Pub Date : 2021-10-01DOI: 10.22034/GJESM.2022.03.01
S. Rahman, M. Ramli, F. Arnia, R. Muharar, M. Ikhwan, S. Munzir
BACKGROUND AND OBJECTIVES: The increase in the number of vehicles has several negative impacts, including traffic congestion, air pollution, noise levels, and the availability of parking spaces. Drivers looking for parking spaces can cause traffic jams and air pollution. The solution offered at this time is the development of a smart parking system to overcome these problems. The smart parking system offers a parking availability information feature in a parking area to break up congestion in the parking space. Deep learning is a successful method to solve parking space classification problems. It is known that this method requires a large computational process. Th aims of this study are to modified the architecture of Convolutional Neural Networks, part of deep learning to classify parking spaces. Modification of the Convolutional Neural Networks architecture is assumed to increase the work efficiency of the smart parking system in processing parking availability information.METHODS: Research is focusing on developing parking space classification techniques using camera sensors due to the rapid advancement of technology and algorithms in computer vision. The input image has 3x3 dimensions. The first convolution layer accepts the input image and converts it into 56x56 dimensions. The second convolution layer is composed in the same way as the first layer with dimensions of 25x25. The third convolution layer employs a 3 x 3 filter matrix with padding of up to 15 and converts it into 10x10 dimensions. The fourth layer is composed in the same way as the third layer, but with the addition of maximum pooling. The software used in the test is Python with a Python framework.FINDINGS: The proposed architecture is the Efficient Parking Network or EfficientParkingNet. It can be shown that this architecture is more efficient in classifying parking spaces compared to some other architectures, such as the mini–Alex Network (mAlexnet) and the Grassmannian Deep Stacking Network with Illumination Correction (GDSN-IC). EfficientParkingNet has not been able to pass the accuracy of Yolo Mobile Network (Yolo+MobileNet). Furthermore, Yolo+MobileNet has so many parameters that it cannot be used on low computing devices. Selection of EfficientParkingNet as a lightweight architecture tailored to the needs of use. EfficientParkingNet's lightweight computing architecture can increase the speed of information on parking availability to users.CONCLUSION: EfficientParkingNet is more efficient in determining the availability of parking spaces compared to mAlexnet, but still cannot match Yolo+MobileNet. Based on the number of parameters, EfficientParkingNet uses half of the number of parameters of mAlexnet and is much smaller than Yolo+MobileNet. EfficientParkingNet has an accuracy rate of 98.44% for the National Research Council parking dataset and higher than other architectures. EfficientParkingNet is suitable for use in parking systems with low computing devices such as th
{"title":"Enhancement of convolutional neural network for urban environment parking space classification","authors":"S. Rahman, M. Ramli, F. Arnia, R. Muharar, M. Ikhwan, S. Munzir","doi":"10.22034/GJESM.2022.03.01","DOIUrl":"https://doi.org/10.22034/GJESM.2022.03.01","url":null,"abstract":"BACKGROUND AND OBJECTIVES: The increase in the number of vehicles has several negative impacts, including traffic congestion, air pollution, noise levels, and the availability of parking spaces. Drivers looking for parking spaces can cause traffic jams and air pollution. The solution offered at this time is the development of a smart parking system to overcome these problems. The smart parking system offers a parking availability information feature in a parking area to break up congestion in the parking space. Deep learning is a successful method to solve parking space classification problems. It is known that this method requires a large computational process. Th aims of this study are to modified the architecture of Convolutional Neural Networks, part of deep learning to classify parking spaces. Modification of the Convolutional Neural Networks architecture is assumed to increase the work efficiency of the smart parking system in processing parking availability information.METHODS: Research is focusing on developing parking space classification techniques using camera sensors due to the rapid advancement of technology and algorithms in computer vision. The input image has 3x3 dimensions. The