Table 1 shows the characteristics of the subjects. After data cleaning, there were only 49 Indonesian and 51 Taiwanese subjects left in the final dataset. Of these Indonesian subjects, 28 were males and 21 females, and 28 of them were under the age of 40. In Taiwan, there were 19 males and 32 females, with 28 of them under the age of 65. Subjects in both countries included white-collar workers, blue-collar workers, and housewife/unemployed. There were 20 and 29 subjects in the high-BMI group in Indonesia and Taiwan for health evaluation, respectively.

Table 2 shows the 5-min resolution of PM2.5, PM1 and PM1/PM2.5 ratios in both countries; the data can be found in [43]. Overall, the mean personal exposure of PM2.5 and PM1 was 30.4 ± 20.0 and 27.0 ± 15.7 µg/m3 in Indonesia and 14.9 ± 11.2 and 13.9 ± 9.8 µg/m3 in Taiwan, respectively. The means and standard deviations of PM2.5 and PM1 in Indonesia did not differ much in both seasons, with the maximum level of PM2.5 (473.6 µg/m3, during exposure to cooking) occurring in the wet season and that of PM1 (154.0 µg/m3, during exposure to mosquito coil burning) occurring in the dry season. In contrast, in Taiwan, the mean levels of PM2.5 and PM1 in winter were more than twice those in summer, with the maximum level of PM2.5 (467.4 µg/m3) being observed in summer and that of PM1 (217.7 µg/m3) being observed in winter, both occurring during exposure to emissions from community factories. PM2.5 and PM1 exposure in Taiwan was generally lower than that in Indonesia. The PM1/PM2.5 ratios of personal exposure were, on average, 0.90 in Indonesia and 0.92 in Taiwan, showing that the majority of PM2.5 is PM1. Additionally, these ratios did not change much in different seasons.

In ambient air, the overall PM2.5 level in Indonesia was 32.0 ± 14.0 µg/m3. That in the dry season was slightly higher than the personal exposure, and that in the wet season was in a similar range. However, the maximum PM2.5 in ambient air was 98.6 µg/m3, about one-quarter of the highest level of personal exposure (473.6 µg/m3). In Taiwan, the mean ambient PM2.5 and PM1 levels were 21.2 ± 8.1 and 20.5 ± 7.4 µg/m3, respectively, and they were slightly higher than the personal exposure in winter and almost double that in summer. Nevertheless, the maximum PM2.5 level in ambient air was 59.7 µg/m3 in Taiwan, about one-eighth of the highest personal exposure level (467.4 µg/m3). A huge discrepancy between personal peak PM exposure and maximum ambient levels was observed for both countries, presumably due to close human contact with nearby sources, again emphasizing the importance of assessing actual personal exposure in exposure–health evaluations.

With microsensors taking high-resolution measurements, peak exposure can be captured. Figure S1 (in Supplementary Information) shows one example of personal PM2.5 exposure in each country riding scooters with a 1-min resolution. In the morning, the highest peak PM2.5 of the Indonesian subject was 95.7 µg/m3, two times higher than the mean levels (37.0 ± 16.0 µg/m3) during the 1-h commuting period; the mean PM2.5 in the evening commuting period was 22.9 ± 10.0 µg/m3, with a peak of 70.6 µg/m3. In Taiwan, the subject was exposed to peak levels of 19.6 and 21.8 µg/m3, about 30% higher than the mean levels of 15.2 ± 3.1 and 15.7 ± 2.4 µg/m3 during the morning and evening commute, respectively.

Most epidemiological studies using ambient levels from standard monitoring stations with hourly means as surrogates for exposure [7,8,9] lack information on peak values. For tracking pollution trends, peak values may not be suitable for regulatory purposes. Nevertheless, for environmental health research, peak exposure levels are important since they could be responsible for asthmatic attacks and strokes [14, 44]. With the assistance of lightweight personal microsensors with a high temporal resolution, peak exposure in minutes can be captured, providing valuable information for exposure assessment and exposure–health evaluation.

