Characterization of size-segregated PM down to UFP (PM0.1) and its trace and major elemental composition in blacksmith factories, Indonesia
0 Abstract
This study examines the size-segregated particulate matter (PM) concentrations in blacksmith workshops (WSs) in West Java, Indonesia, using the Ambient Nano Sampler (ANS). PM was categorized into six size fractions: > 10 µm, 10-2.5 µm, 2.5-1 µm, 1-0.5 µm, 0.5-0.1 µm, and < 0.1 µm (ultrafine particles, UFPs). The results showed that WSs with intensive welding activities had the highest ultrafine PM concentrations. WS-A recorded the highest total suspended particle (TSP) concentration (3,504.58 µg/m3), with UFPs contributing 998.27 µg/m3 (37% of PM2.5), while WS-B had the lowest TSP concentration (1,978.16 µg/m3). The ratio of PM < 0.1 to PM2.5 ranged from 0.26 to 0.42, demonstrating the dominance of UFPs in fine PM fractions. Outdoor UFP levels (4.64 µg/m3) were significantly lower than indoor concentrations, confirming that blacksmithing activities are a major emission source. Heavy metal analysis revealed iron (Fe) as the dominant element (up to 284.775 µg/m3 in UFPs), with Cr, Pb, and Mn also detected, highlighting occupational exposure risks. These findings indicate that blacksmith WSs generate significantly higher UFP and metal emissions than ambient environments, posing potential health hazards for workers. This study underscores the need for effective air quality management in small-scale metal industries.
Keywords
INTRODUCTION
Air pollution is a global environmental problem and a critical occupational health issue, posing significant risks across various industrial sectors[1-3]. One of the most harmful pollutants in occupational settings is particulate matter (PM), especially due to its chemical components, including polycyclic aromatic hydrocarbons (PAHs), heavy metals, and organic compounds[4-7]. Exposure to PM is associated with various adverse health effects and can even lead to premature mortality[8-10]. According to the European Environment Agency[11], prolonged exposure to PM has been indirectly linked to over 417,000 premature deaths in Europe.
PM varies in size, typically ranging from 10 nm to 100 µm. The smaller the particles, the greater their potential to harm the human body, as their health impacts are size-dependent. Smaller particles, such as fine particles (PM2.5), microparticles (PM1), and ultrafine particles (UFPs, < 0.1 µm), pose higher risks due to their ability to penetrate deep into the respiratory system and enter systemic circulation[12-14].
Most occupational PM studies focus on PM4, also known as respirable particles[15-18]. However, industrial processes often emit large quantities of UFPs, which are more harmful because they can reach the alveolar region, dissolve into the bloodstream, and potentially deposit in the brain[19-21]. UFPs originate from both natural and anthropogenic sources but are primarily associated with human activities such as combustion and high-temperature processing[19,22]. In urban environments, UFPs are mainly emitted from vehicle exhaust and are referred to as “fresh emissions” because they have not yet coagulated into larger particles[22-25]. Like other PM types, UFPs often carry harmful chemicals, including heavy metals and PAHs, making them a major concern in industrial settings[26-28].
The ironworking industry is a significant source of PM emissions. Processes such as welding, cutting, forging, and grinding release substantial quantities of PM, including UFPs. Previous studies have reported high concentrations of PM4 and PM2.5 in indoor environments within such factories, linking these exposures to respiratory and cardiovascular diseases, oxidative stress, and increased cancer risk[29-33]. Moreover, heavy metals such as iron (Fe), chromium (Cr), and manganese (Mn) further elevate these health risks due to their toxicological properties[34-39].
In Indonesia, traditional blacksmith workshops (WSs) are widely found, particularly in rural areas of Java. These WSs typically use medium-carbon steel derived from scrap materials such as automotive leaf springs and railway tracks. Production activities, including metal cutting, forging, grinding, and welding, generate high levels of PM and UFPs, exposing workers to potentially hazardous pollutants. Previous studies have shown that PM concentrations in blacksmith environments often exceed health-based thresholds. For example, exposure to crystalline silica in PM4 has been associated with respiratory disorders and pneumoconiosis among workers[40]. However, such studies have generally focused on coarse or respirable PM, with limited attention to size-segregated PM down to UFPs or their detailed chemical composition, including heavy metals. This gap underscores the need for a more comprehensive analysis of finer PM fractions, which may carry greater health risks due to deeper lung penetration and higher toxic surface loading.
Given the toxic potential of UFPs, driven by their large surface area-to-mass ratio and oxidative reactivity, a detailed investigation into PM size segregation and UFP composition in blacksmith WSs is essential. Moreover, understanding the health risks associated with exposure to UFPs and toxic metals in these settings is crucial for developing appropriate risk mitigation strategies. To our knowledge, no prior study in Indonesia has comprehensively quantified UFP concentrations or analyzed their heavy metal content in blacksmith WSs. This is particularly important, as UFPs constitute a significant fraction of inhaled PM but are not currently regulated under most occupational exposure standards.
