Multi-air pollution risk assessment in Southeast Asia region using integrated remote sensing and socio-economic data products

Sakti, Anjar Dimara; Anggraini, Tania Septi; Ihsan, K. T. N.; Misra, Prakhar; Trang, Nguyen Thi Quynh; Pradhan, Biswajeet; Wenten, I. Gede; Hadi, Pradita Octoviandiningrum; Wikantika, Ketut

doi:10.1016/j.scitotenv.2022.158825

Cited by 26 publications

(11 citation statements)

References 35 publications

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“…In this study, the analysis of the estimated energy demand was associated with the distribution of the energy load used to determine the priorities for developing new renewable energy plants. This energy demand analysis was divided into two target areas to better estimate the results: urban and rural 58 . Statistical data on energy demand per capita in each country were integrated with statistical data on the country’s urban energy use and demand.…”

Section: Methodsmentioning

confidence: 99%

Spatial integration framework of solar, wind, and hydropower energy potential in Southeast Asia

Sakti

Rohayani

Izzah

et al. 2023

Sci Rep

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Amid its massive increase in energy demand, Southeast Asia has pledged to increase its use of renewable energy by up to 23% by 2025. Geospatial technology approaches that integrate statistical data, spatial models, earth observation satellite data, and climate modeling can be used to conduct strategic analyses for understanding the potential and efficiency of renewable energy development. This study aims to create the first spatial model of its kind in Southeast Asia to develop multi-renewable energy from solar, wind, and hydropower, further broken down into residential and agricultural areas. The novelty of this study is the development of a new priority model for renewable energy development resulting from the integration of area suitability analysis and the estimation of the amount of potential energy. Areas with high potential power estimations for the combination of the three types of energy are mostly located in northern Southeast Asia. Areas close to the equator, have a lower potential than the northern countries, except for southern regions. Solar photovoltaic (PV) plant construction is the most area-intensive type of energy generation among the considered energy sources, requiring 143,901,600 ha (61.71%), followed by wind (39,618,300 ha; 16.98%); a combination of solar PV and wind (37,302,500 ha; 16%); hydro (7,665,200 ha; 3.28%); a combination of hydro and solar PV (3,792,500 ha; 1.62%); and a combination of hydro and wind (582,700 ha; 0.25%). This study is timely and important because it will inform policies and regional strategies for transitioning to renewable energy, with consideration of the different characteristics present in Southeast Asia.

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Section: Methodsmentioning

confidence: 99%

Spatial integration framework of solar, wind, and hydropower energy potential in Southeast Asia

Sakti

Rohayani

Izzah

et al. 2023

Sci Rep

Self Cite

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“…While the NO2 data used is air pollution data in the troposphere (ESA, 2018). Night light and NO2 data can indicate human activity and indicate an urban area (Sakti et al, 2023). Sources of NO2 gas are more dominant in urban areas which can be caused by road activities (motorized vehicles), industry, mining, power plants, and land/forest fires (Sakti et al, 2023).…”

Section: Socioeconomic Factorsmentioning

confidence: 99%

“…Night light and NO2 data can indicate human activity and indicate an urban area (Sakti et al, 2023). Sources of NO2 gas are more dominant in urban areas which can be caused by road activities (motorized vehicles), industry, mining, power plants, and land/forest fires (Sakti et al, 2023). Data processing will compare socioeconomic data at two different temporal points, before and after the tsunami.…”

Section: Socioeconomic Factorsmentioning

confidence: 99%

The Impact of Environmental, Social, And Economic Factors Due to Anak Krakatau Volcano Tsunami Using Remote Sensing Technology

Virtriana,

Harto,

Rohayani

et al. 2023

IOP Conf. Ser.: Earth Environ. Sci.

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Tsunamis often occur in the territory of Indonesia because it is widely traversed by encounters between earth plates under the sea. Thus, if an earthquake occurs, it has the potential to generate a tsunami. The Indonesian region is also located in the ring of the fire area, and is the country with the highest number of active volcanoes with more than 127 events, that can encourage volcanic earthquakes. In addition, when volcanoes erupt offshore, the potential for a tsunami will be even greater because the expulsion of material can generate landslides toward the sea, causing a tsunami. The tsunami that happened in the eruption of Anak Krakatau on December 22, 2018, in the Sunda Strait, resulted in a tsunami in the Lampung and Banten areas. This study tries to compare environmental, social, and economic factors before and after the tsunami in the coastal areas of Lampung and Banten. The integration of remote sensing data will be carried out to determine environmental, social, and economic factors such as Land Cover, NDWI, NDVI, Night Light, and NO2 data. Random Forest Machine Learning will also be involved in shaping land cover models. Random Forest is often used because it has high accuracy. The results show a significant change in land cover in coastal areas and the impact of environmental, social, and economic factors. This study is expected to support the mitigation process in the event of a tsunami in the future. In addition, it is expected to be used by policymakers in planning development after the tsunami disaster.

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“…Additionally, this study relies solely on NDWI averages and does not integrate tidal data to assess coastal changes. However, this research can serve as a reference for dynamic observations related to renewable energy potential (Bender et al, 2017;Ihsan et al, 2022Ihsan et al, , 2021, air pollution (Pavani and Rao, 2017;A.D. Sakti et al, 2023), hazard (Das et al, 2006;Virtriana et al, 2023), and food security (Gommes et al, 2016;Virtriana et al, 2022) in a particular region.…”

Section: Prediction Change In Futurementioning

confidence: 99%

Monitoring Coastal Areas Using Ndwi From Landsat Image Data From 1985 Based on Cloud Computation Google Earth Engine and Apps

Ihsan,

Harto,

Sakti

et al. 2023

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. The coastal area is an area that has a dense population with a lot of human activities that occur there. Due to environmental changes and human activities, changes often occur in coastal areas ranging from erosion and sedimentation. Changes must continuously be monitored to plan countermeasures due to the occurring phenomena. This study aims to create a website-based application to monitor coastal areas. This study will use Landsat data 5,7,8, and 9 to see changes in coastal areas. The analysis can be provided from 1985 until recent data by integrating four Landsat satellites. The NDWI index (Normalized Difference Wetness Index) analyzes changes occurring in coastal areas and differentiates between water and land area. The analysis is not only in the form of changes that occur in coastal areas but also in time series analysis, and trends that occur at a point can be analyzed using land trend analysis. The resulting website based on Cloud Computation in Google Earth Engine can be seen at the link https://bit.ly/MonitoringPesisir. This website can automatically update, and users can choose the location to monitor. This research is expected to be used by policymakers to monitor and plan the development and regulation of coastal areas.

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Multi-air pollution risk assessment in Southeast Asia region using integrated remote sensing and socio-economic data products

Cited by 26 publications

References 35 publications

Spatial integration framework of solar, wind, and hydropower energy potential in Southeast Asia

Spatial integration framework of solar, wind, and hydropower energy potential in Southeast Asia

The Impact of Environmental, Social, And Economic Factors Due to Anak Krakatau Volcano Tsunami Using Remote Sensing Technology

Monitoring Coastal Areas Using Ndwi From Landsat Image Data From 1985 Based on Cloud Computation Google Earth Engine and Apps

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