Mapping Asbestos Vulnerability in Indonesia Using Earthquake Vulnerability

Abstract

Objectives

This study aims to create Indonesia’s first asbestos exposure risk map by analyzing asbestos roofing prevalence, population density, and earthquake vulnerability. This represents a significant innovation by providing valuable insights to support the prevention of asbestos-related diseases in Indonesia.

Methods

This semi-quantitative study was conducted between June 2024 and September 2024. Data on asbestos roof usage and population density were obtained from the Indonesian Central Statistics Agency, while earthquake risk data were sourced from the Geological Disaster Mitigation Portal. Risk categorization was applied to each variable, and a matrix was developed to evaluate combined risks. Validation was conducted through cross-referencing, and maps were generated using Geographic Information System software.

Results

Nationally, asbestos roofing use is approximately 8.10%, although prevalence varies significantly among provinces. Bangka Belitung has the highest prevalence at 55.16%, followed by DKI Jakarta (52.10%), Riau Islands (31.99%), and Banten (21.22%). DKI Jakarta also has the highest population density, with 16 158 inhabitants per square kilometer. Combining these factors, DKI Jakarta emerges as the province with the highest asbestos exposure risk. Based on asbestos roofing prevalence, population density, and earthquake risk, provinces classified at very high asbestos exposure risk include DKI Jakarta, West Java, DI Yogyakarta, East Java, Banten, Bali, and West Nusa Tenggara. Provinces categorized as high-risk include North Sumatra, Bengkulu, Lampung, and Central Java.

Conclusions

This map supports the development of future public health policies by identifying high-risk areas, optimizing resource allocation, and informing targeted intervention strategies.

INTRODUCTION

Asbestos remains a significant public health concern due to its carcinogenic properties, particularly in regions where its use persists [1]. Indonesia, approximately 90% of raw asbestos is processed into asbestos cement products [2], which are commonly used for roofing due to affordability, durability, and resistance to heat and fire. However, asbestos roofs, especially in densely populated and long-term residential areas, pose serious health risks. Airborne asbestos fibers can cause devastating respiratory illnesses, including asbestosis, lung cancer, and mesothelioma—conditions with irreversible and often fatal outcomes [3,4].

According to the International Ban Asbestos Secretariat, as of 2024, 72 countries have banned asbestos use [5]. As asbestos usage declines in these countries, consumption has risen in nations that have yet to implement bans, thereby increasing exposure risks in those regions. Reports from countries with asbestos bans indicate persistent cases of mesothelioma and other asbestos-related diseases more than 20 years post-ban [6]. Unfortunately, Indonesia has largely overlooked asbestos-related diseases, with no official governmental prevalence data available. However, Suraya and Collegues [7–9]’s research has documented multiple cases, highlighting that asbestos-exposed workers face a three-fold increased risk of lung cancer compared to unexposed individuals.

Data from Indonesia’s Central Statistics Agency in 2023 show approximately 8.1% of houses nationwide use asbestos roofs, though distribution varies widely among provinces. In densely populated areas like Jakarta, asbestos roofs cover over 50% of residential buildings [10]. Despite this extensive use, significant protective measures against asbestos hazards remain lacking. Public awareness of asbestos-related environmental risks is low, often under the misconception that intact asbestos poses minimal immediate health threats due to low fiber release. Nevertheless, multiple factors can substantially elevate airborne asbestos fiber levels, including aging, decay, construction or renovation activities, demolition, fires, and damage from natural disasters [11,12].

The risk of asbestos exposure is particularly severe in densely populated areas, where closely packed buildings and living conditions amplify even minor fiber releases into significant health threats. High population density also leads to more frequent asbestos usage in construction and renovation, exacerbating risks. Historically, asbestos has been extensively employed in densely populated areas for roofing, insulation, and renovation projects. Combined with intense construction, transportation, and industrial activities typical of these regions, this creates a critical public health hazard. Continuous asbestos weathering and improper waste disposal persistently endanger these communities [4,13–15].