first convolution layer accepts the input image and converts it into 56x56 dimensions. The second convolution layer is composed in the same way as the first layer with dimensions of 25x25. The third convolution layer employs a 3 x 3 filter matrix with padding of up to 15 and converts it into 10x10 dimensions. The fourth layer is composed in the same way as the third layer, but with the addition of maximum pooling. The software used in the test is Python with a Python framework.FINDINGS: The proposed architecture is the Efficient Parking Network or EfficientParkingNet. It can be shown that this architecture is more efficient in classifying parking spaces compared to some other architectures, such as the mini–Alex Network (mAlexnet) and the Grassmannian Deep Stacking Network with Illumination Correction (GDSN-IC). EfficientParkingNet has not been able to pass the accuracy of Yolo Mobile Network (Yolo+MobileNet). Furthermore, Yolo+MobileNet has so many parameters that it cannot be used on low computing devices. Selection of EfficientParkingNet as a lightweight architecture tailored to the needs of use. EfficientParkingNet's lightweight computing architecture can increase the speed of information on parking availability to users.CONCLUSION: EfficientParkingNet is more efficient in determining the availability of parking spaces compared to mAlexnet, but still cannot match Yolo+MobileNet. Based on the number of parameters, EfficientParkingNet uses half of the number of parameters of mAlexnet and is much smaller than Yolo+MobileNet. EfficientParkingNet has an accuracy rate of 98.44% for the National Research Council parking dataset and higher than other architectures. EfficientParkingNet is suitable for use in parking systems with low computing devices such as th","PeriodicalId":46495,"journal":{"name":"GLOBAL JOURNAL OF ENVIRONMENTAL SCIENCE AND MANAGEMENT-GJESM","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43363844","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 : 2021-09-29DOI: 10.22034/GJESM.2022.02.05
A. Brotosusilo, D. Utari, H. A. Negoro, A. Firdaus, R. A. Velentina
BACKGROUND AND OBJECTIVES: Indonesia's economic growth is estimated to be driven by high levels of consumption which lead to large amounts of waste. Education is required to raise environmental awareness among the population as it is one of the ways to overcome the waste issue, especially in urban areas, which are the engines of economic growth. This study aims to determine whether the higher levels of education have a greater impact on citizens regarding environmental concerns such as littering.METHODS: The study took logistics regression on the primary data survey from 7 cities (Jakarta, Jambi, Muaro Jambi, Ambon, Padang, Surabaya, and Tasikmalaya) in Indonesia during 2019-2021. The survey includes 563 observations on the household level, involving a total of 2,349 respondents. The logistic regression predicts the likelihood of urban citizens to litter, given their socio-economic backgrounds and existing littering behavior and environmental awareness.FINDINGS: This study found that education did not affect decreasing the value of littering behavior as expected since it is estimated that an increase of 1 year in school will increase the probability of littering by 0.0189. Formal education is not enough to decrease the probability of littering behavior on the individual level. In contrast, informal education taught on keeping a clean environment matters is better than conventional formal education. Besides that, having self-initiative on environmental caring and good habits from childhood will decrease the probability of littering on an individual level. An individual has a self-initiative, the probability of littering will be 0.1732 times lower than those who do not have self-initiative. This study also found that per capita income and per capita expenditure in big cities in Indonesia ranged between USD 156,903 and USD 116,857. These economic factors affect the behavior of citizens not to litter. The per capita expenditure increasing by USD 1 per person per day will decrease the probability of littering by -0.0468. However, these factors are not enough to minimize the littering behavior since the disposal place availability becomes another keys factor in decreasing littering behavior on urban citizens.CONCLUSION: The government should also focus on building citizens' behavior regarding waste management awareness especially building good habits since childhood and individual initiative, simultaneously implementing the programs to reduce waste production.
