Pub Date : 2024-05-01DOI: 10.32526/ennrj/22/20230300
Zeni Anggraini, Muhayatun Santoso, A. Sofyan
Air pollution around industrial area has become a serious concern for both the public and local government. Thus, research on PM2.5 characterization is urgently needed. This study identifies the concentration and chemical characteristics of PM2.5 to provide an in-depth understanding of the composition of these particles around the largest industrial complex in North Jakarta. Sixty samples of PM2.5 were collected from residential sites around industrial areas in North Jakarta. Samples were collected on Teflon filters using a SuperSASS instrument during the period from February to July 2023, representing the wet and dry seasons. Mass concentrations of PM2.5, black carbon, and 19 chemical elements were determined. The average mass concentration of PM2.5 in the wet and dry seasons was 27.81±11.82 µg/m3 and 46.63±14.37 µg/m3, respectively. Although the concentration of PM2.5 was lower during the wet season, the concentrations of black carbon and certain elements did not decrease significantly. This shows that pollutants play an important role in both seasons in the study location. Sulfur is the most abundant element with the average concentration in the dry season (2,727.89 ng/m3) higher than in the wet season (1,983.18 ng/m3). The PM2.5 mass reconstruction results show that ammonium sulfate and black carbon have the largest portion of PM2.5 mass. The results are expected to be used as a scientific reference in studying air pollution problems in this region and assist in formulating air protection policies to reduce PM2.5 emissions.
{"title":"Characteristics of Fine Particulate Matter (PM2.5) Chemical Composition in the North Jakarta Industrial Area","authors":"Zeni Anggraini, Muhayatun Santoso, A. Sofyan","doi":"10.32526/ennrj/22/20230300","DOIUrl":"https://doi.org/10.32526/ennrj/22/20230300","url":null,"abstract":"Air pollution around industrial area has become a serious concern for both the public and local government. Thus, research on PM2.5 characterization is urgently needed. This study identifies the concentration and chemical characteristics of PM2.5 to provide an in-depth understanding of the composition of these particles around the largest industrial complex in North Jakarta. Sixty samples of PM2.5 were collected from residential sites around industrial areas in North Jakarta. Samples were collected on Teflon filters using a SuperSASS instrument during the period from February to July 2023, representing the wet and dry seasons. Mass concentrations of PM2.5, black carbon, and 19 chemical elements were determined. The average mass concentration of PM2.5 in the wet and dry seasons was 27.81±11.82 µg/m3 and 46.63±14.37 µg/m3, respectively. Although the concentration of PM2.5 was lower during the wet season, the concentrations of black carbon and certain elements did not decrease significantly. This shows that pollutants play an important role in both seasons in the study location. Sulfur is the most abundant element with the average concentration in the dry season (2,727.89 ng/m3) higher than in the wet season (1,983.18 ng/m3). The PM2.5 mass reconstruction results show that ammonium sulfate and black carbon have the largest portion of PM2.5 mass. The results are expected to be used as a scientific reference in studying air pollution problems in this region and assist in formulating air protection policies to reduce PM2.5 emissions.","PeriodicalId":11784,"journal":{"name":"Environment and Natural Resources Journal","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":"141025579","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: 10.32526/ennrj/22/20230256
Mary Sheenalyn P. Rodil, Corazon D. Sacdalan, Rissabell R. Robero, Maria Evytha L. Salinas, Trixie N. Santander
Methyl violet 6B (MV6B), found in wastewater, poses hazardous effects to aquatic ecosystems and human health; therefore, it must be removed immediately. In response, this study pioneered the development of a dye adsorbent by incorporating phenolated alkali lignin (PAL) into magnetite (Fe3O4), offering a solution for MV6B removal. Lignin was extracted from coconut husk through alkali extraction, chemically modified using phenolation, and integrated onto the magnetite surface. SEM and FTIR spectroscopy were used to characterize the adsorbent, and various parameters were optimized, along with evaluations of the adsorption kinetics and isotherm models, as well as the adsorbent’s reusability. PAL was successfully deposited onto the magnetite based on the characterization. The experimental results revealed that the optimal conditions for the removal of MV6B using PAL/Fe3O4 composite are pH 4, a temperature of 313 K, a dosage of 0.10 g PAL/Fe3O4 per 15 mL of MV6B, and a contact time of 150 minutes. MV6B’s equilibrium removal rate was 95.1%, with an adsorption capacity at equilibrium of 6.42 mg/g. The adsorption of MV6B followed a pseudo-second-order kinetic model and the Freundlich model isotherm. A thermodynamic study showed that the adsorption process was spontaneous and exothermic. PAL/Fe3O4 was highly reusable after three cycles without the need for desorption. Hence, this study has demonstrated that the PAL/ Fe3O4 adsorbent is practical, economical, and efficient for wastewater treatment.