Another critical asbestos exposure factor is Indonesia’s location on the Pacific Ring of Fire, an area known for intense seismic activity, making many regions highly vulnerable to earthquakes and related disasters [16,17]. Earthquakes can cause significant damage to aging buildings containing asbestos, releasing hazardous fibers into the air depending on quake severity and building fragility [18,19]. Such releases have severe socioeconomic and health implications. For instance, evidence from the Great Hanshin Earthquake demonstrated asbestos fiber concentrations from damaged buildings significantly exceeding safe limits, highlighting the severe health risks posed by seismic events [20].

This study aimed to create an asbestos exposure risk map for Indonesia by integrating data on asbestos roof distribution, population density, and earthquake risk. The study prioritizes regions based on (1) asbestos roofing prevalence, (2) combined asbestos roofing use and population density, and (3) integrated asbestos roofing use, population density, and earthquake risks. By identifying areas where these risk factors overlap, the maps offer crucial guidance to government agencies, public health officials, and urban planners for targeted mitigation efforts. Additionally, these maps form a foundation for future policy development and improved protective measures against asbestos exposure.

METHODS

Study Design and Objectives

This semi-quantitative study was conducted between June 2024 and September 2024. The primary objective was to map the risk of asbestos exposure at the provincial level across Indonesia by analyzing 3 key factors: the prevalence of asbestos roof usage, population density, and earthquake potential.

Data Collection

Asbestos usage data were collected from the National Bureau of Statistics–Social Welfare Statistics (2023 Census data) [21], indicating annual asbestos consumption at the provincial level. These data were cross-checked with previous datasets to ensure consistency. Population density data were also obtained from the National Bureau of Statistics (2023 Census data) [21], with population per square kilometer recorded at the provincial level. Population density data were similarly cross-verified against earlier census information to confirm consistent growth trends, as higher population density could amplify exposure risks. Data on earthquake potential were obtained from the Indonesian Geological Disaster Mitigation Portal. Earthquake risk levels were assessed using the Modified Mercalli Intensity scale, an ordinal measure of earthquake intensity based on observed effects on people, structures, and the Earth’s surface [22]. These data were cross-referenced with records from the national disaster management agency on earthquake occurrences in Indonesia over recent years [23]. Areas prone to earthquakes could experience significant infrastructural damage, thereby increasing asbestos exposure risk due to degradation of building materials.

For inclusion in this study, provinces were required to have complete datasets for asbestos roof usage, population density, and earthquake potential. Provinces lacking any of these data points were excluded from the analysis.

Data Preprocessing and Preparation

Asbestos roof data from all Indonesian provinces were categorized into 5 risk levels to reflect actual asbestos usage and associated health hazards. Although no safe threshold for asbestos exposure has been established—since even minimal exposure carries cancer risks—greater asbestos prevalence correlates with increased danger. In the United States, a national survey indicated that approximately 18% of rental residential buildings contained sprayed or troweled on friable asbestos-containing material [24]. In Cartagena, Colombia, some neighborhoods had up to 47% of their total area covered by asbestos-cement roofs, with a city-wide average of around 20% [25]. In this mapping study, asbestos roof prevalence was classified as follows: below 1% usage was categorized as very low risk; 1–5% as low risk; >5–10% as medium risk; >10–20% as high risk; and over 20% as very high risk [4,26].

Population density categories were based both on idealized urban standards and the practical realities of Indonesian demographics. Indonesia’s population density varies dramatically—from as low as 12 to as high as 16 000 people per square kilometer—with a national average of 145.7. The optimal density, ensuring economic stability, land availability, and safety, largely depends on urban infrastructure and preparedness [15,27]. Thus, for this study, population density was categorized as follows: fewer than 50 people per square kilometer was very low; 51–100 was low; 101–150 was medium; 151–200 was high; and above 201 was very high.

Generation of the Asbestos Risk Classification

The asbestos exposure risk map categorizes areas into 5 distinct risk levels—very low, low, medium, high, and very high—using a vivid color gradient for clarity and ease of interpretation. The risk mapping comprised 3 layers of analysis: the initial layer assessed risk based solely on asbestos roof usage; the second combined asbestos roof prevalence with population density; and the third, most comprehensive layer integrated asbestos roof prevalence, population density, and earthquake potential. The first matrix assessed exposure risk by positioning asbestos roof usage levels on the vertical axis and population density on the horizontal axis. The second matrix further evaluated risk by integrating the combined asbestos roof usage and population density on the vertical axis, with earthquake potential on the horizontal axis. The risk categories were determined as follows: (1) very high risk when both variables fell into combinations of very high and very high, very high and high, very high and medium, or high and high; (2) high risk when combinations were medium and high, or very high and low; (3) medium risk for combinations of very high and very low, high and low, medium and low, medium and medium, or low and high; (4) low risk when combinations included very low and high, very low and medium, or low and medium; and (5) very low risk for combinations of low and very low, or very low and very low.

