Global Journal of Environmental Science and Management
Volume:8 Issue: 3, Summer 2022

Soil or rock properties where buildings are situated play an important role in the ground shaking caused by an earthquake. The highly populated Banda Aceh city in the northernmost Sumatra is flanked by two active faults, the Seulimeum and the Aceh segment. Therefore, it is crucial to investigate the subsurface characteristics of the region to reduce the earthquake risk as there was no regional study has been conducted so far.

Characteristics of the soil or rock of the subsurface were derived from various seismic parameters. The seismic microtremors were recorded at 36 sites covering the highly populated city and the two active faults. The spatial autocorrelation method was used to obtain a dispersion curve based on the relationship between seismic frequencies and phase velocity from triangular geophones array to determine the shear wave velocity of the subsurface layer. The seismic amplification, dominant frequency and vulnerability value at each measurement point were measured using the horizontal-to-vertical spectral ratio method. The maps of velocity structure and HVSR parameters were generated from the interpolation of those seismic parameters.

Based on the variation of the four geophysical parameters: shear wave velocity, seismic amplification, dominant frequency, and seismic vulnerability, the study area can be clustered into five different groups: I) Banda Aceh, II) Jantho, III) Krueng Raya, IV) Lhoknga-Lhoong, and V) Seulawah, which classify the different types of rocks. The classification of soil properties from the combination of shear wave and horizontal-to-vertical spectral ratio data correlates with the geology of the study area.

The Banda Aceh city, flanked by the two active faults, is characterized by low shear wave velocity and high amplification because the city stands on the sedimentary basin; thus, it requires a detailed investigation prior to constructing infrastructures. The other clusters are located on the relatively less vulnerable areas, indicated by moderate shear wave velocity and moderate to low seismic vulnerability indexes. The joint analysis shows that the combination of physical properties, including the shear wave velocity, seismic amplification, and dominant frequency, can be used to investigate lithology and seismic vulnerability into a specific cluster. The research results are essential for hazard mitigation and can be used for disaster risk management by the local government. A detailed investigation with denser measurement points needs to be conducted to comprehensively describe the types of rocks in Banda Aceh and its surrounding.

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.

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.

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.

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 the Raspberry Pi because of the small number of parameters.

During the Coronavirus disease 2019 pandemic, an effective and efficient medical waste management plan is required to prevent disease transmission from the Coronavirus disease 2019 viral solid wastes. Screening stations are critical locations where solid Coronavirus disease 2019 wastes are created. Solid trash collection and management strategies for screening stations must be studied as they are the first point of origin for solid Coronavirus disease 2019 wastes. The goal of this study is to evaluate the level of healthcare workers' knowledge in the medical waste management field in Jordanian Coronavirus disease 2019 screening stations, with an emphasis on understanding and implementing Jordanian medical waste management protocols, by examining the awareness, perspective, and practice about the many aspects of Coronavirus disease 2019 wastes.

A study sample (n = 78) involving technicians, nurses, and physicians working at various screening stations in Jordan's public and private sectors was evaluated. From April 2021 to September 2021, a cross-sectional survey involving questionnaires was carried out. The survey included questions on medical waste management knowledge and awareness among healthcare personnel regarding the Coronavirus 2019 medical waste administrative and collection procedures. There are various limitations to this cross-sectional study that should be noted. This is a study conducted among health care employees when an overwhelming amount of coronavirus disease cases were being recorded locally and worldwide, affecting transportation ability and minimizing time spent with screening station personnel.

The outcomes of the first module of the questionnaire revealed a high degree of medical waste management knowledge and awareness among healthcare staff. On the other hand, nurses demonstrated the highest awareness and application of COVID-19 medical waste administrative procedures. Furthermore, the results of the third module revealed that the private sector fails to successfully execute national and international procedures, with the most significant negative responses among other categories.

It was concluded that Jordan's solid waste collection and disposal methods were effectively implemented, which might aid in the virus eradication. Even though most Jordanian screening stations have effectively implemented knowledge and awareness regarding solid Coronavirus disease 2019 wastes management practices, there is a need to undertake periodic training and continuous monitoring with a specific focus on the appropriate administrative and collecting processes for both technicians, and private sector stations.

Fine topographic information is a key input parameter for a detailed flood simulation and mapping. This study aimed to compare the accuracy statistics of the flood models developed using the digital elevation datasets with different resolutions from the light detection and ranging and interferometric synthetic aperture radar systems.

The study applied the Hydrologic Engineering Center-Hydrologic Modeling System and Hydrologic Engineering Center-River Analysis System models workable within the geographic information system to simulate and map flood hazards in Maapag Watershed. The models’ validity and accuracy were tested using the confusion error matrix, f-measurement, and the root means square error statistics.

Results show that using the light detection and ranging dataset, the model is accurate at 88%, 0.61, and 0.41; while using the interferometric synthetic aperture radar dataset, the model is accurate at 76%, 0.34, 0.53; for the error matrix, f-measurement, and root mean square error; respectively.