{"title":"Phenolated Alkali Lignin/Magnetite Composite as an Adsorbent for Methyl Violet 6B in Wastewater","authors":"Mary Sheenalyn P. Rodil, Corazon D. Sacdalan, Rissabell R. Robero, Maria Evytha L. Salinas, Trixie N. Santander","doi":"10.32526/ennrj/22/20230256","DOIUrl":"https://doi.org/10.32526/ennrj/22/20230256","url":null,"abstract":"Methyl violet 6B (MV6B), found in wastewater, poses hazardous effects to aquatic ecosystems and human health; therefore, it must be removed immediately. In response, this study pioneered the development of a dye adsorbent by incorporating phenolated alkali lignin (PAL) into magnetite (Fe3O4), offering a solution for MV6B removal. Lignin was extracted from coconut husk through alkali extraction, chemically modified using phenolation, and integrated onto the magnetite surface. SEM and FTIR spectroscopy were used to characterize the adsorbent, and various parameters were optimized, along with evaluations of the adsorption kinetics and isotherm models, as well as the adsorbent’s reusability. PAL was successfully deposited onto the magnetite based on the characterization. The experimental results revealed that the optimal conditions for the removal of MV6B using PAL/Fe3O4 composite are pH 4, a temperature of 313 K, a dosage of 0.10 g PAL/Fe3O4 per 15 mL of MV6B, and a contact time of 150 minutes. MV6B’s equilibrium removal rate was 95.1%, with an adsorption capacity at equilibrium of 6.42 mg/g. The adsorption of MV6B followed a pseudo-second-order kinetic model and the Freundlich model isotherm. A thermodynamic study showed that the adsorption process was spontaneous and exothermic. PAL/Fe3O4 was highly reusable after three cycles without the need for desorption. Hence, this study has demonstrated that the PAL/ Fe3O4 adsorbent is practical, economical, and efficient for wastewater treatment.","PeriodicalId":11784,"journal":{"name":"Environment and Natural Resources Journal","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":"141139513","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: 10.32526/ennrj/22/20230282
Tipmol Traiyut, Patranit Srijuntrapun, W. Rawang
This study reflects the experiences of communities who have adapted to climate change in three different geological locations in the country of Thailand: by the riverside, coast, and in the mountains. The communities presented the lessons learned and identified key adaptation factors. The study used in-depth interviews and focus group discussions, with results showing that the community’s learning and adaptation to climate change were at a high level. The results broaden understanding of climate change in these locations and provide information for resource management approaches. Among the seven factors, five factors illustrated that they were highly adapted, including: (1) applying knowledge about nature, ecosystems, and traditional wisdom; (2) management that allowed the use of adaptations; (3) a shared vision of success; (4) collaboration; and (5) having a variety of options and approaches. Two factors that illustated that the community was only moderately adapted included: (1) learning about violent events and disasters; and (2) following government guidelines. It was found that a lack of information about the ecosystems and environmental resources they required for large-scale infrastructure construction caused issues. This is a problem, and the government must consult with local communities when setting long-term plans and assessing needs, because communities have diverse livelihoods and depend on natural resources. Hence, future studies should include climate change awareness and understanding of what is required by adding community needs linked to climate change adaptation into state development plans as well as utilizing the wisdom and traditional knowledge involving ecology held by these communities into sustainability plans.
