The Internet of Things (IoT) has been identified in various applications across different domains, such as in the healthcare sector. IoT has also been recognised for its revolution in reshaping modern healthcare with aspiring wide range prospects, including economical, technological and social. This study aims to establish IoT-based smart home security solutions for real-time health monitoring technologies in telemedicine architecture. A multilayer taxonomy is driven and conducted in this study. In the first layer, a comprehensive analysis on telemedicine, which focuses on the client and server sides, shows that other studies associated with IoT-based smart home applications have several limitations that remain unaddressed. Particularly, remote patient monitoring in healthcare applications presents various facilities and benefits by adopting IoT-based smart home technologies without compromising the security requirements and potentially large number of risks. An extensive search is conducted to identify articles that handle these issues, related applications are comprehensively reviewed and a coherent taxonomy for these articles is established. A total number of (n = 3064) are gathered between 2007 and 2017 for most reliable databases, such as ScienceDirect, Web of Science and Institute of Electrical and Electronic Engineer Xplore databases. Then, the articles based on IoT studies that are associated with telemedicine applications are filtered. Nine articles are selected and classified into two categories. The first category, which accounts for 22.22% (n = 2/9), includes surveys on telemedicine articles and their applications. The second category, which accounts for 77.78% (n = 7/9), includes articles on the client and server sides of telemedicine architecture. The collected studies reveal the essential requirement in constructing another taxonomy layer and review IoT-based smart home security studies. Therefore, IoT-based smart home security features are introduced and analysed in the second layer. The security of smart home design based on IoT applications is an aspect that represents a crucial matter for general occupants of smart homes, in which studies are required to provide a better solution with patient security, privacy protection and security of users' entities from being stolen or compromised. Innovative technologies have dispersed limitations related to this matter. The existing gaps and trends in this area should be investigated to provide valuable visions for technical environments and researchers. Thus, 67 articles are obtained in the second layer of our taxonomy and are classified into six categories. In the first category, 25.37% (n = 17/67) of the articles focus on architecture design. In the second category, 17.91% (n = 12/67) includes security analysis articles that investigate the research status in the security area of IoT-based smart home applications. In the third category, 10.44% (n = 7/67) includes articles about security schemes. In the fourth category, 17.91% (n = 12/67) comprises security examination. In the fifth category, 13.43% (n = 9/67) analyses security protocols. In the final category, 14.92% (n = 10/67) analyses the security framework. Then, the identified basic characteristics of this emerging field are presented and provided in the following aspects. Open challenges experienced on the development of IoT-based smart home security are addressed to be adopted fully in telemedicine applications. Then, the requirements are provided to increase researcher's interest in this study area. On this basis, a number of recommendations for different parties are described to provide insights on the next steps that should be considered to enhance the security of smart homes based on IoT. A map matching for both taxonomies is developed in this study to determine the novel risks and benefits of IoT-based smart home security for real-time remote health monitoring within client and server sides in telemedicine applications.