Therefore, this study aims to fill that gap by characterizing size-segregated PM, with a particular focus on UFPs, and analyzing their associated metal content in traditional blacksmith WSs. The objectives are to quantify PM concentrations across six size fractions, including UFPs, and to assess the levels of heavy metals such as Fe, Cr, Mn, and Pb - elements commonly associated with metalworking activities.
EXPERIMENTAL
Study area
The study was conducted in Desa Mekarmaju, located in Kecamatan Pasirjambu, Kabupaten Bandung, West Java, Indonesia. This village is recognized as a traditional blacksmithing hub, housing 205 blacksmith WSs that produce knives and agricultural tools. These WSs form the primary economic activity for the majority of the residents. Desa Mekarmaju is situated at an altitude of 1,000 to 1,200 meters above sea level and is surrounded by rice fields, forest reserves, and production forests.
The village spans an area of approximately 140 hectares, encompassing residential zones, agricultural land, and forested areas. It is divided into four dusun (hamlets), further subdivided into 14 Rukun Warga (RW) and 47 Rukun Tetangga (RT), with a total population of 8,902 people and 2,883 households. Among the 10 active RWs, blacksmith WSs employ a total of 569 workers, with each WS typically operated by families. Men primarily engage in metal forging and grinding tasks, while women and children contribute to ancillary activities such as crafting knife sheaths.
The blacksmithing processes, such as grinding, forging, metal cutting, and welding, possibly generate significant PM emissions. To ensure representativeness, five blacksmith WSs were selected based on their high production capacity, number of workers, and inclusion of all major blacksmithing activities (grinding, forging, welding, initial cutting, and finishing). These WSs are among the largest and most active in the village, and their layout, working conditions, and production methods are typical of traditional blacksmithing practices in the region. The details of the study area and five-WS layout are shown in Figure 1 and Table 1.
Figure 1. Location of the sampling site in Mekarmaju Village, Bandung Regency, West Java, Indonesia. Base map sources: Google Maps and Google Satellite imagery (accessed via QGIS). Map compiled using QGIS software. WS: Workshop.
Existing conditions of blacksmith WSs
| Aspect | WS-A | WS-B | WS-C | WS-D | WS-E |
| Number of workers (persons) | 16 | 16 | 9 | 7 | 7 |
| Production capacity (units/day) | 700-1,000 | 200-300 | 500-600 | 80-100 | 250 |
| Types of products | Hoes, crowbars, chisels, mattocks, forks, machetes | Machetes | Hoes, mattocks | Forks, machetes | Hoes, crowbars, forks, mattocks |
| WS area | ± 500 m2 | ± 280 m2 | ± 250 m2 | ± 140 m2 | ± 182 m2 |
| Facilities | Cutting machine, welding machine, grinder, forging machine | Cutting machine, welding machine, grinder, forging machine | Cutting machine, welding machine, grinder, forging machine | Cutting machine, welding machine, grinder, forging machine | Cutting machine, welding machine, grinder, forging machine |
| Environmental conditions | Adequate ventilation, good lighting | Poor ventilation, crowded workspace | Sufficient ventilation, good lighting | Poor ventilation, good lighting | Adequate ventilation, poor lighting |
Size-segregated PM sampling
Size-segregated PM sampling was conducted using the Ambient Nano Sampler (ANS), a cascade air sampler capable of collecting particles across six size fractions: > 10 µm, 10-2.5 µm, 2.5-1 µm, 1-0.5 µm, 0.5-0.1 µm, and < 0.1 µm[41-42]. The sampler was placed at the center of each WS to capture emissions from all surrounding activities, including grinding, forging, metal cutting, and welding. This placement ensured that the measurements represented overall PM emissions during active production processes.
Five blacksmith WSs were selected based on the following criteria: (1) complete production processes from raw material to finished products; (2) high production capacity; and (3) employment of more than five workers. Each WS was sampled 3 times. PM was collected using polytetrafluoroethylene (PTFE) filters (55 mm diameter), which were preconditioned in a desiccator for 24 h before and after sampling to ensure mass stability.
The ANS includes four impactor stages equipped with PTFE filters for collecting particles in the size ranges of > 10 µm, 10-2.5 µm, 2.5-1 µm, and 1-0.5 µm, arranged from top to bottom. The fifth stage uses an inertial filter (IF) made of SUS304 stainless steel (fiber diameter = 9.8 µm) to capture particles in the 0.5-0.1 µm range, while a backup filter collects particles smaller than 0.1 µm (UFP). The flow rate was maintained at 40 L/min throughout sampling.
Due to the high concentration of airborne particles, the sampling duration was limited to 2 h to prevent particle overload and excess pressure drop, which could reduce flow rate and affect the size-selective cut-off of the impactor.