{"title":"Factors in Community Adaptation for Climate Change Mitigation in Thailand","authors":"Tipmol Traiyut, Patranit Srijuntrapun, W. Rawang","doi":"10.32526/ennrj/22/20230282","DOIUrl":"https://doi.org/10.32526/ennrj/22/20230282","url":null,"abstract":"This study reflects the experiences of communities who have adapted to climate change in three different geological locations in the country of Thailand: by the riverside, coast, and in the mountains. The communities presented the lessons learned and identified key adaptation factors. The study used in-depth interviews and focus group discussions, with results showing that the community’s learning and adaptation to climate change were at a high level. The results broaden understanding of climate change in these locations and provide information for resource management approaches. Among the seven factors, five factors illustrated that they were highly adapted, including: (1) applying knowledge about nature, ecosystems, and traditional wisdom; (2) management that allowed the use of adaptations; (3) a shared vision of success; (4) collaboration; and (5) having a variety of options and approaches. Two factors that illustated that the community was only moderately adapted included: (1) learning about violent events and disasters; and (2) following government guidelines. It was found that a lack of information about the ecosystems and environmental resources they required for large-scale infrastructure construction caused issues. This is a problem, and the government must consult with local communities when setting long-term plans and assessing needs, because communities have diverse livelihoods and depend on natural resources. Hence, future studies should include climate change awareness and understanding of what is required by adding community needs linked to climate change adaptation into state development plans as well as utilizing the wisdom and traditional knowledge involving ecology held by these communities into sustainability plans.","PeriodicalId":11784,"journal":{"name":"Environment and Natural Resources Journal","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":"141052495","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 : 2024-03-01DOI: 10.32526/ennrj/22/20230254
Jan Aizel E. Arellano, Irish Benja M. Argame, Francis Ruel G. Castillo, Christian Geen E. Salazar, Mark Kevin S. Lopez
Excessive deposition of heavy metals into the environment due to anthropogenic activities necessitates an eco-friendly clean-up strategy. Among microorganisms, limited studies have been made on the mycoremediation potential of microfungi. This paper evaluated three landfill microfungal isolates of Aspergillus species for tolerance and uptake to Cd and Mn. Culture media optimization was also performed for the evaluation of the tolerance index and heavy metal analysis of soil samples from the landfill site. Among the nine heavy metals analyzed, Mn and Fe were detected in relatively high amounts, while Cd, Ni, and Cu were detected in a moderate range. Luxuriant mycelial growth of A. oryzae (MK120548.1) and A. flavus (MH864264.1) was observed in potato dextrose agar while A. terreus (MH047280.1) grew best in potato sucrose agar. In terms of tolerance index, A. oryzae (MK120548.1) and A. flavus (MH864264.1) demonstrated high tolerance to Cd up to 10 mg/kg. A. oryzae (MK120548.1) showed high tolerance to Mn up to 1,000 mg/kg while A. flavus (MH864264.1) exhibited a very high 10,000 mg/kg tolerance. In terms of metal uptake, A. oryzae (MK120548.1) showed the highest metal uptake of up to 654 mg/kg of Cd, while A. terreus (MH047280.1) exhibited the highest metal uptake of 997 mg/kg ofMn. With these findings, A. oryzae (MK120548.1), A. flavus (MH864264.1), and A. terreus (MH047280.1) have considerable mycoremediation potential. Bioremediation studies in conjunction with plants can be explored to further assess the potential of these Aspergillus species.
由于人类活动导致重金属过度沉积到环境中，因此有必要采取生态友好型净化策略。在微生物中，有关微真菌菌核修复潜力的研究还很有限。本文评估了三种垃圾填埋场曲霉分离物对镉和锰的耐受性和吸收能力。此外，还对培养基进行了优化，以评估耐受指数和垃圾填埋场土壤样本的重金属分析。在分析的九种重金属中，锰和铁的含量相对较高，而镉、镍和铜的含量则处于中等水平。在马铃薯葡萄糖琼脂中观察到 A. oryzae（MK120548.1）和 A. flavus（MH864264.1）菌丝生长旺盛，而 A. terreus（MH047280.1）在马铃薯蔗糖琼脂中生长最好。就耐受指数而言，A. oryzae（MK120548.1）和 A. flavus（MH864264.1）对镉的耐受性高达 10 mg/kg。奥里藻（MK120548.1）对锰的耐受性高达 1,000 毫克/千克，而黄曲霉（MH864264.1）对锰的耐受性则高达 10,000 毫克/千克。在金属吸收方面，A. oryzae（MK120548.1）对镉的金属吸收量最高，达 654 毫克/千克，而 A. terreus（MH047280.1）对锰的金属吸收量最高，达 997 毫克/千克。根据这些发现，A. oryzae（MK120548.1）、A. flavus（MH864264.1）和 A. terreus（MH047280.1）具有相当大的生物修复潜力。可结合植物进行生物修复研究，以进一步评估这些曲霉菌种的潜力。