{"title":"Community empowerment of waste management in the urban environment: More attention on waste issues through formal and informal educations","authors":"A. Brotosusilo, D. Utari, H. A. Negoro, A. Firdaus, R. A. Velentina","doi":"10.22034/GJESM.2022.02.05","DOIUrl":"https://doi.org/10.22034/GJESM.2022.02.05","url":null,"abstract":"BACKGROUND AND OBJECTIVES: Indonesia's economic growth is estimated to be driven by high levels of consumption which lead to large amounts of waste. Education is required to raise environmental awareness among the population as it is one of the ways to overcome the waste issue, especially in urban areas, which are the engines of economic growth. This study aims to determine whether the higher levels of education have a greater impact on citizens regarding environmental concerns such as littering.METHODS: The study took logistics regression on the primary data survey from 7 cities (Jakarta, Jambi, Muaro Jambi, Ambon, Padang, Surabaya, and Tasikmalaya) in Indonesia during 2019-2021. The survey includes 563 observations on the household level, involving a total of 2,349 respondents. The logistic regression predicts the likelihood of urban citizens to litter, given their socio-economic backgrounds and existing littering behavior and environmental awareness.FINDINGS: This study found that education did not affect decreasing the value of littering behavior as expected since it is estimated that an increase of 1 year in school will increase the probability of littering by 0.0189. Formal education is not enough to decrease the probability of littering behavior on the individual level. In contrast, informal education taught on keeping a clean environment matters is better than conventional formal education. Besides that, having self-initiative on environmental caring and good habits from childhood will decrease the probability of littering on an individual level. An individual has a self-initiative, the probability of littering will be 0.1732 times lower than those who do not have self-initiative. This study also found that per capita income and per capita expenditure in big cities in Indonesia ranged between USD 156,903 and USD 116,857. These economic factors affect the behavior of citizens not to litter. The per capita expenditure increasing by USD 1 per person per day will decrease the probability of littering by -0.0468. However, these factors are not enough to minimize the littering behavior since the disposal place availability becomes another keys factor in decreasing littering behavior on urban citizens.CONCLUSION: The government should also focus on building citizens' behavior regarding waste management awareness especially building good habits since childhood and individual initiative, simultaneously implementing the programs to reduce waste production.","PeriodicalId":46495,"journal":{"name":"GLOBAL JOURNAL OF ENVIRONMENTAL SCIENCE AND MANAGEMENT-GJESM","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2021-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42857940","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 : 2021-09-04DOI: 10.22034/GJESM.2022.02.09
N. Robinah, A. Safiki, O. Thomas, B. Annette
BACKGROUND AND OBJECTIVES: The effect of infrastructure equipment is taking a toll on the health and economic well-being of residents all around the world. This is mainly because it contributes to ambient air pollution, noise, and vibration in the surroundings. The study aimed at analyzing the effects of the road infrastructure equipment on the surroundings in Uganda. The emissions of carbon dioxide, carbon monoxide, nitrogen dioxide, hydrocarbons, and particulate matter were analyzed.METHODS: Six road infrastructure equipment were sampled consisting of an excavator, roller, grader, concrete mixer, tamper, and wheel loader, obtained from a case study project in Kampala city, Uganda. The diesel exhaust air emissions were computed and analyzed using the emissions rate equation model for non-road equipment, developed by Environmental Protection Agency. This was based on the horsepower and power rating of the equipment. Noise and vibrations levels were obtained using a sound level meter, seismometers, and accelerators, while following the National Environment Regulations.FINDINGS: The greenhouse gas of carbon dioxide was the most predominant accounting for 84.1 percent of the total emissions. The grader was the highest emitter of this greenhouse gas, at 1,531.5 g/h, representing 37.1%. The lowest air pollutant emission was nitrogen dioxide at 1.43 g/h for the concrete mixer, representing 1.4%. Overall, the equipment emitted more greenhouse gases than air criteria pollutants at 88.8% and 11.2% respectively. The highest criteria air pollutant was particulate matter at 100.5 g/h, emitted by the grader. Most of the emissions met the standards stipulated by Environmental Protection Agency, for reducing emissions back to the environment, except particulate matter. However, the concentrations of some pollutants like carbon monoxide and nitrogen dioxide did not satisfy the limits required for ambient air quality that is safe for workers. All the equipment had noise levels way above the recommended 70.00 decibel, except for the wheel loader. Only the excavator produced vibrations higher than permissible vibration limit by 4%.CONCLUSION: The criteria air pollutants of carbon monoxide, nitrogen dioxide, and particulate matter emitted by the equipment were all not safe to the workers. They exceeded the permissible limits of 50 ppm, 5 ppm, and 0.02 g/kW/h respectively. This partly shows why ambient air pollution had been reported in urban centers in Uganda. The study shows the need for strengthening the regulations and monitoring of the construction equipment being used, in order to protect the surroundings.