For quality assurance and quality control (QA/QC), we included five travel blank filters, each consisting of five PTFE filters and one inertial filter, to detect any contamination during transport and handling. These blanks underwent the same preconditioning, transport, and handling procedures as the sampling filters but were not exposed to airflow, ensuring the accuracy of both PM mass and chemical analysis results.
Trace and major elemental analysis
The heavy metal analysis of PM was performed using Inductively Coupled Plasma Mass Spectrometry (ICP-MS), a highly sensitive and accurate technique for trace element quantification. This method enables the simultaneous detection of multiple elements at low ppb concentrations. The procedure followed the US EPA Compendium Method IO-3.5: Determination of Metals in Ambient PMr using ICP-MS.
For this study, the PTFE and IFs containing size-segregated PM samples were digested using a mixture of nitric acid (HNO3) and hydrofluoric acid (HF) in a closed-vessel microwave digestion system, ensuring complete dissolution of the collected particles. The resulting solutions were analyzed using an Agilent 7900 ICP-MS, which ionizes the samples and measures the mass-to-charge ratio of the resulting ions to determine the concentrations of the target metals.
The analysis focused on metals commonly linked to anthropogenic activities, including Fe, Cr, Mn, and other trace elements. These metals are of particular concern due to their toxicological relevance and potential to cause respiratory, neurological, and cardiovascular health effects upon inhalation exposure. The measured concentrations were used to assess the chemical composition of each PM size fraction, offering critical insight into the emissions profile of blacksmith WSs.
To ensure measurement accuracy, travel blank filters were also analyzed using the same ICP-MS method. The resulting blank concentrations were subtracted from the corresponding sample values to correct for any background contamination during filter handling or transport. All analyses were performed at PT Sky Pacific Indonesia, a certified analytical laboratory located in Bogor, West Java, Indonesia.
RESULTS AND DISCUSSION
Concentration of size-segregated PMs down to UFP
The concentration of PM across different size fractions in the five blacksmith WSs is presented in Figures 2 and 3. Figure 2 illustrates the mass concentrations of size-segregated PMs in each WS, while Figure 3 shows the percentage contribution of each fraction to TSP. Table 2 provides a summary of PM concentrations and their respective ratios for each WS.
Figure 2. PM concentration (µg/m3) across different WSs (WS-A to WS-E) and the outdoor environment. The PM concentrations are categorized based on particle size ranges (< 0.1 µm, 0.1-0.5 µm, 0.5-1.0 µm, 1.0-2.5 µm, 2.5-10 µm, and > 10 µm). Error bars indicate the variability of PM levels. WS-A[NA] and WS-C[NA] represent periods with no activities in the respective WSs, providing a comparison of PM concentrations during operational and non-operational conditions. PM: Particulate matter; WS: workshop.
Figure 3. Stacked bar chart showing the distribution of PM size fractions across different blacksmith WS locations. The PM size categories include < 0.1 µm, 0.1-0.5 µm, 0.5-1.0 µm, 1.0-2.5 µm, 2.5-10 µm, and > 10 µm. Each bar represents the contribution of different PM size ranges to the total PM concentration at each location. Variations in particle size distribution indicate differences in emission sources and WS activities. WS-A[NA] and WS-C[NA] represent periods with no activities in the respective WSs, providing a comparison of PM concentrations during operational and non-operational conditions. PM: Particulate matter; WS: workshop.