{"title":"Evaluation of Tolerance and Uptake of Cd and Mn for Microfungi Aspergillus flavus, Aspergillus oryzae, and Aspergillus terreus Isolated from Landfill Soil Collected from Bangar, La Union Philippines","authors":"Jan Aizel E. Arellano, Irish Benja M. Argame, Francis Ruel G. Castillo, Christian Geen E. Salazar, Mark Kevin S. Lopez","doi":"10.32526/ennrj/22/20230254","DOIUrl":"https://doi.org/10.32526/ennrj/22/20230254","url":null,"abstract":"Excessive deposition of heavy metals into the environment due to anthropogenic activities necessitates an eco-friendly clean-up strategy. Among microorganisms, limited studies have been made on the mycoremediation potential of microfungi. This paper evaluated three landfill microfungal isolates of Aspergillus species for tolerance and uptake to Cd and Mn. Culture media optimization was also performed for the evaluation of the tolerance index and heavy metal analysis of soil samples from the landfill site. Among the nine heavy metals analyzed, Mn and Fe were detected in relatively high amounts, while Cd, Ni, and Cu were detected in a moderate range. Luxuriant mycelial growth of A. oryzae (MK120548.1) and A. flavus (MH864264.1) was observed in potato dextrose agar while A. terreus (MH047280.1) grew best in potato sucrose agar. In terms of tolerance index, A. oryzae (MK120548.1) and A. flavus (MH864264.1) demonstrated high tolerance to Cd up to 10 mg/kg. A. oryzae (MK120548.1) showed high tolerance to Mn up to 1,000 mg/kg while A. flavus (MH864264.1) exhibited a very high 10,000 mg/kg tolerance. In terms of metal uptake, A. oryzae (MK120548.1) showed the highest metal uptake of up to 654 mg/kg of Cd, while A. terreus (MH047280.1) exhibited the highest metal uptake of 997 mg/kg ofMn. With these findings, A. oryzae (MK120548.1), A. flavus (MH864264.1), and A. terreus (MH047280.1) have considerable mycoremediation potential. Bioremediation studies in conjunction with plants can be explored to further assess the potential of these Aspergillus species.","PeriodicalId":11784,"journal":{"name":"Environment and Natural Resources Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140278170","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 : 2024-02-14DOI: 10.32526/ennrj/22/20230310
Butsakorn Yodphet, N. Jangpromma, Wanwipa Kaewpradit Polpinit, N. Riddech
Drought stress is a major agricultural problem that leads to increased accumulation of ethylene in plants. It also has negative effects on plant productivity and growth. Andrographis paniculate is an important herb widely used in medical applications to inhibit diseases caused by viruses. In order to improve the production quality and growth of the A. paniculata, ACC-deaminase plant growth-promoting rhizobacteria were isolated from rice rhizosphere soil. All bacterial isolates were screened for their plant growth-promoting properties, including ACC deaminase, IAA production, biofilm formation, and exopolysaccharide production. Among the bacterial isolates, Rh-01 and Rh-22 exhibited positive results (cutting-edge) in all tests and were identified as Paenibacillus polymyxa Rh-01 and Stenotrophomonas maltophilia Rh-22, respectively. These strains were selected for further pot experiment study. Our results revealed that treatment with chemical fertilizer showed the highest potential to promote A. paniculata seedlings under normal moisture conditions. However, under water limitation conditions, the application of ACC-deaminase plant growth-promoting rhizobacteria led to a higher chlorophyll content compared to the control treatment. In addition, under normal irrigation conditions, plant growth promoting rhizobacterial increased relative water content and total biomass. In terms of plant stress markers, the proline content in Andrographis paniculate’s seedling stage was low under water limitation conditions. In conclusion, to enhance the growth of A. paniculate seedlings during water limitation stress, a combination of microbial biofertilizers and chemical fertilizers is beneficial.