Geographic Information System for Map Development

The Geographic Information System (GIS) software utilized for map creation was ArcGIS version 10.8 (Esri, Redlands, CA, USA). The asbestos exposure risk maps were generated by inputting analyzed risk-level data, transforming raw data into clear visualizations that illustrate the scope and severity of asbestos exposure risk across Indonesian provinces.

Statistical Analysis

Descriptive statistical analysis was performed to summarize the distributions of asbestos roof usage, population density, and earthquake potential. Means, medians, and standard deviations (SDs) were calculated using SPSS version 30 (IBM Corp., Armonk, NY, USA).

Ethics Statement

The Ethical Committee of Binawan University stated that this study did not involve human participants or animals as research subjects; therefore, ethical clearance was not required.

RESULTS

Nationally, approximately 8.1% of houses in Indonesia have asbestos roofing, although prevalence varies significantly among provinces. In certain provinces, over half of all homes utilize asbestos roofs, while in others, asbestos usage is entirely absent. Bangka Belitung has the highest prevalence at 55.16%, followed closely by DKI Jakarta at 52.10%, Riau Islands at 31.99%, and Banten at 21.22%. Conversely, East Nusa Tenggara has the lowest usage, at just 0.13% (Figure 1).

Figure 1.

Prevalence of asbestos roof usage by province in Indonesia.

Population density in Indonesia varies widely, with a median±SD value of 101.0±2751.2 people per square kilometer. North Kalimantan has the lowest density, with only 10 people per square kilometer, while DKI Jakarta has an exceptionally high density of 16 158 people per square kilometer (Figure 2). Coupled with the fact that 52.1% of homes in DKI Jakarta use asbestos roofs, DKI Jakarta emerges as the province with the highest risk of asbestos exposure, highlighting the critical intersection of high asbestos usage and extreme population density.

Figure 2.

Population density by province in Indonesia.

The asbestos exposure risk map based on asbestos roof prevalence is shown in Figure 3.

Figure 3.

Asbestos risk map based on asbestos roof usage in Indonesia.

Considering both asbestos roof usage and population density, several provinces in Indonesia exhibit very high asbestos exposure risk, as depicted in Figure 4. These include North Sumatra, Lampung, Riau Islands, DKI Jakarta, West Java, Banten, Central Java, East Java, Bali, and West Nusa Tenggara. Provinces classified as high risk include Bangka Belitung Islands, DI Yogyakarta, and South Sulawesi. The remaining provinces fall into moderate, low, or very low-risk categories, reflecting significant disparities in exposure risk across Indonesia.

Figure 4.

Asbestos risk map based on asbestos roof usage and population density in Indonesia.

The asbestos exposure risk map incorporating asbestos roof prevalence, population density, and earthquake potential further expands upon the previous analysis. Considering these combined factors, provinces identified as very high risk include DKI Jakarta, West Java, DI Yogyakarta, East Java, Banten, Bali, and West Nusa Tenggara. Provinces at high risk are North Sumatra, Bengkulu, Lampung, and Central Java. The remaining provinces are categorized into moderate, low, and very low-risk levels. The comprehensive asbestos exposure risk map based on these combined factors is presented in Figure 5.

Figure 5.

Asbestos risk map based on asbestos roof usage, population density, and earthquake potential in Indonesia.

DISCUSSION

This study represents a significant initial step in evaluating asbestos exposure risks within Indonesian society after more than 7 decades of asbestos use. Findings reveal considerable disparities in asbestos exposure risks across Indonesian provinces. The national average±SD asbestos roof usage of 8.10±13.50 conceals marked regional variations, with provinces such as Bangka Belitung Islands (55.16%) and DKI Jakarta (52.10%) exhibiting exceptionally high usage rates, posing substantial exposure risks to local populations. High asbestos roofing usage is also documented in neighboring countries like Cambodia, Vietnam, and Thailand [28]. Most provinces with elevated asbestos usage are situated near asbestos roofing manufacturing industries [29]. This finding should prompt both central and local governments to urgently review and potentially revise policies concerning asbestos-containing materials and related industries in Indonesia.

