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Research Article
Evaluation of Knowledge, Attitude, Motivation, and Behavior of Low-Income Community Towward Making Environmentally Safe Toilets in Lowland Areas of South Sulawesi Province
Bakhrani Abdul Rauf,
Faizal Amir,
St. Fatmah Hiola,
Mithen Lullulangi*
,
Haruna,
Rahmansah
Issue:
Volume 9, Issue 4, December 2025
Pages:
157-166
Received:
24 August 2025
Accepted:
11 September 2025
Published:
30 September 2025
Abstract: Safe and proper sanitation is one of the important aspects in efforts to improve the quality of public health. However, in the lowland areas of South Sulawesi Province, low-income communities still face challenges in building and using environmentally safe toilets. The objectives of this study were to: (1) assess public knowledge in building environmentally friendly toilets, (2) assess public attitudes towards building healthy toilets, (3) assess the level of public motivation, and (4) assess public behavior in building environmentally safe toilets in the lowland areas of South Sulawesi Province. This study was conducted in several lowland areas involving 300 heads of families from low-income communities selected using the purposive sampling method. The variables studied included public knowledge, attitudes, motivation, and behavior towards building healthy toilets. Data analysis was conducted using descriptive statistics. The results of the study showed that: (1) the level of knowledge of low-income communities in building environmentally safe toilets was in the high category, (2) public attitudes towards the importance of healthy toilets were also in the high category, (3) public motivation to build healthy toilets was high, and (4) real community behavior in building environmentally safe toilets also showed a high category. These findings indicate that although economic limitations are a challenge, community awareness and commitment to maintaining environmental sanitation are quite good.
Abstract: Safe and proper sanitation is one of the important aspects in efforts to improve the quality of public health. However, in the lowland areas of South Sulawesi Province, low-income communities still face challenges in building and using environmentally safe toilets. The objectives of this study were to: (1) assess public knowledge in building enviro...
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Research Article
A Multi-Temporal Land Cover Analysis of Kathmandu Using Google Earth Engine and ENVI: A Comparative Study of SVM and RF Algorithms
Issue:
Volume 9, Issue 4, December 2025
Pages:
167-182
Received:
22 September 2025
Accepted:
5 October 2025
Published:
28 October 2025
DOI:
10.11648/j.ajese.20250904.12
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Abstract: Rapid urbanization and land cover change have emerged as major environmental concerns in developing regions, particularly within the Kathmandu District of Nepal. This study aims to analyze multi-temporal land cover changes and compare the performance of two machine learning algorithms—Support Vector Machine (SVM) and Random Forest (RF)—across two platforms: Google Earth Engine (GEE) and ENVI. Sentinel-2 satellite imagery from 2017, 2020, and 2023 was utilized to classify four major land cover classes (water, bareland, built-up, and vegetation) using supervised classification techniques. Preprocessing included cloud masking, filtering, and subsetting, while training samples were generated from high-resolution Google Earth Pro and Copernicus Sentinel imagery. Accuracy was assessed using user’s accuracy, producer’s accuracy, overall accuracy, and the Kappa coefficient derived from confusion matrices. Results indicate a steady increase in built-up areas (from 24.75% in 2017 to 37.06% in 2023) and bareland, alongside a marked decline in vegetation. The RF algorithm in GEE achieved the highest performance with an overall accuracy of 98.43% and Kappa coefficient of 0.9773 in 2023, demonstrating strong stability across all years. SVM, while slightly less consistent, achieved 97.6% user accuracy and 98.8% producer accuracy for the water class in 2023, outperforming RF in that category. ENVI-based SVM models attained an overall accuracy of 91.96% and Kappa coefficient of 0.8862, performing well for vegetation but showing slightly lower robustness than RF in GEE. In conclusion, the integration of cloud-based (GEE) and desktop (ENVI) remote sensing platforms with machine learning algorithms proved highly effective for large-scale urban monitoring. The findings highlight rapid urban expansion and vegetation loss in Kathmandu and offer valuable insights for sustainable urban planning and environmental management.
Abstract: Rapid urbanization and land cover change have emerged as major environmental concerns in developing regions, particularly within the Kathmandu District of Nepal. This study aims to analyze multi-temporal land cover changes and compare the performance of two machine learning algorithms—Support Vector Machine (SVM) and Random Forest (RF)—across two p...
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Review Article
Impacts of COVID-19 Lockdowns on Environmental Degradation and Climate Change: A Review
Abdullahi Hassan Gana*,
Abba Idris Sa’id
Issue:
Volume 9, Issue 4, December 2025
Pages:
183-189
Received:
6 September 2025
Accepted:
18 September 2025
Published:
31 October 2025
DOI:
10.11648/j.ajese.20250904.13
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Abstract: The COVID-19 pandemic, first identified in late 2019, rapidly transformed social and economic systems worldwide. To contain its spread, many governments imposed strict restrictions that closed businesses, schools, airports, factories, and other socio-economic activities. These unprecedented measures created a global “experiment” in reducing human pressures on the environment, providing valuable insights into how ecosystems respond when anthropogenic activities are curtailed. Short-term environmental improvements were widely reported. Urban air quality improved markedly, with nitrogen dioxide concentrations falling and global daily CO2 emissions declining by up to 17% at the peak of lockdowns. Air pollution dropped by about 25% in China and nearly 50% in New York compared with the previous year. Water bodies also exhibited temporary gains, including greater clarity and reductions in biochemical and chemical oxygen demand, largely due to reduced industrial discharges. Lockdowns further suppressed anthropogenic noise and seismic activity, while wildlife displayed shifts in movement and behavior during the “anthropause.” At the same time, the pandemic introduced new environmental pressures. Increased household wastewater and detergent use added pollutant loads to urban systems, while surges in personal protective equipment (PPE) and medical waste intensified plastic pollution risks. Conservation programs were interrupted, and global CO2 emissions rebounded strongly by 2021, highlighting the temporary nature of early gains. These contrasting outcomes underscore the complexity of human–environment interactions under crisis conditions. While the pandemic revealed the scale of improvement achievable through short-term behavioral change, it also confirmed that durable benefits require systemic policy interventions. Strengthening environmental monitoring, building resilient waste management systems, and aligning recovery strategies with climate and biodiversity objectives are essential to embedding the lessons of the pandemic into long-term sustainability planning. This review therefore synthesizes emerging evidence on the environmental impacts of COVID-19 lockdowns, identifying both opportunities and challenges to inform future climate and sustainability strategies.
Abstract: The COVID-19 pandemic, first identified in late 2019, rapidly transformed social and economic systems worldwide. To contain its spread, many governments imposed strict restrictions that closed businesses, schools, airports, factories, and other socio-economic activities. These unprecedented measures created a global “experiment” in reducing human p...
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