{"title":"Interaction between Rhizobacteria and Andrographis paniculata Under Water Limitation","authors":"Butsakorn Yodphet, N. Jangpromma, Wanwipa Kaewpradit Polpinit, N. Riddech","doi":"10.32526/ennrj/22/20230310","DOIUrl":"https://doi.org/10.32526/ennrj/22/20230310","url":null,"abstract":"Drought stress is a major agricultural problem that leads to increased accumulation of ethylene in plants. It also has negative effects on plant productivity and growth. Andrographis paniculate is an important herb widely used in medical applications to inhibit diseases caused by viruses. In order to improve the production quality and growth of the A. paniculata, ACC-deaminase plant growth-promoting rhizobacteria were isolated from rice rhizosphere soil. All bacterial isolates were screened for their plant growth-promoting properties, including ACC deaminase, IAA production, biofilm formation, and exopolysaccharide production. Among the bacterial isolates, Rh-01 and Rh-22 exhibited positive results (cutting-edge) in all tests and were identified as Paenibacillus polymyxa Rh-01 and Stenotrophomonas maltophilia Rh-22, respectively. These strains were selected for further pot experiment study. Our results revealed that treatment with chemical fertilizer showed the highest potential to promote A. paniculata seedlings under normal moisture conditions. However, under water limitation conditions, the application of ACC-deaminase plant growth-promoting rhizobacteria led to a higher chlorophyll content compared to the control treatment. In addition, under normal irrigation conditions, plant growth promoting rhizobacterial increased relative water content and total biomass. In terms of plant stress markers, the proline content in Andrographis paniculate’s seedling stage was low under water limitation conditions. In conclusion, to enhance the growth of A. paniculate seedlings during water limitation stress, a combination of microbial biofertilizers and chemical fertilizers is beneficial.","PeriodicalId":11784,"journal":{"name":"Environment and Natural Resources Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139777903","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 : 2024-02-14DOI: 10.32526/ennrj/22/20230310
Butsakorn Yodphet, N. Jangpromma, Wanwipa Kaewpradit Polpinit, N. Riddech
Drought stress is a major agricultural problem that leads to increased accumulation of ethylene in plants. It also has negative effects on plant productivity and growth. Andrographis paniculate is an important herb widely used in medical applications to inhibit diseases caused by viruses. In order to improve the production quality and growth of the A. paniculata, ACC-deaminase plant growth-promoting rhizobacteria were isolated from rice rhizosphere soil. All bacterial isolates were screened for their plant growth-promoting properties, including ACC deaminase, IAA production, biofilm formation, and exopolysaccharide production. Among the bacterial isolates, Rh-01 and Rh-22 exhibited positive results (cutting-edge) in all tests and were identified as Paenibacillus polymyxa Rh-01 and Stenotrophomonas maltophilia Rh-22, respectively. These strains were selected for further pot experiment study. Our results revealed that treatment with chemical fertilizer showed the highest potential to promote A. paniculata seedlings under normal moisture conditions. However, under water limitation conditions, the application of ACC-deaminase plant growth-promoting rhizobacteria led to a higher chlorophyll content compared to the control treatment. In addition, under normal irrigation conditions, plant growth promoting rhizobacterial increased relative water content and total biomass. In terms of plant stress markers, the proline content in Andrographis paniculate’s seedling stage was low under water limitation conditions. In conclusion, to enhance the growth of A. paniculate seedlings during water limitation stress, a combination of microbial biofertilizers and chemical fertilizers is beneficial.
{"title":"Interaction between Rhizobacteria and Andrographis paniculata Under Water Limitation","authors":"Butsakorn Yodphet, N. Jangpromma, Wanwipa Kaewpradit Polpinit, N. Riddech","doi":"10.32526/ennrj/22/20230310","DOIUrl":"https://doi.org/10.32526/ennrj/22/20230310","url":null,"abstract":"Drought stress is a major agricultural problem that leads to increased accumulation of ethylene in plants. It also has negative effects on plant productivity and growth. Andrographis paniculate is an important herb widely used in medical applications to inhibit diseases caused by viruses. In order to improve the production quality and growth of the A. paniculata, ACC-deaminase plant growth-promoting rhizobacteria were isolated from rice rhizosphere soil. All bacterial isolates were screened for their plant growth-promoting properties, including ACC deaminase, IAA production, biofilm formation, and exopolysaccharide production. Among the bacterial isolates, Rh-01 and Rh-22 exhibited positive results (cutting-edge) in all tests and were identified as Paenibacillus polymyxa Rh-01 and Stenotrophomonas maltophilia Rh-22, respectively. These strains were selected for further pot experiment study. Our results revealed that treatment with chemical fertilizer showed the highest potential to promote A. paniculata seedlings under normal moisture conditions. However, under water limitation conditions, the application of ACC-deaminase plant growth-promoting rhizobacteria led to a higher chlorophyll content compared to the control treatment. In addition, under normal irrigation conditions, plant growth promoting rhizobacterial increased relative water content and total biomass. In terms of plant stress markers, the proline content in Andrographis paniculate’s seedling stage was low under water limitation conditions. In conclusion, to enhance the growth of A. paniculate seedlings during water limitation stress, a combination of microbial biofertilizers and chemical fertilizers is beneficial.","PeriodicalId":11784,"journal":{"name":"Environment and Natural Resources Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139837579","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 : 2024-02-13DOI: 10.32526/ennrj/22/20230194