Size-segregated concentrations of PMs and their ratios in all WSs
| PM | WS-A | WS-B | WS-C | WS-D | WS-E |
| Size-segregated (µg/m3) | |||||
| < 0.1 µm | 998.27 | 487.86 | 239.58 | 445.20 | 1,184.59 |
| 0.1-0.5 µm | 584.55 | 253.53 | 281.99 | 863.65 | 792.68 |
| 0.5-1.0 µm | 606.53 | 226.38 | 245.43 | 370.86 | 444.78 |
| 1.0-2.5 µm | 404.21 | 136.95 | 135.11 | 161.40 | 204.23 |
| 2.5-10 µm | 525.14 | 341.74 | 774.95 | 186.94 | 364.43 |
| > 10 µm | 385.87 | 531.72 | 1,244.44 | 96.82 | 260.91 |
| PM concentration (µg/m3) | |||||
| TSP | 3,504.58 | 1,978.16 | 2,921.51 | 2,124.88 | 3,251.61 |
| PM10 | 3,118.71 | 1,446.45 | 1,677.07 | 2,028.06 | 2,990.70 |
| PM2.5 | 2,593.56 | 1,104.71 | 902.12 | 1,841.11 | 2,626.28 |
| PM1 | 2,189.36 | 967.76 | 767.01 | 1,679.72 | 2,422.05 |
| PM ratio | |||||
| PM0.1/TSP | 0.26 | 0.25 | 0.10 | 0.30 | 0.27 |
| PM0.1/PM10 | 0.30 | 0.33 | 0.16 | 0.31 | 0.30 |
| PM0.1/PM2.5 | 0.37 | 0.42 | 0.26 | 0.34 | 0.38 |
| PM0.1/PM1 | 0.44 | 0.48 | 0.31 | 0.37 | 0.42 |
| PM1/TSP | 0.59 | 0.51 | 0.35 | 0.80 | 0.60 |
| PM1/PM10 | 0.68 | 0.67 | 0.53 | 0.85 | 0.69 |
| PM1/PM2.5 | 0.85 | 0.88 | 0.86 | 0.92 | 0.88 |
| PM2.5/TSP | 0.69 | 0.57 | 0.40 | 0.87 | 0.68 |
| PM2.5/PM10 | 0.80 | 0.76 | 0.60 | 0.91 | 0.78 |
| PM10/TSP | 0.86 | 0.75 | 0.64 | 0.95 | 0.85 |
Among the 5 WSs, WS-A exhibited the highest TSP concentration (3,504.58 µg/m3), with PM0.1 contributing 998.27 µg/m3 (37% of PM2.5 and 44% of PM1). This WS produces multiple metal products, including hoes, crowbars, chisels, mattocks, forks, and machetes, and has the largest production capacity (700-1,000 units/day) with 16 workers. The high concentration of UFP can be attributed to intensive welding activities, which generate hot metal vapors that rapidly condense into fine and ultrafine particles. Additionally, the PM1/TSP ratio (0.59) and PM2.5/TSP ratio (0.69) suggest that the majority of airborne particles in this WS are in the fine fraction, posing a significant health risk to workers. Despite having adequate ventilation, the high PM levels indicate a need for improved control measures.
In contrast, WS-C, which had better ventilation efficiency than other WSs, recorded a lower TSP concentration (2,921.51 µg/m3) but a higher proportion of coarse particles (> 10 µm, 1,244.44 µg/m3). The PM0.1 to TSP ratio was only 0.10, significantly lower than in other WSs, indicating that fine and ultrafine particles were less dominant. This WS specializes in the production of hoes and mattocks (500-600 units/day), with nine workers and a WS size of 250 m2. The high percentage of coarse particles suggests that grinding activities, which primarily generate larger particles, were the dominant source of emissions. These findings suggest that the combination of adequate ventilation and a predominance of coarse PM emissions helped reduce fine particle accumulation.
WS-B recorded the lowest TSP concentration (1,978.16 µg/m3), with PM0.1 accounting for 487.86 µg/m3. The PM2.5/TSP ratio (0.57) suggests a slightly higher presence of coarser particles. This WS mainly produces machetes (200-300 units/day), with 16 workers in a smaller area (280 m2) and poor ventilation. Despite the relatively low PM concentrations, the WS’s crowded working conditions and limited ventilation could increase worker exposure to airborne pollutants. The PM0.1/PM1 ratio (0.48) suggests that welding activities were less intensive compared to WS-A, which may explain the lower fraction of UFPs.
WS-D exhibited a more balanced size distribution, with 0.1-0.5 µm and < 0.1 µm particles as the dominant fractions. The PM0.1 concentration was 445.20 µg/m³, and the PM1/TSP ratio (0.80) was the highest among all WSs, indicating that most airborne particles were in the fine mode. This WS specializes in forging forks and machetes (80-100 units/day) and has the smallest number of workers (7) and the smallest WS area (140 m2). The PM10/TSP ratio (0.95) indicates that nearly all PMs were within the respirable range, highlighting a significant occupational health risk. The combination of limited ventilation and high metal-processing activities contributes to the observed PM distribution. This suggests that a mix of forging and welding processes contributed to the high levels of fine PM.
On the other hand, WS-E recorded one of the highest PM0.1 concentrations (1,184.59 µg/m3) and a TSP of 3,251.61 µg/m3, with a PM0.1/TSP ratio of 0.27, highlighting the significant contribution of ultrafine particles. The PM1/TSP ratio (0.60) suggests a dominance of fine PM, similar to WS-A. This WS produces hoes, crowbars, forks, and mattocks (250 units/day) and has seven workers. The WS has adequate ventilation but poor lighting, which may influence the dispersion of fine particles. The intense welding activities likely contributed to the elevated UFP concentrations, similar to WS-A.
Outdoor air sampling near the WSs showed a PM0.1-0.5 concentration of 36.23 µg/m3, with fine particles accounting for over 50% of the total PM. This suggests that emissions from blacksmithing activities influence the ambient air quality in the surrounding area.