{"title":"Impact of road infrastructure equipment on the environment and surroundings","authors":"N. Robinah, A. Safiki, O. Thomas, B. Annette","doi":"10.22034/GJESM.2022.02.09","DOIUrl":"https://doi.org/10.22034/GJESM.2022.02.09","url":null,"abstract":"BACKGROUND AND OBJECTIVES: The effect of infrastructure equipment is taking a toll on the health and economic well-being of residents all around the world. This is mainly because it contributes to ambient air pollution, noise, and vibration in the surroundings. The study aimed at analyzing the effects of the road infrastructure equipment on the surroundings in Uganda. The emissions of carbon dioxide, carbon monoxide, nitrogen dioxide, hydrocarbons, and particulate matter were analyzed.METHODS: Six road infrastructure equipment were sampled consisting of an excavator, roller, grader, concrete mixer, tamper, and wheel loader, obtained from a case study project in Kampala city, Uganda. The diesel exhaust air emissions were computed and analyzed using the emissions rate equation model for non-road equipment, developed by Environmental Protection Agency. This was based on the horsepower and power rating of the equipment. Noise and vibrations levels were obtained using a sound level meter, seismometers, and accelerators, while following the National Environment Regulations.FINDINGS: The greenhouse gas of carbon dioxide was the most predominant accounting for 84.1 percent of the total emissions. The grader was the highest emitter of this greenhouse gas, at 1,531.5 g/h, representing 37.1%. The lowest air pollutant emission was nitrogen dioxide at 1.43 g/h for the concrete mixer, representing 1.4%. Overall, the equipment emitted more greenhouse gases than air criteria pollutants at 88.8% and 11.2% respectively. The highest criteria air pollutant was particulate matter at 100.5 g/h, emitted by the grader. Most of the emissions met the standards stipulated by Environmental Protection Agency, for reducing emissions back to the environment, except particulate matter. However, the concentrations of some pollutants like carbon monoxide and nitrogen dioxide did not satisfy the limits required for ambient air quality that is safe for workers. All the equipment had noise levels way above the recommended 70.00 decibel, except for the wheel loader. Only the excavator produced vibrations higher than permissible vibration limit by 4%.CONCLUSION: The criteria air pollutants of carbon monoxide, nitrogen dioxide, and particulate matter emitted by the equipment were all not safe to the workers. They exceeded the permissible limits of 50 ppm, 5 ppm, and 0.02 g/kW/h respectively. This partly shows why ambient air pollution had been reported in urban centers in Uganda. The study shows the need for strengthening the regulations and monitoring of the construction equipment being used, in order to protect the surroundings.","PeriodicalId":46495,"journal":{"name":"GLOBAL JOURNAL OF ENVIRONMENTAL SCIENCE AND MANAGEMENT-GJESM","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2021-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47786015","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 : 2021-09-02DOI: 10.22034/GJESM.2022.02.06
A. Charkhestani, D. Kebria
BACKGROUND AND OBJECTIVES: Safeguarding water resources became a major concern in many parts of the world as it aims to provide safe and healthy water for humans. Water quality monitoring is a popular tool in ensuring water quality is safe and within the allowable limits and standards for the health of the community. To provide interventions and strategies for the rehabilitation, a water quality monitoring plan was conducted to describe the water quality and the classification of the river.METHODS: This study conducted an environmental analysis to determine existing conditions and processes in the surrounding environment such as the land use, drainage pattern, reconnaissance survey of the river, and a key interview to describe the barangay profile and the community's water use and practices. The water quality monitoring covers the evaluation of ten water quality parameters: temperature, pH, dissolved oxygen, total dissolved solids, total suspended solids, phosphate, nitrate, oil and grease, chloride, and E. coli.FINDINGS: Results of the study presents the water quality against the ten water quality criteria. Phosphate measured on four stations ranges between 2.40-4.50 mg/L exceeding the allowable 0.50mg/L; the oil and grease exceeds the standards 2 mg/L with measured values of 2.40-4.60 mg/L in stations 2, 3, and 4; while measured chloride in all stations prove that the water is salty with values exceeding the freshwater requirement of 250mg/L; and the measured TSS in stations 2, 3 and 4 ranges from 32.30 to 49.3 mg/L exceeds the standards of 30mg/L. E. coli was also detected in water samples collected in all sampling stations. The computed water quality index of 39.02 described water as poor, always impaired, and threatened by the surrounding environment. CONCLUSION: The measured concentrations for phosphate, oil/ grease, chloride, and TSS exceeds the water quality requirement suggesting that the water is contaminated. The E. coli detected in all water samples, further recommends prohibition of recreational activities to avoid accidental intakes and skin contact on the polluted water. The existing activities in the surrounding residential, commercial and agricultural areas contributed to water contamination as aggravated by the unreliable drainage system, absence of proper sanitation facilities, and collection and disposal behavior of the community. From this, a scientific basis can be drawn on how the river can be rehabilitated and protected and serve as guide for policymakers and water managers on implementing strategies to achieve sustainable water resources.