Table 3 shows the means and standard deviations of seven HRV indices and HR during non-sleeping periods. Since the physiological signals during sleeping and non-sleeping periods are very different, only observations during non-sleeping periods were used for PM-health evaluation. There was no significant difference between two seasons in both countries. Most of the means and standard deviations of these indices of Taiwanese subjects were smaller than those of Indonesian subjects, presumably due to the older subjects in Taiwan.

With TADs, it is possible to differentiate the PM levels during exposure to different sources. The frequency of these exposure sources encountered by the subjects is shown in Fig. S2. In both countries, traffic exposure was the most common, and thus was classified into five transportation modes. Among these modes, cars and scooters were the two transportation modes used most frequently by Indonesian subjects; while for Taiwanese subjects, a scooter was the most commonly used mode. Besides traffic emissions, the most common sources in Indonesia and Taiwan were aromatic products and cooking, respectively. Three exposure sources only occurred in Taiwan, namely agricultural waste burning, garbage burning, and incense burning.

Figure 1 shows 5-min PM2.5 exposure levels when the subjects were exposed to different sources. Overall, the medians and 90th percentiles of PM2.5 exposure levels in Indonesia were higher than those in Taiwan for the same source; nevertheless, Taiwanese subjects were exposed to extremely high levels associated with community factory emissions. Mosquito coil burning and factory emissions are associated with the highest medians and 95th percentiles of PM2.5 among all sources in Indonesia and Taiwan, respectively. The PM1/PM2.5 ratios associated with different sources are shown in Fig. S3. Mosquito coil burning and community factories are associated with the lowest 5th percentiles of PM1/PM2.5 ratios of all sources in Indonesia and Taiwan, respectively. Additionally, most sources in Indonesia are associated with lower 95th percentiles of PM1/PM2.5 ratios than those sources in Taiwan. Nevertheless, most of the PM1/PM2.5 ratios range from 0.7 to 1.0 and do not significantly differ among most sources.

(a) Indonesia and (b) Taiwan.

The maximum 5-min PM2.5 exposure levels associated with different sources are listed in Table S1. The top two maximums were associated with cooking and mosquito coil burning in Indonesia and with community factories and mosquito coil burning in Taiwan. The ratio of the maximum 5-min PM2.5 exposure to the mean exposure of that hour was also calculated. The top two highest ratios in Indonesia are associated with cooking (ratio = 4.9) and mosquito coil burning (3.5), and those in Taiwan are associated with traffic exposure during scootering (3.6) and incense burning (3.4). Moreover, the exposure percentage of that source (considering the exposure duration of the same source) accounted for the overall 24-h PM2.5 exposure was calculated. In Indonesia, the top two are associated with aromatic products and mosquito coil burning, which accounted for 42.0% and 18.0% of the 24-h exposure, respectively. In Taiwan, the top two sources were ETS (39.8%) and factory emissions (37.1%). To reduce health risks, sources associated with high maximum/mean ratios or those accounting for a high percentage of daily exposure should be given high priority for source control.

The above source evaluation did not exclude the influence of concurrent ambient levels. Thus, GAMM was further applied to assess the average contribution of different sources to PM2.5 and PM1 exposure excluding the influence of ambient levels, with results shown in Table 4. There were seasonal differences, with more sources having statistically significant incremental contributions during the dry season in Indonesia. Overall, for Indonesian subjects, cleaning, cooking, community factories, mosquito coil burning, and traffic were statistically significant sources of exposure when observations from both seasons were pooled. Mosquito coil burning and community factories were the top two sources contributing, on average, 5.82 and 4.73 µg/m3 for PM2.5, respectively (Table 4a). In addition, the PM2.5 levels during walking, biking, scootering, and driving cars were 1.5–2.4 µg/m3 higher than the exposure during non-commuting periods. Interestingly, cleaning caused significantly less PM2.5 exposure in the wet season only. It is speculated that the higher relative humidity in the wet season may cause less resuspension of PM2.5, resulting in reduced exposure during cleaning. It should be noted that the source contribution to the PM2.5 exposure of these subjects was assessed previously using linear mixed-effects models [34], without removing the influence of ambient levels entirely. Using GAMM with direct deduction of concurrent ambient levels, our results showed slightly lower estimates than the previous estimates. Of course, the actual source contribution on exposure depends on the real condition. For example, in the worst situation of mosquito coil burning in this study, the 5-min peak exposure was 301.6 µg/m3 when the concurrent ambient level was only 46.7 µg/m3.