The PM2.5 concentrations measured in all five blacksmith WSs far exceeded the health-based exposure guidelines set by international and national agencies. According to the World Health Organization (WHO) air quality guidelines (2021)[14], the recommended 24-h average PM2.5 concentration should not exceed 15 µg/m3. Even though this guideline is primarily for ambient exposure, it highlights the extreme levels observed in occupational settings. Additionally, the Occupational Safety and Health Administration (OSHA) in the United States sets a Permissible Exposure Limit (PEL) for respirable dust of 5 mg/m3 (5,000 µg/m3) over an 8-h time-weighted average, which is more suitable for occupational settings. The Indonesian Ministry of Manpower also sets a similar threshold of 3 mg/m3 (3,000 µg/m3) for respirable PM in workplaces. In this study, PM2.5 concentrations ranged from 902.12 µg/m3 (WS-C) to 2,626.28 µg/m3 (WS-E). While these values were still below the OSHA and Indonesian PELs, they are considerably higher than the WHO’s health-protective guidelines and signal a substantial exposure risk, especially under prolonged and repeated exposure scenarios.
Statistical analyses were conducted to compare PM concentrations among five WSs for six particle size fractions. The Shapiro-Wilk test showed mostly normal distributions, while Levene’s test confirmed homogeneity of variances across groups. Due to some non-normality, the Kruskal-Wallis test was used. Results indicated no statistically significant differences in PM concentrations between WSs for all particle size fractions (P > 0.05). This suggests that the PM exposure levels were comparable across WS environments during the monitoring period. Full p-values are summarized in Supplementary Table 1 and visualized in Supplementary Figure 1.
UFP comparison with previous studies
Although no previous studies in Indonesia have investigated UFP concentrations in indoor occupational environments such as blacksmith WSs, the results of this study provide an important initial reference for understanding UFP exposure levels in small-scale metal industries. This information helps contextualize the magnitude of UFP emissions in such workplaces relative to what has been reported in other environments.
In this study, UFPs constituted a significant portion of the PM. The ratio of UFP to PM2.5 ranged between 0.26 and 0.42, and the ratio of UFP to TSP ranged from 0.10 to 0.30, indicating that UFPs made up a substantial fraction of the inhalable particles in these settings. Such high proportions highlight the potential health risks posed by UFPs, as they have the ability to penetrate deep into the respiratory system and enter systemic circulation.
UFP concentrations observed in the blacksmith WSs (239.58-1,184.59 µg/m3) were significantly higher than those reported in previous ambient air studies in Indonesia. For example, studies in urban and residential sites in Jambi showed values ranging from 6.7 to 29.2 µg/m3[43]. Roadside and school environments in Medan recorded concentrations of 9.3-17.0 µg/m3 and 14.3-17.5 µg/m3, respectively[44]. Although these environments differ in function and source, the stark contrast in UFP concentrations underscores the intense emissions generated by metal processing activities, such as welding, forging, and grinding in blacksmith WSs.
Industrial studies outside Indonesia, such as those in Japan’s titanium dioxide manufacturing plants, reported UFP concentrations ranging from 0.7 to 31.3 µg/m3[31], which are still substantially lower than those in our study. These comparisons, while not directly equivalent, provide useful context and underscore the urgent need for UFP monitoring and mitigation strategies in informal industrial settings.
Outdoor measurements near the WSs revealed UFP concentrations of 4.64 µg/m3, aligning more closely with typical urban ambient values in Indonesia. The sharp difference between indoor and outdoor concentrations highlights the impact of WS-specific emissions and limited ventilation. These findings emphasize the critical need for improved emission control and ventilation strategies in traditional industries. Furthermore, they support the case for expanding future UFP research into other indoor environments such as homes, kitchens, schools, and restaurants, where people spend a significant portion of their time.
Trace and major element concentration in the PM
The elemental composition of each size-segregated PM fraction is summarized in Table 3. Among the detected metals, Fe was the dominant element, with concentrations reaching up to 330 µg/m3 in UFP. The high levels of Fe are attributable to the nature of blacksmithing processes, where iron and steel are the primary materials. All major activities in the WSs, including initial cutting, grinding, welding, and forging, contributed to the emission of Fe-rich particles, particularly in the fine and ultrafine size ranges. This study aligns with the previous study[45], which found that Fe is the predominant element component in the PM generated during the welding process[46].