{"title":"Laboratory analysis to determine the accurate characteristics of urban food waste","authors":"A. Charkhestani, D. Kebria","doi":"10.22034/GJESM.2022.02.06","DOIUrl":"https://doi.org/10.22034/GJESM.2022.02.06","url":null,"abstract":"BACKGROUND AND OBJECTIVES: Safeguarding water resources became a major concern in many parts of the world as it aims to provide safe and healthy water for humans. Water quality monitoring is a popular tool in ensuring water quality is safe and within the allowable limits and standards for the health of the community. To provide interventions and strategies for the rehabilitation, a water quality monitoring plan was conducted to describe the water quality and the classification of the river.METHODS: This study conducted an environmental analysis to determine existing conditions and processes in the surrounding environment such as the land use, drainage pattern, reconnaissance survey of the river, and a key interview to describe the barangay profile and the community's water use and practices. The water quality monitoring covers the evaluation of ten water quality parameters: temperature, pH, dissolved oxygen, total dissolved solids, total suspended solids, phosphate, nitrate, oil and grease, chloride, and E. coli.FINDINGS: Results of the study presents the water quality against the ten water quality criteria. Phosphate measured on four stations ranges between 2.40-4.50 mg/L exceeding the allowable 0.50mg/L; the oil and grease exceeds the standards 2 mg/L with measured values of 2.40-4.60 mg/L in stations 2, 3, and 4; while measured chloride in all stations prove that the water is salty with values exceeding the freshwater requirement of 250mg/L; and the measured TSS in stations 2, 3 and 4 ranges from 32.30 to 49.3 mg/L exceeds the standards of 30mg/L. E. coli was also detected in water samples collected in all sampling stations. The computed water quality index of 39.02 described water as poor, always impaired, and threatened by the surrounding environment. CONCLUSION: The measured concentrations for phosphate, oil/ grease, chloride, and TSS exceeds the water quality requirement suggesting that the water is contaminated. The E. coli detected in all water samples, further recommends prohibition of recreational activities to avoid accidental intakes and skin contact on the polluted water. The existing activities in the surrounding residential, commercial and agricultural areas contributed to water contamination as aggravated by the unreliable drainage system, absence of proper sanitation facilities, and collection and disposal behavior of the community. From this, a scientific basis can be drawn on how the river can be rehabilitated and protected and serve as guide for policymakers and water managers on implementing strategies to achieve sustainable water resources.","PeriodicalId":46495,"journal":{"name":"GLOBAL JOURNAL OF ENVIRONMENTAL SCIENCE AND MANAGEMENT-GJESM","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2021-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46339974","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 : 2021-08-20DOI: 10.22034/GJESM.2022.01.05
O. Cahyonugroho, S. Hariyanto, G. Supriyanto
BACKGROUND AND OBJECTIVES: Dissolved organic matter has a fundamental role in supporting phytoplankton abundance and growth in aquatic environments. However, these organisms produce dissolved organic matter with varied quantities or characteristics depending on the nutrient availability and the species composition. Therefore, this study aims to assess the characteristic of dissolved organic matter on surface water and its correlation with phytoplankton abundance for monitoring water quality.METHODS: The sample was obtained at four Kali Surabaya River stations for further dissolved organic matter analysis and phytoplankton species analysis. The analysis was presented through bulk parameters of total organic, ultraviolet at 254 nm wavelength (UV254), specific ultraviolet absorbance value, and fluorescence spectroscopy using excitation-emission matrices with fluorescence regional integration analysis.FINDINGS: The results showed the bulk parameters of dissolved organic matter at all stations were significantly different, as Station 1 and 2 were higher, while 3 and 4 had a lower concentration. Furthermore, the fluorescence spectroscopy identified four components of dissolved organic matter at all stations, namely aromatic proteins-like (AP-like), humic acid-like (HA-like), soluble microbial by-products-like (SMPs-like), and fulvic acid-like (FA-like). Also, stations 1 and 2 were grouped in the high percentage FRI of humic substance (FA-like and HA-like), while 3 and 4 were classified in the high percentage FRI of non-humic substances (AP-like and SMPs-like).CONCLUSION: The main phytoplankton species, namely Plectonema sp., Pinularia sp., Nitzchia sp., Navicula sp., had the highest abundance at Stations 1, 3, and 4, respectively. A strong correlation between dissolved organic matter analysis and phytoplankton abundance led to the usage of these methods for monitoring surface water quality.