The model developed using the light detection and ranging dataset showed higher accuracy than the model developed using the interferometric synthetic aperture radar. Nevertheless, the latter can be used for flood simulation and mapping as an alternative to the former considering the cost of model implementation and the smaller degree of accuracy residual error. Hence, flood modelers particularly from local authorities prefer to use coarser datasets to optimize the budget for flood simulation and mapping undertakings.

The southeast Indian Ocean is one of the areas where tropical cyclones formed. A comprehensive understanding of the cyclone impact in the Southeastern Indian Ocean is needed to anticipate future changes due to the warming trend. The present study investigates the influence of Cyclone Marcus on oceanographic processes in the subsurface and surface layers and its impact on temperature and Chlorophyll-a in the Southeastern Indian Ocean. The present study applies the Argo Float data located near the peak of the Cyclone Markus path and could capture the subsurface layer vertically that has never been reported previously. This study performs Copernicus data set and Argo Float data to analyze the oceanographic feature of the region before, during, and after Cyclone Marcus. The average surface current velocity increased almost two times during Cyclone Marcus, and the eddy was formed in the clockwise direction following the surface wind pattern. The Argo Float data presents that Cyclone Marcus could induce surface divergence (clockwise eddy) where the cold water and high salinity waters pumped up to the surface layer, starting 1 day after the peak of Cyclone Marcus, resulting in cooling surface temperature by 1.7 °C and deepening mixed layer depth up to 60 m. It implies that the lifted nutrient-rich water stays in the mixed layer depth for 11 days, and sea surface Chlorophyll-a concentration increase with time lags of 2.5 days and 5.6 days, respectively. The Chlorophyll-a concentration increases 2.5 times, and since then starts to decrease until its ‘normal concentration’ within two weeks. Cyclone Marcus triggers the entrainment between the subsurface layer and the sea surface, forcing a phytoplankton growth, particularly in the path area. The future cyclone could increase in the category in the study area, as the warming trend in the Indian Ocean.

Peat swamp forest ecosystems are fragile ecosystems with different peat depths according to the level of peat formation. Moreover, a peat swamp forest can have diverse vegetation and high carbon stocks. Thus, caution should be taken in the sustainable management of a peat swamp forest. However, the connection between vegetation diversity, carbon stocks, and peat depths has not been widely studied in efforts to conserve vegetation and peatlands. This study aimed to analyze the connection between vegetation diversity, carbon stocks, and peat depths in the Kahayan Sebangau Peat Hydrology Unit. Plots at the peat depths of four sites were studied: site 1 (<50 cm), site 2 (393-478 cm), site 3 (479-564 cm), and site 4 (565-649 cm). All Pearson correlation values between peat depth, vegetation diversity, and carbon stock were positive with each other. This shows that peat depth, vegetation diversity, and carbon stock are interdependent and connected to one another.

A solar panel is a device that converts solar rays into electricity. It is a step to reduce emissions from fossil energy, which is to replace it with renewable energy. It requires a control system to ensure that the position of the solar panel is always perpendicular to the sun''s rays. This study aims to modify the fuzzy set based on fuzzy entropy in the control system that has been developed. The modifications made are expected to increase the efficiency of solar panels in harvesting energy. Type II fuzzy sliding mode control is used, along with a modified fuzzy set based on the entropy value. Before modification, the system containing the fuzzy set generates a histogram of entropy and voltage performance, which is the initial value and the comparison value. The algorithm alters the footprint of the uncertainty limit. This change results in a new fuzzy set, which results in a new histogram and voltage. The final step is to compare the initial and final parameters based on the results of the modifications. The solar panels require only 7.3x10-5 degrees of movement per second. This is a very slow movement for a dc motor with a maximum voltage of 12 volts. The simulation produced a stable speed of 7.297x10-5 on the unmodified system and 7.295x10-5 on the modified system. The modified system experiences a slight delay towards the stable point because the fuzzy entropy method reduces the dominance of set point positions in the system. The modified fuzzy set is good at controlling the solar panel driving motor based on the output voltage value. On both controllers under consideration, the voltages follow the same pattern. However, it experienced a control mismatch at the point towards the set point. Finally, by changing the foot of uncertainty and adjusting it proportionally according to control needs, the control system based on fuzzy sets with fuzzy entropy can be further developed.

Soil is the most important basic natural resource for the support of agricultural production systems. Productivity maintenance in these ecosystems depends on their physicochemical. However, there are no significant studies on the current status of soil fertility and quality in tropical areas vulnerable to climate change and lacking management practices. The purpose of this study was to assess the physical and chemical properties of the soil to propose guidelines on soil handling and management in tropical areas.

Data on texture, macronutrients, micronutrients, and cation ratios were collected at 200 farms in the Sucre Department of Northern Colombia. Correlation analysis and principal component analysis were performed on the resulting data set, and a soil quality index was calculated.