Trace and elemental components of each size-segregated PM in all WSs
| Components | WS-A (µg/m3) | |||||
| < 0.1 µm | 0.1-0.5 µm | 0.5-1.0 µm | 1.0-2.5 µm | 2.5-10 µm | > 10 µm | |
| As | 0.2 | 0.02 | 0.04 | 0.01 | 0.01 | 0.0040 |
| Cr | 0.1 | 0.002 | 0.08 | 0.02 | 0.03 | 0.02 |
| Cu | 1 | 0.004 | 0.4 | 0.004 | 0.004 | 0.004 |
| Pb | 0.4 | 0.06 | 0.4 | 0.09 | 0.07 | 0.030 |
| Zn | 0.5 | 0.004 | 0.5 | 0.004 | 0.01 | 0.004 |
| Si | 14 | 3 | 21 | 3 | 4 | 0.004 |
| Al | 24 | 1 | 9 | 9 | 8 | 5.00 |
| Fe | 154 | 0.04 | 119 | 49 | 51 | 36 |
| Mn | 12 | 0 | 14 | 2 | 0.0003 | 1.0000 |
| Ni | 0.1 | 0.002 | 0.04 | 0.02 | 0.020 | 0.020 |
| Ca | 28 | 0.4 | 13 | 13 | 0.4 | 6 |
| Mg | 6 | 0.4 | 3 | 3 | 2 | 2 |
| K | 21 | 2 | 19 | 3 | 0.4 | 0.4 |
| Ti | 7 | 0.5 | 4 | 2 | 2 | 2 |
| Components | WS-B (µg/m3) | |||||
| < 0.1 µm | 0.1-0.5 µm | 0.5-1.0 µm | 1.0-2.5 µm | 2.5-10 µm | > 10 µm | |
| As | 0.2 | 0.01 | 0.04 | 0.03 | 0.04 | 0.05 |
| Cr | 0.7 | 0.002 | 0.3 | 0.4 | 0.5 | 1 |
| Cu | 1 | 0.004 | 0.1 | 0.004 | 0.004 | 0.1 |
| Pb | 0.06 | 0.004 | 0.1 | 0.02 | 0.004 | 0.01 |
| Zn | 0.2 | 0.004 | 0.004 | 0.4 | 0.1 | 0.004 |
| Si | 0.004 | 0.004 | 1 | 0.004 | 0.5 | 0.004 |
| Al | 9 | 0.1 | 3 | 3 | 2 | 6 |
| Fe | 108 | 0.04 | 76 | 77 | 80 | 165 |
| Mn | 4 | 0.0003 | 3 | 2 | 0.0003 | 1 |
| Ni | 0.5 | 0.002 | 0.2 | 0.2 | 0.2 | 0.2 |
| Ca | 11 | 0.4 | 5 | 3 | 0.4 | 6 |
| Mg | 2 | 0.4 | 1 | 1 | 0.4 | 2 |
| K | 4 | 0.4 | 3 | 0.4 | 0.4 | 0.4 |
| Ti | 3 | 0.02 | 1 | 0.6 | 0.4 | 1 |
| Components | WS-C (µg/m3) | |||||
| < 0.1 µm | 0.1-0.5 µm | 0.5-1.0 µm | 1.0-2.5 µm | 2.5-10 µm | > 10 µm | |
| As | 0.04 | 0.02 | 0.03 | 0.01 | 0.01 | 0.0004 |
| Cr | 0.03 | 0.002 | 0.08 | 0.05 | 0.05 | 0.06 |
| Cu | 0.004 | 0.004 | 0.004 | 0.004 | 0.5 | 3 |
| Pb | 0.1 | 0.08 | 0.05 | 0.1 | 0.3 | 0.2 |
| Zn | 0.1 | 0.004 | 0.4 | 0.3 | 0.004 | 0.5 |
| Si | 0.004 | 2 | 2 | 0.004 | 11 | 15 |
| Al | 1 | 0.3 | 3 | 3 | 10 | 6 |
| Fe | 8 | 0.04 | 42 | 18 | 50 | 60 |
| Mn | 4 | 1 | 6 | 5 | 0.0003 | 3 |
| Ni | 0.06 | 0.004 | 0.004 | 0.05 | 0.002 | 0.002 |
| Ca | 7 | 0.4 | 11 | 12 | 0.5 | 51 |
| Mg | 11 | 1 | 16 | 17 | 69 | 84 |
| K | 7 | 2 | 7 | 5 | 0.4 | 4 |
| Ti | 0.8 | 0.3 | 2 | 1 | 6 | 7 |
| Components | WS-D (µg/m3) | |||||
| < 0.1 µm | 0.1-0.5 µm | 0.5-1.0 µm | 1.0-2.5 µm | 2.5-10 µm | > 10 µm | |
| As | 0.08 | 0.03 | 0.04 | 0.009 | 0.0004 | 0.007 |
| Cr | 0.09 | 0.002 | 0.1 | 0.08 | 0.06 | 0.06 |
| Cu | 0.03 | 0.004 | 0.004 | 0.004 | 0.004 | 0.004 |
| Pb | 0.1 | 0.07 | 0.1 | 0.02 | 0.01 | 0.0004 |
| Zn | 5 | 0.004 | 0.5 | 0.004 | 0.004 | 0.004 |
| Si | 0.004 | 9 | 7 | 0.004 | 0.004 | 0.004 |
| Al | 2 | 3 | 0.9 | 1 | 0.4 | 0.2 |
| Fe | 76 | 0.04 | 109 | 109 | 83 | 69 |
| Mn | 6 | 3 | 11 | 0.9 | 0.0003 | 0.4 |
| Ni | 0.06 | 0.002 | 0.06 | 0.05 | 0.002 | 0.04 |
| Ca | 10 | 9 | 12 | 6 | 0.4 | 2 |
| Mg | 10 | 4 | 5 | 3 | 2 | 1 |
| K | 29 | 89 | 67 | 0.4 | 0.4 | 0.4 |
| Ti | 3 | 2 | 3 | 1 | 0.7 | 0.8 |
| Components | WS-E (µg/m3) | |||||
| < 0.1 µm | 0.1-0.5 µm | 0.5-1.0 µm | 1.0-2.5 µm | 2.5-10 µm | > 10 µm | |
| As | 0.3 | 0.09 | 0.05 | 0.02 | 0.0004 | 0.001 |
| Cr | 0.4 | 0.002 | 0.1 | 0.07 | 0.04 | 0.02 |
| Cu | 0.5 | 0.06 | 0.5 | 0.004 | 0.004 | 0.03 |
| Pb | 1 | 0.4 | 0.4 | 0.1 | 0.08 | 0.01 |
| Zn | 64 | 29 | 38 | 8 | 1 | 0.6 |
| Si | 112 | 44 | 40 | 9 | 6 | 2 |
| Al | 6 | 4 | 1 | 2 | 2 | 0.04 |
| Fe | 330 | 0.04 | 136 | 52 | 38 | 16 |
| Mn | 59 | 21 | 22 | 6 | 0.0003 | 0.0003 |
| Ni | 0.3 | 0.002 | 0.03 | 0.07 | 0.01 | 0.01 |
| Ca | 38 | 4 | 11 | 11 | 0.4 | 5 |
| Mg | 53 | 6 | 17 | 19 | 26 | 7 |
| K | 98 | 37 | 28 | 7 | 0.4 | 0.4 |
| Ti | 12 | 4 | 5 | 4 | 5 | 2 |