For Taiwanese subjects, agricultural waste burning, cooking, community factories, incense burning, and mosquito coil burning were statistically significant sources for either PM2.5 or PM1 exposure when observations from both seasons were pooled (Table 4b). During commuting, only the exposure levels of car drivers were statistically significantly lower compared to those during the non-commuting periods, among all different transportation modes. Community factories, mosquito coil burning, and agricultural waste burning were the three major contributors to PM exposure. Community factories and mosquito coil burning were the sources that contributed the highest increments to subjects’ PM2.5 (10.1 µg/m3) and PM1 (7.23 µg/m3) exposure, respectively. Community factories had a higher incremental PM2.5 or PM1 contribution in summer compared to winter. In contrast, agricultural waste burning had higher incremental PM2.5 or PM1 contributions in winter compared to summer. Additionally, it is surprising to see that mosquito coil burning occurred in winter rather than in summer. Taiwanese people usually use air conditioning indoors with the windows closed during the summer, while they tend to keep their windows open in the winter. With rising temperatures due to climate change, there are more mosquitoes in winter. This may be the reason why the subjects burned mosquito coils in winter to repel mosquitoes from outdoors.

Environmental research on industrial PM emissions has focused on large industrial parks. Investigations of community air quality or residents’ exposure affected by nearby factories have been scarce. Nevertheless, due to packed living conditions in Asia and Africa, the impacts of community factories have been reported to affect personal PM exposure with traditional personal samplers [29, 30, 45]. For example, Nkhama et al. [45]. conducted a panel study near a cement factory in Zambia and found that 24-h mean PM2.5 concentrations ranged from 2.39 to 24.93 µg/m3 in the exposed community compared to 1.69–6.03 µg/m3 in the control community. Recently, more work has been conducted with microsensors. For example, Omokungbe et al. [46] found that daily mean PM2.5 level was 213.3 µg/m3 with low-cost sensors, compared to 20.2–44.1 µg/m3 in the communities further downwind of an iron smelting facility in Nigeria. In Taiwan, the subjects passing by community factories were exposed to, on average, 38.4 µg/m3 higher daily PM10 exposure than the non-exposed subjects [29]. The 5-min PM2.5 and PM1 levels near community factories in Taiwan were, on average, 6.7 ± 8.9 and 5.8 ± 8.4 µg/m3 higher than those at the community background location [30]. The average PM2.5 (10.1 µg/m3) and PM1 (6.66 µg/m3) exposure increment due to community factories in the current study was within the previously reported range. This also shows that community factories remain a significant PM exposure source for Taiwanese, even though the ambient PM levels have significantly reduced in the past 20 years.

