. Chemisorption was found to be a beneficial process for both GO-AC@algae and GNPs-AC@algae samples, where Pb2+ was adsorbed in a single layer onto the uniform material surface. Among the various adsorbents, GO-AC@Cyst-C achieved the highest monolayer adsorption capacity of 25.58 mg/g according to the Langmuir model, making it the most effective nanoadsorbents. Kinetic studies revealed that the sorption mechanism of GO-AC@algae were better described by the second-order kinetic model. Meanwhile, the first-order kinetic model was found to be suited for GNPs-AC@algae samples. The nanohybrids could be employed as greener adsorbents at industrial scale for wastewater treatment without incurring significant costs.
{"title":"Flagella, palmella and cyst Haematococcus lacustris microalgae cells decorated on graphene oxide and graphene nanoplatelets-activated carbon as novel adsorbents for the removal of lead from water","authors":"Kholiswa Yokwana , Hideaki Nagare , Bulelwa Ntsendwana , Adeniyi S. Ogunlaja , Sabelo D. Mhlanga","doi":"10.1016/j.pce.2024.103778","DOIUrl":"10.1016/j.pce.2024.103778","url":null,"abstract":"<div><div>Industrialization has led to generation of large quantities of waste which constitutes various toxic heavy metals such as lead (Pb). In this work, novel bio-nanostructured graphene-based microalgae nanohybrid adsorbents, using three different cell types of <em>Haematococcus lacustris</em> (<em>i.e.</em>, flagella (flg-C), palmella (Pal-C) and cyst (Cyst-C)) to introduce more functional moieties and enhance the surface properties of the nanohybrids. The nanostructured graphene oxide-activated carbon modified with algae cells (GO-AC@algae) and graphene nanoplatelets-activated carbon modified with algae cells (GNPs-AC@algae) nanohybrids were characterized and used for the removal of Pb ions. The GO-AC@algae nanohybrids demonstrated a high lead removal efficiency of over 98.0%, whereas the GNPs-AC@algae nanohybrids achieved more than 85.0%. Among the GO-AC@algae nanohybrids, the nanohybrid with cyst cell (GO-AC@Cyst-C) shown remarkable efficacy as an adsorbent for the removal of Pb<sup>2+</sup> ions from aqueous solutions due to its high specific area, abundance of oxygen-nitrogen-based functional moieties, hydrophilicity, and pore structure. Chemisorption was found to be a beneficial process for both GO-AC@algae and GNPs-AC@algae samples, where Pb<sup>2+</sup> was adsorbed in a single layer onto the uniform material surface. Among the various adsorbents, GO-AC@Cyst-C achieved the highest monolayer adsorption capacity of 25.58 mg/g according to the Langmuir model, making it the most effective nanoadsorbents. Kinetic studies revealed that the sorption mechanism of GO-AC@algae were better described by the second-order kinetic model. Meanwhile, the first-order kinetic model was found to be suited for GNPs-AC@algae samples. The nanohybrids could be employed as greener adsorbents at industrial scale for wastewater treatment without incurring significant costs.</div></div>","PeriodicalId":54616,"journal":{"name":"Physics and Chemistry of the Earth","volume":"137 ","pages":"Article 103778"},"PeriodicalIF":3.0,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142705047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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