B. Thien, Asya E. Ovsepyan, V. T. Phuong
This study utilised remote sensing data and ArcGIS 10.8 software to evaluate changes in land use and land cover (LULC) and their effects on land surface temperature (LST) in Hai Duong Province, Vietnam, from 1992 to 2022. Landsat satellite data were pre-processed and classified using supervised methods for the years 1992, 2010, and 2022. In 1992, vegetation cover accounted for 57.89% of land cover, increasing to 84.49% in 2010, but then decreasing again to 66.67% in 2022. In contrast, the built-up area consistently increased, from 2.88% in 1992 to 29.35% in 2022, as most of the barren land present in 1992 became built-up area in 2022. The LST values were calculated from the thermal bands for the years 1992, 2010, and 2022 and ranged from 16.09°C to 34.27°C, 17.04°C to 36.74°C, and 11.03°C to 28.44°C, respectively. In addition, the Normalized Difference Vegetation Index (NDVI) values were calculated using the near-infrared band and the red band, with values ranging from -0.40 to 0.70 over the study period. A linear regression analysis indicated a shift in the correlation between NDVI and LST from positive to negative. This study highlights the significant transformation that occurred in Hai Duong Province due to rapid population density increases, urban growth and infrastructure development, leading to a decline in greenery. These LULC changes can cause severe environmental damage. These research findings will assist policymakers in formulating management strategies and sustainable land-use plans to minimize potential harm and promote sustainable development in the area.
{"title":"Monitoring Land Surface Temperature Relationship to Land Use and Land Cover in Hai Duong Province, Vietnam","authors":"B. Thien, Asya E. Ovsepyan, V. T. Phuong","doi":"10.32526/ennrj/22/20230194","DOIUrl":"https://doi.org/10.32526/ennrj/22/20230194","url":null,"abstract":"This study utilised remote sensing data and ArcGIS 10.8 software to evaluate changes in land use and land cover (LULC) and their effects on land surface temperature (LST) in Hai Duong Province, Vietnam, from 1992 to 2022. Landsat satellite data were pre-processed and classified using supervised methods for the years 1992, 2010, and 2022. In 1992, vegetation cover accounted for 57.89% of land cover, increasing to 84.49% in 2010, but then decreasing again to 66.67% in 2022. In contrast, the built-up area consistently increased, from 2.88% in 1992 to 29.35% in 2022, as most of the barren land present in 1992 became built-up area in 2022. The LST values were calculated from the thermal bands for the years 1992, 2010, and 2022 and ranged from 16.09°C to 34.27°C, 17.04°C to 36.74°C, and 11.03°C to 28.44°C, respectively. In addition, the Normalized Difference Vegetation Index (NDVI) values were calculated using the near-infrared band and the red band, with values ranging from -0.40 to 0.70 over the study period. A linear regression analysis indicated a shift in the correlation between NDVI and LST from positive to negative. This study highlights the significant transformation that occurred in Hai Duong Province due to rapid population density increases, urban growth and infrastructure development, leading to a decline in greenery. These LULC changes can cause severe environmental damage. These research findings will assist policymakers in formulating management strategies and sustainable land-use plans to minimize potential harm and promote sustainable development in the area.","PeriodicalId":11784,"journal":{"name":"Environment and Natural Resources Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139840129","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 : 2024-02-13DOI: 10.32526/ennrj/22/20230194
B. Thien, Asya E. Ovsepyan, V. T. Phuong
This study utilised remote sensing data and ArcGIS 10.8 software to evaluate changes in land use and land cover (LULC) and their effects on land surface temperature (LST) in Hai Duong Province, Vietnam, from 1992 to 2022. Landsat satellite data were pre-processed and classified using supervised methods for the years 1992, 2010, and 2022. In 1992, vegetation cover accounted for 57.89% of land cover, increasing to 84.49% in 2010, but then decreasing again to 66.67% in 2022. In contrast, the built-up area consistently increased, from 2.88% in 1992 to 29.35% in 2022, as most of the barren land present in 1992 became built-up area in 2022. The LST values were calculated from the thermal bands for the years 1992, 2010, and 2022 and ranged from 16.09°C to 34.27°C, 17.04°C to 36.74°C, and 11.03°C to 28.44°C, respectively. In addition, the Normalized Difference Vegetation Index (NDVI) values were calculated using the near-infrared band and the red band, with values ranging from -0.40 to 0.70 over the study period. A linear regression analysis indicated a shift in the correlation between NDVI and LST from positive to negative. This study highlights the significant transformation that occurred in Hai Duong Province due to rapid population density increases, urban growth and infrastructure development, leading to a decline in greenery. These LULC changes can cause severe environmental damage. These research findings will assist policymakers in formulating management strategies and sustainable land-use plans to minimize potential harm and promote sustainable development in the area.