{"title":"Dissolved organic matter and its correlation with phytoplankton abundance for monitoring surface water quality","authors":"O. Cahyonugroho, S. Hariyanto, G. Supriyanto","doi":"10.22034/GJESM.2022.01.05","DOIUrl":"https://doi.org/10.22034/GJESM.2022.01.05","url":null,"abstract":"BACKGROUND AND OBJECTIVES: Dissolved organic matter has a fundamental role in supporting phytoplankton abundance and growth in aquatic environments. However, these organisms produce dissolved organic matter with varied quantities or characteristics depending on the nutrient availability and the species composition. Therefore, this study aims to assess the characteristic of dissolved organic matter on surface water and its correlation with phytoplankton abundance for monitoring water quality.METHODS: The sample was obtained at four Kali Surabaya River stations for further dissolved organic matter analysis and phytoplankton species analysis. The analysis was presented through bulk parameters of total organic, ultraviolet at 254 nm wavelength (UV254), specific ultraviolet absorbance value, and fluorescence spectroscopy using excitation-emission matrices with fluorescence regional integration analysis.FINDINGS: The results showed the bulk parameters of dissolved organic matter at all stations were significantly different, as Station 1 and 2 were higher, while 3 and 4 had a lower concentration. Furthermore, the fluorescence spectroscopy identified four components of dissolved organic matter at all stations, namely aromatic proteins-like (AP-like), humic acid-like (HA-like), soluble microbial by-products-like (SMPs-like), and fulvic acid-like (FA-like). Also, stations 1 and 2 were grouped in the high percentage FRI of humic substance (FA-like and HA-like), while 3 and 4 were classified in the high percentage FRI of non-humic substances (AP-like and SMPs-like).CONCLUSION: The main phytoplankton species, namely Plectonema sp., Pinularia sp., Nitzchia sp., Navicula sp., had the highest abundance at Stations 1, 3, and 4, respectively. A strong correlation between dissolved organic matter analysis and phytoplankton abundance led to the usage of these methods for monitoring surface water quality.","PeriodicalId":46495,"journal":{"name":"GLOBAL JOURNAL OF ENVIRONMENTAL SCIENCE AND MANAGEMENT-GJESM","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2021-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45286003","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 : 2021-07-07DOI: 10.22034/GJESM.2022.01.08
S. Nimesha, C. Hewawasam, D. J. Jayasanka, Y. Murakami, N. Araki, N. Maharjan
BACKGROUND AND OBJECTIVES: Electronic equipment production is one of the major industrial sectors in Indonesia, as it also contributes to Indonesia’s export commodities, which increase because of rapid technological developments. Cell phones, which have considerable potential to become electronic waste, recorded the enormous escalation in electronic production. This research aimed to increase community involvement and the collection of used cell phones from households in e-waste management in Indonesia. A survey was conducted to explore a household’s environmental awareness and willingness to recycle based on sociodemographics, environmental hazard awareness, and used cell phone usage in Jabodetabek, Indonesia.METHODS: In this research, a peer questionnaire was used and organized into five sections: The first section contained the sociodemographic details of the respondents. The second section comprised multiple concerns that relate to recycling and environmental awareness. The third section contained the family cell phone information. The fourth section determined the cell phone consumer behavior. The fifth section consisted of willingness to recycle. Statistical correlations between variables were assessed, and the chi-square independence test was used to evaluate the statistical correlations. FINDINGS: Mostly the households will replace their used cell phone if there is damage (66.84%) and keep the used cell phone at home (59.5%), thus becoming an obstacle in applying the appropriate recycling system and a circular economy. The average cell phone ownership in Jabodetabek is 1.28 units, and the average cell phone life span of people in Jabodetabek is 2.6 years. The Environmental Hazard Awareness variable has significant differences with occupation and income level (p-value = 0.028 and 0.046), Used