The development of wireless body area sensor networks is imperative for modern telemedicine. However, attackers and cybercriminals are gradually becoming aware in attacking telemedicine systems, and the black market value of protected health information has the highest price nowadays. Security remains a formidable challenge to be resolved. Intelligent home environments make up one of the major application areas of pervasive computing. Security and privacy are the two most important issues in the remote monitoring and control of intelligent home environments for clients and servers in telemedicine architecture. The personal authentication approach that uses the finger vein pattern is a newly investigated biometric technique. This type of biometric has many advantages over other types (explained in detail later on) and is suitable for different human categories and ages. This study aims to establish a secure verification method for real-time monitoring systems to be used for the authentication of patients and other members who are working in telemedicine systems. The process begins with the sensor based on Tiers 1 and 2 (client side) in the telemedicine architecture and ends with patient verification in Tier 3 (server side) via finger vein biometric technology to ensure patient security on both sides. Multilayer taxonomy is conducted in this research to attain the study's goal. In the first layer, real-time remote monitoring studies based on the sensor technology used in telemedicine applications are reviewed and analysed to provide researchers a clear vision of security and privacy based on sensors in telemedicine. An extensive search is conducted to identify articles that deal with security and privacy issues, related applications are reviewed comprehensively and a coherent taxonomy of these articles is established. ScienceDirect, IEEE Xplore and Web of Science databases are checked for articles on mHealth in telemedicine based on sensors. A total of 3064 papers are collected from 2007 to 2017. The retrieved articles are filtered according to the security and privacy of telemedicine applications based on sensors. Nineteen articles are selected and classified into two categories. The first category, which accounts for 57.89% (n = 11/19), includes surveys on telemedicine articles and their applications. The second category, accounting for 42.1% (n = 8/19), includes articles on the three-tiered architecture of telemedicine. The collected studies reveal the essential need to construct another taxonomy layer and review studies on finger vein biometric verification systems. This map-matching for both taxonomies is developed for this study to go deeply into the sensor field and determine novel risks and benefits for patient security and privacy on client and server sides in telemedicine applications. In the second layer of our taxonomy, the literature on finger vein biometric verification systems is analysed and reviewed. In this layer, we obtain a final set of 65 articles classified into four categories. In the first category, 80% (n = 52/65) of the articles focus on development and design. In the second category, 12.30% (n = 8/65) includes evaluation and comparative articles. These articles are not intensively included in our literature analysis. In the third category, 4.61% (n = 3/65) includes articles about analytical studies. In the fourth category, 3.07% (n = 2/65) comprises reviews and surveys. This study aims to provide researchers with an up-to-date overview of studies that have been conducted on (user/patient) authentication to enhance the security level in telemedicine or any information system. In the current study, taxonomy is presented by explaining previous studies. Moreover, this review highlights the motivations, challenges and recommendations related to finger vein biometric verification systems and determines the gaps in this research direction (protection of finger vein templates in real time), which represent a new research direction in this area.
Ground-based LiDAR also known as Terrestrial Laser Scanning (TLS) technology is an active remote sensing imaging method said to be one of the latest advances and innovations for plant phenotyping. Basal Stem Rot (BSR) is the most destructive disease of oil palm in Malaysia that is caused by white-rot fungus Ganoderma boninense, the symptoms of which include flattening and hanging-down of the canopy, shorter leaves, wilting green fronds and smaller crown size. Therefore, until now there is no critical investigation on the characterisation of canopy architecture related to this disease using TLS method was carried out. This study proposed a novel technique of BSR classification at the oil palm canopy analysis using the point clouds data taken from the TLS. A total of 40 samples of oil palm trees at the age of nine-years-old were selected and 10 trees for each health level were randomly taken from the same plot. The trees were categorised into four health levels - T0, T1, T2 and T3, which represents the healthy, mildly infected, moderately infected and severely infected, respectively. The TLS scanner was mounted at a height of 1 m and each palm was scanned at four scan positions around the tree to get a full 3D image. Five parameters were analysed: S200 (canopy strata at 200 cm from the top), S850 (canopy strata at 850 cm from the top), crown pixel (number of pixels inside the crown), frond angle (degree of angle between fronds) and frond number. The results taken from statistical analysis revealed that frond number was the best single parameter to detect BSR disease as early as T1. In classification models, a linear model with a combination of parameters, ABD - A (frond number), B (frond angle) and D (S200), delivered the highest average accuracy for classification of healthy-unhealthy trees with an accuracy of 86.67 per cent. It also can classify the four severity levels of infection with an accuracy of 80 per cent. This model performed better when compared to the severity classification using frond number. The novelty of this research is therefore on the development of new approach to detect and classify BSR using point clouds data of TLS.