During mechanical cutting, fine Fe particles are directly released into the air. Grinding generates iron metal aerosols that remain suspended, while forging at high temperatures leads to the evaporation and subsequent condensation of Fe into fine particles. Welding further contributes to Fe emissions through the release of metal fumes containing Fe[47]. Additionally, residual dust from these processes often accumulates on floors and walls, contributing to the re-suspension of Fe particles in the air, which elevates Fe levels in the coarser particles (2.5-10 and > 10 µm).
Aside from Fe, other notable elements detected include Si and K. Si was present due to its role as a component of steel, even though in smaller amounts compared to Fe[48]. Si enhances the mechanical strength of steel and improves its resistance to oxidation and corrosion at high temperatures. This aligns with previous findings that Si is a minor but significant element in steel working environments[49]. K was attributed to the use of charcoal during forging activities, consistent with previous findings linking high K concentrations to combustion sources in metalworking.
Furthermore, regardless of the WS, the current study also revealed the presence of toxic metals such as Cr, Pb, and Mn. Although these elements were detected at lower concentrations compared to Fe, their toxicological relevance is significant - particularly Cr, which is classified as a Group 1 carcinogen in its hexavalent form (Cr6+) and is associated with both respiratory and dermal health effects[50-53]. Pb is also a highly harmful substance due to its potent neurotoxicity, especially with long-term occupational exposure, and has been linked to cardiovascular disease and kidney dysfunction[51,54,55]. Mn poses a serious health risk as well, as excessive exposure can lead to neurotoxicity and symptoms resembling Parkinson’s disease, particularly in polluted environments[56,57]. These metals were detected even in smaller particle size fractions in this study, which are respirable and capable of penetrating deep into the respiratory system - raising concerns about chronic exposure and cumulative health risks for workers.
While these findings provide critical insights into size-segregated PM and elemental exposure in blacksmith environments, several limitations should be acknowledged to support appropriate interpretation and guide future research directions. First, the study relied solely on area sampling using the ANS, which was placed at the center of each workshop to represent general indoor air quality. Although workers remained mostly stationary during their tasks, this approach does not capture personal exposure variations based on proximity, activity type, or duration. As such, individual-level exposure could be affected by specific work processes, movements, and protective practices, which could not be directly assessed. Additionally, the current study did not focus on exposure variation by activity cycle due to limitations in the number of samplers available. Future studies should employ personal samplers positioned close to workers engaged in different blacksmithing tasks to provide a more accurate and task-specific exposure profile.
Second, the sampling duration in this study was limited to approximately 2 h per WS. This duration was chosen based on prior trials, which revealed that extending sampling times under high PM concentrations led to significant pressure drops, reducing the flow rate from 40 L/min to around 35 L/min. Such a drop could compromise the impactor's cut-off size accuracy. Therefore, we selected a 2-h sampling window to ensure consistent and reliable data collection. However, this short duration may not fully capture the temporal variability of PM emissions throughout the entire workday.