Cellphone Usage variable has significant differences with the income level variable (p-value = 0.024). The others, a statistically significant difference between sociodemographic variable and Willingness to Recycle was observed; p-value = 0.003 for age and p-value = 0.034 for occupation. CONCLUSION: This paper showed that Environmental Hazard Awareness and Willingness to Recycle have an important role in increasing the collection of used cell phones from households. . This study assessed community-based factors located in urban areas. The factors could encourage their participation in collection activities, obtain information on the preferred collection channels of residents, and provide a perspective for managing cell phones through an analysis of the improvements and influences of Indonesia’s current e-waste recycling program. Therefore, to develop a new strategy, the findings of this study can provide insights into the e-waste problem and citizen’s awareness of e-waste management.
{"title":"Effectiveness of natural coagulants in water and wastewater treatment","authors":"S. Nimesha, C. Hewawasam, D. J. Jayasanka, Y. Murakami, N. Araki, N. Maharjan","doi":"10.22034/GJESM.2022.01.08","DOIUrl":"https://doi.org/10.22034/GJESM.2022.01.08","url":null,"abstract":"BACKGROUND AND OBJECTIVES: Electronic equipment production is one of the major industrial sectors in Indonesia, as it also contributes to Indonesia’s export commodities, which increase because of rapid technological developments. Cell phones, which have considerable potential to become electronic waste, recorded the enormous escalation in electronic production. This research aimed to increase community involvement and the collection of used cell phones from households in e-waste management in Indonesia. A survey was conducted to explore a household’s environmental awareness and willingness to recycle based on sociodemographics, environmental hazard awareness, and used cell phone usage in Jabodetabek, Indonesia.METHODS: In this research, a peer questionnaire was used and organized into five sections: The first section contained the sociodemographic details of the respondents. The second section comprised multiple concerns that relate to recycling and environmental awareness. The third section contained the family cell phone information. The fourth section determined the cell phone consumer behavior. The fifth section consisted of willingness to recycle. Statistical correlations between variables were assessed, and the chi-square independence test was used to evaluate the statistical correlations. FINDINGS: Mostly the households will replace their used cell phone if there is damage (66.84%) and keep the used cell phone at home (59.5%), thus becoming an obstacle in applying the appropriate recycling system and a circular economy. The average cell phone ownership in Jabodetabek is 1.28 units, and the average cell phone life span of people in Jabodetabek is 2.6 years. The Environmental Hazard Awareness variable has significant differences with occupation and income level (p-value = 0.028 and 0.046), Used Cellphone Usage variable has significant differences with the income level variable (p-value = 0.024). The others, a statistically significant difference between sociodemographic variable and Willingness to Recycle was observed; p-value = 0.003 for age and p-value = 0.034 for occupation. CONCLUSION: This paper showed that Environmental Hazard Awareness and Willingness to Recycle have an important role in increasing the collection of used cell phones from households. . This study assessed community-based factors located in urban areas. The factors could encourage their participation in collection activities, obtain information on the preferred collection channels of residents, and provide a perspective for managing cell phones through an analysis of the improvements and influences of Indonesia’s current e-waste recycling program. Therefore, to develop a new strategy, the findings of this study can provide insights into the e-waste problem and citizen’s awareness of e-waste management.","PeriodicalId":46495,"journal":{"name":"GLOBAL JOURNAL OF ENVIRONMENTAL SCIENCE AND MANAGEMENT-GJESM","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2021-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46041293","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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