Decreasing water pollution is a big problem in coastal waters. Coastal health of ecosystems can be affected by high concentrations of suspended sediment. In this work, a Modified Hopfield Neural Network Algorithm (MHNNA) was used with remote sensing imagery to classify the total suspended solids (TSS) concentrations in the waters of coastal Langkawi Island, Malaysia. The adopted remote sensing image is the Advanced Land Observation Satellite (ALOS) image acquired on 18 January 2010. Our modification allows the Hopfield neural network to convert and classify color satellite images. The samples were collected from the study area simultaneously with the acquiring of satellite imagery. The sample locations were determined using a handheld global positioning system (GPS). The TSS concentration measurements were conducted in a lab and used for validation (real data), classification, and accuracy assessments. Mapping was achieved by using the MHNNA to classify the concentrations according to their reflectance values in band 1, band 2, and band 3. The TSS map was color-coded for visual interpretation. The efficiency of the proposed algorithm was investigated by dividing the validation data into two groups. The first group was used as source samples for supervisor classification via the MHNNA. The second group was used to test the MHNNA efficiency. After mapping, the locations of the second group in the produced classes were detected. Next, the correlation coefficient (R) and root mean square error (RMSE) were calculated between the two groups, according to their corresponding locations in the classes. The MHNNA exhibited a higher R (0.977) and lower RMSE (2.887). In addition, we test the MHNNA with noise, where it proves its accuracy with noisy images over a range of noise levels. All results have been compared with a minimum distance classifier (Min-Dis). Therefore, TSS mapping of polluted water in the coastal Langkawi Island, Malaysia can be performed using the adopted MHNNA with remote sensing techniques (as based on ALOS images).
Malaysia has a long coastline stretching over 4,809 km where more than 1,300 km of beaches are experiencing erosion.
Coastal erosion is recognised as the permanent loss of land and habitats along the shoreline resulting in the changes
of the coast. Thus, it is important to detect and monitor shoreline changes especially in Pahang coast by identifying the
rate of shoreline erosion and accretion. This study used temporal data and high spatial resolution imagery (SPOT 5) using
remote sensing and GIS techniques to monitor shoreline changes along 10 study locations, which is from Cherating to
Pekan of the Pahang coast. The total length of shoreline changes is about 14 km (14035.10 m) where all these areas are
very likely to experience erosion ranging from 0.1 to 94.7 ha. On the other hand, these coastal areas found a minimal
accretion with increased sediment from 0.1 to 2.8 ha. Overall, the coastal areas are exposed to higher erosion process
than accretion with a very high vulnerability of erosion rate from 1.8 to 20.9 meter per year. The findings on monitoring
shoreline changes and identifying vulnerable erosion areas might be useful in the policy and decision making for
sustainable coastal management.
The FLUXNET2015 dataset provides ecosystem-scale data on CO2, water, and energy exchange between the biosphere and the atmosphere, and other meteorological and biological measurements, from 212 sites around the globe (over 1500 site-years, up to and including year 2014). These sites, independently managed and operated, voluntarily contributed their data to create global datasets. Data were quality controlled and processed using uniform methods, to improve consistency and intercomparability across sites. The dataset is already being used in a number of applications, including ecophysiology studies, remote sensing studies, and development of ecosystem and Earth system models. FLUXNET2015 includes derived-data products, such as gap-filled time series, ecosystem respiration and photosynthetic uptake estimates, estimation of uncertainties, and metadata about the measurements, presented for the first time in this paper. In addition, 206 of these sites are for the first time distributed under a Creative Commons (CC-BY 4.0) license. This paper details this enhanced dataset and the processing methods, now made available as open-source codes, making the dataset more accessible, transparent, and reproducible.