{"title":"Monitoring Land Surface Temperature Relationship to Land Use and Land Cover in Hai Duong Province, Vietnam","authors":"B. Thien, Asya E. Ovsepyan, V. T. Phuong","doi":"10.32526/ennrj/22/20230194","DOIUrl":"https://doi.org/10.32526/ennrj/22/20230194","url":null,"abstract":"This study utilised remote sensing data and ArcGIS 10.8 software to evaluate changes in land use and land cover (LULC) and their effects on land surface temperature (LST) in Hai Duong Province, Vietnam, from 1992 to 2022. Landsat satellite data were pre-processed and classified using supervised methods for the years 1992, 2010, and 2022. In 1992, vegetation cover accounted for 57.89% of land cover, increasing to 84.49% in 2010, but then decreasing again to 66.67% in 2022. In contrast, the built-up area consistently increased, from 2.88% in 1992 to 29.35% in 2022, as most of the barren land present in 1992 became built-up area in 2022. The LST values were calculated from the thermal bands for the years 1992, 2010, and 2022 and ranged from 16.09°C to 34.27°C, 17.04°C to 36.74°C, and 11.03°C to 28.44°C, respectively. In addition, the Normalized Difference Vegetation Index (NDVI) values were calculated using the near-infrared band and the red band, with values ranging from -0.40 to 0.70 over the study period. A linear regression analysis indicated a shift in the correlation between NDVI and LST from positive to negative. This study highlights the significant transformation that occurred in Hai Duong Province due to rapid population density increases, urban growth and infrastructure development, leading to a decline in greenery. These LULC changes can cause severe environmental damage. These research findings will assist policymakers in formulating management strategies and sustainable land-use plans to minimize potential harm and promote sustainable development in the area.","PeriodicalId":11784,"journal":{"name":"Environment and Natural Resources Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139780124","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 : 2024-02-12DOI: 10.32526/ennrj/22/20230241
K. Intarat, Patimakorn Yoomee, Areewan Hussadin, Wanjai Lamprom
In mountainous terrain, landslides are common, particularly in intermontane basin locations. Such regions can adversely affect both human beings and the environment. In the assessment of landslide susceptibility, machine learning (ML) algorithms are increasingly popular due to their compatibility with geospatial data and tools. Herein, this study evaluated the performance of four ML algorithms: namely, random forest (RF), gradient boost (GB), extreme gradient boost (XGB), and stacking ensemble (STK). These algorithms were implemented to create a practical model of landslide susceptibility. The site under investigation is in the province of Chiang Mai, an intermontane basin area in northern Thailand where populations are settled. To address issues of multicollinearity, the variance inflation factor (VIF) was used. Eight out of fourteen factors were selected for examination; hyperparameters of each model were tested to acquire the best combination. Results indicated that the STK model outperforms all other models, providing evaluation metrics (precision, recall, F1-score, and overall accuracy) of 82.92%, 81.18%, 82.04%, and 81.75%, respectively. The area under the receiver operating characteristic (ROC) curve also reveals the high efficiency of the model, achieving 0.8928. However, further analysis of the appropriate model or base learner is necessary for achieving even higher predictive results.