Although the map provides a general overview, it supplies essential insights regarding asbestos exposure risks at the provincial level. By integrating asbestos roof usage, population density, and earthquake risk, the study highlights that exposure risk is influenced not only by usage levels but significantly amplified by population density and vulnerability to natural disasters. These factors substantially elevate community-level risks [14,20,30,31]. Therefore, the map serves as a critical baseline for preventive measures against asbestos exposure, particularly within disaster mitigation frameworks.

This map relies on secondary data sourced from the Indonesian Central Statistics Agency and the Indonesian Geological Disaster Mitigation Portal. Cross-verification with alternate sources found no significant inconsistencies [21–23,29]. References related to asbestos risk mapping using secondary data are limited. A similar initiative by the European Union identified vulnerable regions by integrating asbestos presence and seismic risk [32]. In Italy, maps detecting asbestos-cement covers were developed through direct measurements using advanced technology, such as the Multispectral Infrared and Visible Imaging Spectrometer sensor, which achieved approximately 80% accuracy in detecting asbestos-cement surfaces in the Italian Western Alps [31]. In Colombia, researchers combined direct measurements with secondary data to prioritize regions needing asbestos replacement, based on population density, facility density, and asbestos roof prevalence [13].

However, this study did not encompass other significant asbestos exposure sources, such as asbestos industries or asbestos-containing brake pads. Research has indicated that high population densities correlate with increased transportation activity, potentially raising exposure from asbestos-containing brake pads. An Iranian study found elevated airborne asbestos levels in areas with heavy traffic, adding another dimension of exposure for residents and workers [30,33]. Additionally, the proximity to asbestos industries, another critical exposure source, was not included. Data from Wittenoom, Australia, illustrate heightened lung cancer risk among residents, even those uninvolved in asbestos industries, demonstrating the pervasive dangers of asbestos exposure [34].

In addition to residents, construction workers involved in manufacturing, renovation, and demolition activities constitute another vulnerable group exposed to asbestos. This risk map could serve as a foundation for protective measures for these workers. Iranian research indicated airborne asbestos fiber concentrations during demolition surpassing safe limits, correlating with increased lung cancer risks for workers [6,35]. However, the study by Perkins et al. [36], conducted in Alaska, reported low airborne asbestos levels when wet demolition methods were employed, indicating that effective preventive measures could significantly mitigate airborne asbestos contamination.

Earthquake vulnerability significantly influences asbestos exposure risks, especially given Indonesia’s high seismic activity [23]. Earthquakes can severely damage structures containing asbestos, releasing fibers into the air, thus greatly increasing community exposure risks [37]. Seismic events can uniquely transform stable asbestos materials into airborne hazards, especially severe in densely populated, asbestos-rich regions. Furthermore, volunteers and emergency responders during disasters also risk asbestos exposure, expanding the affected population [20,38]. After earthquakes in various regions of Indonesia, alarming amounts of asbestos fragments have been found in the debris [39]. Similarly, a report regarding the February 6, 2023, earthquakes in Turkey emphasized the additional health threats posed by debris contaminated with asbestos [37].

This map is a powerful tool for increasing public awareness about asbestos-containing materials and their associated health hazards. It can guide public health policy-making and preventive strategies for asbestos-related diseases in Indonesia. Policymakers can leverage the map to identify high-risk areas, optimize resource allocation, and develop focused intervention strategies. Additionally, the map serves as a foundation for strengthening regulations, enhancing occupational safety, and educating the public about asbestos risks.

As Indonesia’s first asbestos risk map, it strongly underscores the urgency of addressing asbestos exposure threats—particularly in high-risk areas where immediate action is crucial. Indonesia should align national policies with global best practices to effectively protect public health. Future research should prioritize direct exposure assessments and long-term health monitoring to inform evidence-based policy-making. Additionally, creating more detailed district-level maps would significantly benefit local governments by accurately capturing variations in exposure risks within provinces.