Soil salinization is an extremely serious land degradation problem in arid and semi-arid regions that hinders the sustainable development of agriculture and food security. Information and research on soil salinity using remote sensing (RS) technology provide a quick and accurate assessment and solutions to address this problem. This study aims to compare the capabilities of Landsat-8 OLI and Sentinel-2A MSI in RS prediction and exploration of the potential application of derivatives to RS prediction of salinized soils. It explores the ability of derivatives to be used in the Landsat-8 OLI and Sentinel-2A MSI multispectral data, and it was used as a data source as well as to address the adaptability of salinity prediction on a regional scale. The two-dimensional (2D) and three-dimensional (3D) optimal spectral indices are used to screen the bands that are most sensitive to soil salinity (0-10 cm), and RS data and topographic factors are combined with machine learning to construct a comprehensive soil salinity estimation model based on gray correlation analysis. The results are as follows: (1) The optimal spectral index (2D, 3D) can effectively consider possible combinations of the bands between the interaction effects and responding to sensitive bands of soil properties to circumvent the problem of applicability of spectral indices in different regions; (2) Both the Landsat-8 OLI and Sentinel-2A MSI multispectral RS data sources, after the first-order derivative techniques are all processed, show improvements in the prediction accuracy of the model; (3) The best performance/accuracy of the predictive model is for sentinel data under first-order derivatives. This study compared the capabilities of Landsat-8 OLI and Sentinel-2A MSI in RS prediction in finding the potential application of derivatives to RS prediction of salinized soils, with the results providing some theoretical basis and technical guidance for salinized soil prediction and environmental management planning.
Both niche and stochastic dispersal processes structure the extraordinary diversity of tropical plants, but determining their relative contributions has proven challenging. We address this question using airborne imaging spectroscopy to estimate canopy β-diversity for an extensive region of a Bornean rainforest and challenge these data with models incorporating niches and dispersal. We show that remotely sensed and field-derived estimates of pairwise dissimilarity in community composition are closely matched, proving the applicability of imaging spectroscopy to provide β-diversity data for entire landscapes of over 1000 ha containing contrasting forest types. Our model reproduces the empirical data well and shows that the ecological processes maintaining tropical forest diversity are scale dependent. Patterns of β-diversity are shaped by stochastic dispersal processes acting locally whilst environmental processes act over a wider range of scales.
Geographic Information System (GIS) and remote sensing are geospatial technologies that have been used for many years in environmental studies, including gathering and analysing of information on the physical parameters of wildlife habitats and modelling of habitat assessments. The home range estimation provided in a GIS environment offers a viable method of quantifying habitat use and facilitating a better understanding of species and habitat relationships. This study used remote sensing, GIS and Analytic Hierarchy Process (AHP) application tools as methods to assess the habitat parameters preference of Asian elephant. Satellite images and topographical maps were used for the environmental and topographical habitat parameter generation encompassing land use-land cover (LULC), Normalized Digital Vegetation Index (NDVI), water sources, Digital Elevation Model (DEM), slope and aspect. The kernel home range was determined using elephant distribution data from satellite tracking, which were then analysed using habitat parameters to investigate any possible relationship. Subsequently, the frequency of the utilization distribution of elephants was further analysed using spatial and geostatistical analyses. This was followed by the use of AHP for identifying habitat preference, selection of significant habitat parameters and classification of criterion. The habitats occupied by the elephants showed that the conservation of these animals would require good management practices within and outside of protected areas so as to ensure the level of suitability of the habitat, particularly in translocation areas.
The use of remote sensing in detecting aerosol or air pollution is not widely applied in Malaysia. The large area of coverage provided by remote sensing satellite may well be the solution to the lack of spatial coverage by the local ground air quality monitoring stations. This article discusses the application of remote sensing instruments in air quality monitoring of Malaysia. The remote sensing data is validated using ground truths either from local ground air monitoring stations or the Aerosol Robotic Network (AERONET). The correlation between remote sensing is relatively good with R from 0.5 to 0.9 depending on the satellite used. The correlation is much improved using the mixed effects algorithm applied on MODIS Aerosol Optical Depth (AOD) data. Accuracy of predicted air quality data by remote sensing is generally tested using the Root Mean Squared Error (RMSE) against the ground truths data. Besides the Geographic Information System (GIS) tools are used in manipulating the data from both remote sensing and ground stations so as to produce meaningful results such as spatio-temporal pattern mapping of air pollution. Overall the results showed that the application of remote sensing instruments in air quality monitoring in Malaysia is very useful and can be improved further.