Third, while the study successfully characterized the concentrations of selected elements (e.g., Fe, Cr, Mn, Pb) in PM, it did not include the analysis of organic pollutants, such as PAHs, which are known to be generated during high-temperature metal processing and could pose significant health risks. Future research should incorporate chemical analysis of PAHs and other toxic components to provide a more comprehensive health risk assessment for blacksmith workers.
Lastly, this study qualitatively described the ventilation conditions in each WS (e.g., “adequate” or “better” ventilation) based on observational criteria. However, quantitative data such as air exchange rates, airflow patterns, or room volume were not measured. This limitation originates from the semi-open and highly variable design of blacksmith WSs in rural settings, which lack standardized ventilation infrastructure. Future research should incorporate direct ventilation measurements to better understand the influence of airflow on PM distribution and worker exposure.
CONCLUSIONS
This study highlights the substantial occupational exposure to fine and ultrafine PM in blacksmith WSs in West Java, Indonesia. UFPs, primarily generated by welding and forging processes, were found to dominate the indoor PM profile, significantly exceeding outdoor levels. WSs with more intense metalworking activities exhibited particularly high UFP concentrations, indicating that emissions stem predominantly from these operations.
In addition to elevated particle concentrations, the presence of heavy metals such as Fe, Cr, Mn, and lead (Pb) in the PM fractions raises serious concerns about long-term respiratory and systemic health risks for workers. The levels observed were markedly higher than those reported in typical ambient settings, underscoring the severity of exposure in these confined occupational environments.
These findings emphasize the urgent need for targeted interventions to reduce exposure and improve workplace safety. Recommended control strategies include the installation of local exhaust ventilation systems, transitioning from charcoal combustion to cleaner heat sources, and the implementation of work rotation schedules to limit cumulative exposure. Providing workers with proper respiratory protection and regular training on occupational health risks is equally essential. Beyond individual interventions, routine air quality assessments and policy support are vital for long-term occupational health management in small-scale industries.
DECLARATIONS
Acknowledgments
The author would like to thank the head and staff of Mekarmaju village, as well as the owner of the blacksmith industry WS, who served as the research subject. The author also acknowledges the Atmospheric Environment and Pollution Control Engineering Laboratory, Kanazawa University, for lending the Ambient Nano Sampler, and the Indonesia Education Scholarship (Beasiswa Pendidikan Indonesia), the Center for Higher Education Funding and Assessment (PPAPT), Indonesia Endowment Fund for Education (LPDP) from Ministry of Higher Education, Science, and Technology of Republic Indonesia (Kemendikbudristek) for providing the Ph.D. scholarship for the first author.
Authors’ contributions
Conceptualization: Regia, R. A.; Suharyanto; Amin, M.; Oginawati, K.
Methodology: Regia, R. A.; Suharyanto; Amin, M.; Santoso, M.; Oginawati, K.
Investigation: Regia, R. A.
Resources: Suharyanto; Amin, M.; Santoso, M.; Oginawati, K.
Data curation: Regia, R. A.; Oginawati, K.; Santoso, M.; Amin, M.
Writing - original draft preparation: Regia, R. A.
Writing - review and editing: Suharyanto; Amin, M.; Soemarko, D. S.; Oginawati, K.
Supervision: Suharyanto, Soemarko, D. S.; Amin, M.; Oginawati, K.
Project administration: Suharyanto; Oginawati, K.
Funding acquisition: Suharyanto; Oginawati, K.
All authors have read and agreed to the published version of the manuscript.
Availability of data and materials
The data that support the findings of this study are available from the corresponding author upon reasonable request. The data are not publicly available because they contain information that could compromise the privacy of research participants.
Financial support and sponsorship
This research was financially supported by the P2MI Program, Institut Teknologi Bandung, Indonesia Education Scholarship (Beasiswa Pendidikan Indonesia), the Center for Higher Education Funding and Assessment (PPAPT), and Indonesia Endowment Fund for Education (LPDP) from the Ministry of Higher Education, Science, and Technology of the Republic of Indonesia (Kemendikbudristek).
Conflicts of interest
All authors declared that there are no conflicts of interest.
Ethical approval and consent to participate
This study was approved by the Research Ethics Committee of Universitas Padjadjaran, Bandung, Indonesia, under approval number 1151/UN6.KEP/EC/2024, issued on 01 November 2024. The ethical clearance is valid for one year from the issuance date. All ethical considerations were strictly followed to ensure the protection of participants' rights and welfare. Informed consent was obtained through a two-way agreement involving verbal communication and signed handwritten consent between the researcher and participants prior to data collection.
Consent for publication
Not applicable.
Copyright
© The Author(s) 2025.
Supplementary Materials
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