Macronutrients N, P, K, S, Ca, Mg, and Na displayed average values of 21.65 ± 10.65 part per million, 40.35 ± 67.21 part per million, 0.46 ± 0.43 meq/100g, 7.94 ± 28.35 part per million, 15.63 ± 17.30 meq/100 g, 5.63 ± 3.58 meq/100g, 0.19 ± 0.20 meq/100g, respectively. Micronutrients Cu, Fe, Zn, and Mn displayed average values of 2.20 ± 1.66 part per million, 48.05 ± 37.87 part per million, 1.16 ± 1.26 part per million, 14.22 ± 12.24 part per million, respectively. The predominant texture among assessed soils was sandy clay loam. A significant correlation was found between (Ca/Mg) K-Ca/K, (Ca/Mg) K-Mg/K, Fe-Cu, and Ca-cation exchange capacity. The soil quality index of the soils assessed in the Department of Sucre indicates a high level of quality, which is strongly influenced by the indicators S, P, Mn (≥ 0.90) Fe, Zn, Cu, K, Na (≥ 0.80).

The macronutrients displayed a deficiency of potassium. It is therefore recommended to monitor these soils and apply fertilization plans according to the needs of each assessed soil. Lastly, this study provides relevant information for proposing guidelines for crop improvement.

Prior to the COVID-19 pandemic, Hoi An City was one of the most famous tourist destinations in the world. This led to a rapid increase in solid waste generation, leading to problems and challenges in solid waste collection and management. This problem is also being experienced by other developing countries of the world. Despite the existence of established waste management strategies, targets set for the collection of recyclable waste have not been met. This study introduces solutions to the problems and challenges faced by the waste management sector in Hoi An city and other developing countries. This study aimed to i) optimize the map of the recovery recycling stations in an urban community, ii) develop an effective solid waste collection system, and iii) provide management tools to enhance recycling activities, contributing to improving waste management in Hoi An city.

The RRSs were integrated into a solid waste collection system in the urban communities of Hoi An City, were conducted through location-allocation analysis in a geographic information system environment. Routing problems of carts were solved in the combination of the rescheduling of existing solid waste collection activities in the study site. The economic evaluation by scenarios was also calculated for ten years to assess the feasibility of scenarios.

Thirty-four locations were identified and optimized to accommodate the RRSs and new collection routes. The distances travelled and working time increased in proportion to the increase in waste separation effectiveness. Waste separation is vital to the effectiveness of the new solid waste collection system. The optimal solid waste practice model (in scenarios 2 and 4) revealed the positive results in improving the solid waste collection system, operating economy, and local adaptation.

This study redesigned the solid waste collection system to solve the current problems in the tourism destination of Hoi An city. This study contributed as a case study of integrating urban recovery recycling stations into optimizing a solid waste collection system in a tourism destination. Introducing strict waste separation was the pivotal first step in systematically upgrading the solid waste collection system in Hoi An City. This study’s findings provide government officials and service providers with methods that can be applied to solve the problems faced by Hoi An city's existing solid waste collection and management system.

The existence of organic matter is one of the main issues for wastewater reclamation since chlorination is applied most frequently before use wastewater reclamation for many purposes. One of the eco-friendly and effective methods is using innovative material through resin immobilized heterogeneous photocatalyst, which is based on the principle of advanced oxidation processes.  Resin immobilized photocatalyst has been using for pollutant reduction, however lack of studies focused on dissolved effluent organic matter and its impact on the formation carcinogenic as by-product of water or wastewater treatment. This study aims to characterize organic matter by resin immobilized photocatalyzed titanium dioxide and zinc oxide and to determine its effectiveness in removing organic matter and potential for disinfection by-products in treated wastewater compare with resin only.

The bulk parameters, including total organic carbon, aromatic organic carbon as ultraviolet at 254 nm wavelength and specific ultraviolet absorbance value, and disinfection by-products formation potential, including trihalomethanes and haloacetic acids concentration was measured.

The results present that all materials could remove organic carbon in the range 58.18% - 93.45%, aromatic organic carbon removal 48.77% - 76.51%, and specific ultraviolet absorbance value decreased into less than 2 L/mg-m after longer contact time. Disinfection by-products formation potential concentration removal decreased and indicated the consistency results with bulk parameters removal. Resin immobilized photocatalyzed zinc oxide performed a higher efficiency removal than resin immobilized photocatalyzed titanium dioxide and resin only.

This study exhibited the performance of resin immobilized photocatalyst with titanium dioxide and zinc oxide in removing dissolved organic matter and to control the formation of disinfection by-products. A combination between bulk parameters and disinfection by-products formation potential removal concluded that the aromatic structure, was mainly haloacetic acids precursors, while the non-aromatic organic fraction was probably trihalomethanes precursors.