The aim of this paper is to review the potentialities and major methodological challenges
of integrating remote sensing (RS) and geographic information system (GIS) with socioeconomic data
from published articles or book chapters. RS and GIS combined with social science (SS)(termed as
geoinformation technology) serve many applications for sustainable management and monitoring of
the environment. This combined approach gives more accurate results than the single one. It makes
information available about the trend and pattern of land use and land cover change (LUCC) with
socioeconomic variables like population, demographic or income. This combined study which links
RS and GIS with socioeconomic data can also be used successfully for monitoring transmission rate
of disease and mapping or preparing vulnerability index. For impact assessment and modelling, this
combined technology provides better results than the single one. There are some methodological
problems for the researchers to link completely two different disciplines as the object of study and
observational unit is completely different. However, this interdisciplinary study is gaining popularity
day by day to researchers from different disciplines as well as decision makers.
Analysis of relationship between sea surface temperature (SST) and Chlorophyll-a (chl-a) improves our understanding on the variability and productivity of the marine environment, which is important for exploring fishery resources. Monthly level 3 and daily level 1 images of Moderate Resolution Imaging Spectroradiometer Satellite (MODIS) derived SST and chl-a from July 2002 to June 2011 around the archipelagic waters of Spermonde Indonesia were used to investigate the relationship between SST and chl-a and to forecast the potential fishing ground of Rastrelliger kanagurta. The results indicated that there was positive correlation between SST and chl-a (R=0.3, p<0.05). Positive correlation was also found between SST and chl-a with the catch of R. kanagurta (R=0.7, p<0.05). The potential fishing grounds of R. kanagurta were found located along the coast (at accuracy of 76.9%). This study indicated that, with the integration of remote sensing technology, statistical modeling and geographic information systems (GIS) technique were able to determine the relationship between SST and chl-a and also able to forecast aggregation of R. kanagurta. This may contribute in decision making and reducing search hunting time and cost in fishing activities.
The aim of this study is to investigate the factors that cause landslides in the area along the new road between Cameron Highlands and Gua Musang. Landslide factors such as lineaments have been extracted from remote sensing data (Landsat TM image) using ERDAS software. A soil map has been produced using field work and laboratory analysis, and the lithology, roads, drainage pattern and rainfall have been digitized using ILWIS software together with the slope angle and elevation from the Digital Elevation Model (DEM). All these parameters, which are vital for landslide hazard assessment, have been integrated into the geographical information system (GIS) for further data processing. Weightage for these landslide relevant factors related to their influence in landslide occurrence using the heuristic method has been carried out. The results from this combination through a modified ‘index overlay with multi class maps’ model was used to produce a landslide hazard zonation map. Five classes of potential landslide hazard have been derived as the following: very low hazard zone 17.27%, low hazard zone 39.35%, medium hazard zone 25.1%, high hazard zone 15.35% and very high hazard zone 2.93%. The results from this work have been checked through the landslide inventory using available aerial photos interpretation and field work, and show that the slope and elevation have the most direct affect on landslide occurrence.