{"title":"Assessment of Landslide Susceptibility in the Intermontane Basin Area of Northern Thailand","authors":"K. Intarat, Patimakorn Yoomee, Areewan Hussadin, Wanjai Lamprom","doi":"10.32526/ennrj/22/20230241","DOIUrl":"https://doi.org/10.32526/ennrj/22/20230241","url":null,"abstract":"In mountainous terrain, landslides are common, particularly in intermontane basin locations. Such regions can adversely affect both human beings and the environment. In the assessment of landslide susceptibility, machine learning (ML) algorithms are increasingly popular due to their compatibility with geospatial data and tools. Herein, this study evaluated the performance of four ML algorithms: namely, random forest (RF), gradient boost (GB), extreme gradient boost (XGB), and stacking ensemble (STK). These algorithms were implemented to create a practical model of landslide susceptibility. The site under investigation is in the province of Chiang Mai, an intermontane basin area in northern Thailand where populations are settled. To address issues of multicollinearity, the variance inflation factor (VIF) was used. Eight out of fourteen factors were selected for examination; hyperparameters of each model were tested to acquire the best combination. Results indicated that the STK model outperforms all other models, providing evaluation metrics (precision, recall, F1-score, and overall accuracy) of 82.92%, 81.18%, 82.04%, and 81.75%, respectively. The area under the receiver operating characteristic (ROC) curve also reveals the high efficiency of the model, achieving 0.8928. However, further analysis of the appropriate model or base learner is necessary for achieving even higher predictive results.","PeriodicalId":11784,"journal":{"name":"Environment and Natural Resources Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139842531","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 : 2024-02-12DOI: 10.32526/ennrj/22/20230241
K. Intarat, Patimakorn Yoomee, Areewan Hussadin, Wanjai Lamprom
In mountainous terrain, landslides are common, particularly in intermontane basin locations. Such regions can adversely affect both human beings and the environment. In the assessment of landslide susceptibility, machine learning (ML) algorithms are increasingly popular due to their compatibility with geospatial data and tools. Herein, this study evaluated the performance of four ML algorithms: namely, random forest (RF), gradient boost (GB), extreme gradient boost (XGB), and stacking ensemble (STK). These algorithms were implemented to create a practical model of landslide susceptibility. The site under investigation is in the province of Chiang Mai, an intermontane basin area in northern Thailand where populations are settled. To address issues of multicollinearity, the variance inflation factor (VIF) was used. Eight out of fourteen factors were selected for examination; hyperparameters of each model were tested to acquire the best combination. Results indicated that the STK model outperforms all other models, providing evaluation metrics (precision, recall, F1-score, and overall accuracy) of 82.92%, 81.18%, 82.04%, and 81.75%, respectively. The area under the receiver operating characteristic (ROC) curve also reveals the high efficiency of the model, achieving 0.8928. However, further analysis of the appropriate model or base learner is necessary for achieving even higher predictive results.
{"title":"Assessment of Landslide Susceptibility in the Intermontane Basin Area of Northern Thailand","authors":"K. Intarat, Patimakorn Yoomee, Areewan Hussadin, Wanjai Lamprom","doi":"10.32526/ennrj/22/20230241","DOIUrl":"https://doi.org/10.32526/ennrj/22/20230241","url":null,"abstract":"In mountainous terrain, landslides are common, particularly in intermontane basin locations. Such regions can adversely affect both human beings and the environment. In the assessment of landslide susceptibility, machine learning (ML) algorithms are increasingly popular due to their compatibility with geospatial data and tools. Herein, this study evaluated the performance of four ML algorithms: namely, random forest (RF), gradient boost (GB), extreme gradient boost (XGB), and stacking ensemble (STK). These algorithms were implemented to create a practical model of landslide susceptibility. The site under investigation is in the province of Chiang Mai, an intermontane basin area in northern Thailand where populations are settled. To address issues of multicollinearity, the variance inflation factor (VIF) was used. Eight out of fourteen factors were selected for examination; hyperparameters of each model were tested to acquire the best combination. Results indicated that the STK model outperforms all other models, providing evaluation metrics (precision, recall, F1-score, and overall accuracy) of 82.92%, 81.18%, 82.04%, and 81.75%, respectively. The area under the receiver operating characteristic (ROC) curve also reveals the high efficiency of the model, achieving 0.8928. However, further analysis of the appropriate model or base learner is necessary for achieving even higher predictive results.","PeriodicalId":11784,"journal":{"name":"Environment and Natural Resources Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139782558","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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