Biomass burning is one of the main sources of air pollution in South East Asia, predominantly during the dry period between June and October each year. Sumatra and Kalimantan, Indonesia, have been identified as the regions connected to biomass burning due to their involvement in agricultural activities. In Sumatra, the Province of Riau has always been found to have had the highest number of hotspots during haze episodes. This study aims to determine the concentration of five major pollutants (PM10, SO2, NO2, CO and O3) in Riau, Indonesia, for 2006 and 2007. It will also correlate the level of air pollutants to the number of hotspots recorded, using the hotspot information system introduced by the Malaysian Centre for Remote Sensing (MACRES). Overall, the concentration of air pollutants recorded was found to increase with the number of hotspots. Nevertheless, only the concentration of PM10 during a haze episode is significantly different when compared to its concentration in non-haze conditions. In fact, in August 2006, when the highest number of hotspots was recorded the concentration of PM10 was found to increase by more than 20% from its normal concentration. The dispersion pattern, as simulated by the Hybrid Single Particle Lagrangian Integrated Trajectory Model (HYSPLIT), showed that the distribution of PM10 was greatly influenced by the wind direction. Furthermore, the particles had the capacity to reach the Peninsular Malaysia within 42 hours of emission from the point sources as a consequence of the South West monsoon.
This paper presents the application of TOPMODEL in the Pinang catchment of Malaysia for stream flow simulation. An attempt has been made to use remote-sensing data (ASTER DEM of 30 m resolution) as a primary input for TOPMODEL in order to simulate the stream flow pattern of this tropical catchment. A calibration period was executed based on 2007-2008 hydro-meteorological dataset which gave a satisfactory Nash-Sutcliffe model (NS) model efficiency of 0.749 and a relative volume error (RVE) of -19.2. The recession curve parameter (m) and soil transmissivity at saturation zone (To), were established as the most sensitive parameters through a sensitivity analysis processes. Hydro-meteorological datasets for the period between 2009 and 2010 were used to validate the model which resulted in satisfactory efficiencies of 0.774 (NS) and -19.84 (RVE), respectively. This study demonstrated the ability ASTER DEM acquired from remote sensing to generate the required TOPMODEL parameters for stream flow simulation which gives insights into better management of available water resources.
Countering the dangers associated the present extreme climate not only requires continuous improvement of local disaster
prevention engineering infrastructure but also needs an enhanced understanding of the causes of the disasters. This study
investigates the geologic hazard risk of 53 slopeland villages in Pingtung county of southern Taiwan. First, remote sensing
(RS) techniques were utilized to interpret environmental geology and geologic hazard zonation, including dip slope, fault,
landslide and debris flow. GIS map overlay analysis was used to further identify the extent of the geologic hazard zonation.
As a final step, field investigation is used to comprehend geologic, topographic conditions and the geologic hazard risk
specific to each locality. Based on data analysis and field investigation results, this study successfully integrates RS, GIS
and GPS techniques to construct a geologic hazard risk assessment method of slopeland village. The results of this study
can be used to promote support for future disaster prevention and disaster mitigation efforts.
Biomass burning is one of the main sources of air pollution in South East Asia, predominantly during the dry period between June and October each year. Sumatra and Kalimantan, Indonesia, have been identified as the regions connected to biomass burning due to their involvement in agricultural activities. In Sumatra, the Province of Riau has always been found to have had the highest number of hotspots during haze episodes. This study aims to determine the concentration of five major pollutants (PM10, SO2, NO2, CO and O3) in Riau, Indonesia, for 2006 and 2007. It will also correlate the level of air pollutants to the number of hotspots recorded, using the hotspot information system introduced by the Malaysian Centre for Remote Sensing (MACRES). Overall, the concentration of air pollutants recorded was found to increase with the number of hotspots. Nevertheless, only the concentration of PM10 during a haze episode is significantly different when compared to its concentration in non-haze conditions. In fact, in August 2006, when the highest number of hotspots was recorded the concentration of PM10 was found to increase by more than 20% from its normal concentration. The dispersion pattern, as simulated by the Hybrid Single Particle Lagrangian Integrated Trajectory Model (HYSPLIT), showed that the distribution of PM10 was greatly influenced by the wind direction. Furthermore, the particles had the capacity to reach the Peninsular Malaysia within 42 hours of emission from the point sources as a consequence of the South West monsoon.
Conventional forest inventory practice took huge of effort, and is time- and cost- consuming. With the aid of remote sensing technology by light detection and ranging (LiDAR), those unbearable factors could be minimized. LiDAR is able to capture forest characteristic information and is well known for estimating forest structure accurately in many studies. Forest monitoring related to forest resource inventory (FRI) becomes more effective by utilizing LiDAR data and it is tremendously useful, especially to distinguish information on density, growth and distribution of trees in a selected area. In this study, LiDAR data was utilized aimed to delineate crown cover and estimate upper-storey canopy area in Yambaru Forest using object-based segmentation and classification techniques. Agreement between field survey and LiDAR data analysis showed that only 33.7% of upper-storey canopy area was successfully delineated. The low accuracy level of canopy detection in Yambaru Forest area was expected mainly due to tree structure, density and topographic condition.
Brunei Bay, which receives freshwater discharge from four major rivers, namely Limbang, Sundar, Weston and Menumbok, hosts a luxuriant mangrove cover in East Malaysia. However, this relatively undisturbed mangrove forest has been less scientifically explored, especially in terms of vegetation structure, ecosystem services and functioning, and land-use/cover changes. In the present study, mangrove areal extent together with species composition and distribution at the four notified estuaries was evaluated through remote sensing (Advanced Land Observation Satellite-ALOS) and ground-truth (Point-Centred Quarter Method-PCQM) observations. As of 2010, the total mangrove cover was found to be ca. 35,183.74 ha, of which Weston and Menumbok occupied more than two-folds (58%), followed by Sundar (27%) and Limbang (15%). The medium resolution ALOS data were efficient for mapping dominant mangrove species such asNypa fruticans,Rhizophora apiculata,Sonneratia caseolaris,S. albaandXylocarpus granatumin the vicinity (accuracy: 80%). The PCQM estimates found a higher basal area at Limbang and Menumbok-suggestive of more mature vegetation, compared to Sundar and Weston. Mangrove stand structural complexity (derived from the complexity index) was also high in the order of Limbang > Menumbok > Sundar > Weston and supporting the perspective of less/undisturbed vegetation at two former locations. Both remote sensing and ground-truth observations have complementarily represented the distribution ofSonneratiaspp. as pioneer vegetation at shallow river mouths,N. fruticansin the areas of strong freshwater discharge,R. apiculatain the areas of strong neritic incursion andX. granatumat interior/elevated grounds. The results from this study would be able to serve as strong baseline data for future mangrove investigations at Brunei Bay, including for monitoring and management purposes locally at present.
Air pollution has massive impacts on human life and poor air quality results in three million deaths annually. Air pollution can result from natural causes, including volcanic eruptions and extreme droughts, or human activities, including motor vehicle emissions, industry, and the burning of farmland and forests. Emission sources emit multiple pollutant types with diverse characteristics and impacts. However, there has been little research on the risk of multiple air pollutants; thus, it is difficult to identify multi-pollutant mitigation processes, particularly in Southeast Asia, where air pollution moves dynamically across national borders. In this study, the main objective was to develop a multi-air pollution risk index product for CO, NO2, and SO2 based on Sentinel-5P remote sensing data from 2019 to 2020. The risk index was developed by integrating hazard, vulnerability, and exposure analyses. Hazard analysis considers air pollution data from remote sensing, vulnerability analysis considers the air pollution sources, and exposure analysis considers the population density. The novelty of this study lies in its development of a multi-risk model that considers the weights obtained from the relationship between the hazard and vulnerability parameters. The highest air pollution risk index values were observed in urban areas, with a high exposure index that originates from pollution caused by human activity. Multi-risk analysis of the three air pollutants revealed that Singapore, Vietnam, and the Philippines had the largest percentages of high-risk areas, while Indonesia had the largest total high-risk area (4361 km2). Using the findings of this study, the patterns and characteristics of the risk distribution of multiple air pollutants in Southeast Asia can be identified, which can be used to mitigate multi-pollutant sources, particularly with respect to supporting the clean air targets in the Sustainable Development Goals.