Traffic emissions are the most frequent encountered source by the subjects in both countries. Microsensors have been used to assess PM exposure during commutes in several Asian countries. deSouza et al. [47]. assessed the PM2.5 exposure of subjects in China taking four different transportation modes, namely bike (90-min averages: 31 ± 16 µg/m3), bus (nearly 1-h averages: 27 ± 15 µg/m3), subway (100-min averages: 25 ± 13 µg/m3), and taxi (60–90-min averages: 20 ± 8 µg/m3), with Plantower PMS1003 sensors. Patra and Vanajakshi [48] have applied Sensirion SPS 30 and Panasonic PM2.5 microsensors to pedestrians in India at heights of 80 and 150 cm; they found that the 1-min PM2.5 exposure levels were 56.7 ± 6.0 and 45.6 ± 5.9 µg/m3 on the first day and 26.3 ± 2.4 and 21.2 ± 1.9 µg/m3 on the second day, respectively. Wang et al. [49] with Plantower PMS3003 microsensors, found 5-min PM2.5 exposure levels of subjects using six different transportation modes in Taiwan to be 17.0 ± 9.5 µg/m3. Wu et al. [50] found the 1-min PM2.5 exposure levels of bikers to be 13.5 ± 8.4, 12.9 ± 4.2, and 15.4 ± 5.4 µg/m3 in Taipei, Osaka, and Seoul, respectively, during rush hours with AirVisual Node. The PM exposure during commuting in this study falls within the previously reported range.

For other sources, only a few studies have assessed personal PM2.5 exposure with microsensors. Tsou et al. [19] assessed the PM2.5 and PM1 exposure of 35 subjects in northern Taiwan and found that the highest contribution was from incense burning, which on average contributed 9.2 µg/m3 for both 5-min PM2.5 and PM1 exposures, higher than the current estimates (1.8–2.6 µg/m3). Lung et al. [28] assessed 33 subjects’ exposures in Taiwan and found that the highest contribution was from ETS, with 8.5 µg/m3 increments for 30-min PM2.5 exposure, higher than the estimate in the current study (roughly 0.5 µg/m3). The distance from the smokers may be one of the reasons for this difference. In addition, Hien et al. [51] assessed visitors’ PM2.5 and PM1 exposure inside temples in Vietnam and Taiwan. They found that PM2.5 and PM1 exposure levels were 36.5 ± 33.9 and 22.7 ± 18.7 µg/m3 (30-min averages) in Vietnamese temples and 97.0 ± 65.4 and 74.5 ± 53.4 µg/m3 (40-min averages) in Taiwanese temples, respectively. The PM exposure related to incense burning in the current study is lower than their assessment. Moreover, the PM emissions from aromatic products (such as candle burning and hair spray) have been reported with traditional monitors in only a few papers [52,53,54], and not with sensors. The affordability and ease of use of microsensors may facilitate their use in studies on these exposure sources.

Several chamber studies have highlighted the high PM emission factors of cooking, mosquito coil burning, incense burning, and candle burning [55,56,57,58]. Liu et al. [59] also noted the prevalence of mosquito coil burning in Asia, Africa, and South America, while Yadav et al. [60] emphasized the prevalence of incense burning in eight countries, including three heavily populated Asian countries: China, India, and Indonesia. These observations further underscore the importance of studying personal PM exposure resulting from these sources, which could be assessed with microsensors as shown in this work.

PM levels during the non-sleeping periods used in the exposure–health evaluation are summarized in Table S2. The immediate impacts of 5-min PM2.5 and PM1 on the HRV indices and HR of the subjects are listed in Table 5. For Indonesian subjects, only LF/HF was significantly affected by PM2.5 and PM1 in the wet season (Table S3). However, when focusing on analyzing those subjects riding scooters (n = 13), more HRV indices were affected. Thus, Table 5a, b summarizes the results of these scooter riders. It was found that more HRV indices reached statistically significant impacts associated with PM1 than PM2.5, and in the dry season than the wet season. In the dry season, on average, a −3.1% to −5.7% change in SDNN, LF/HF, LF, VLF, and TP were observed for a 10 µg/m3 increase in PM2.5. For a 10 µg/m3 increase in PM1, on average, a −2.5% to −6.7% change in SDNN, RMSSD, LF/HF, LF, VLF, and TP were observed in the dry season. Just for reference, the interquartile ranges (IQR) for the PM2.5 and PM1 exposure levels of Indonesian subjects during non-sleeping periods were 17.3 and 15.4 µg/m3, respectively. Additionally, the percentage changes of these significant HRV indices were all consistently higher when associated with PM1 than PM2.5. Additionally, no statistically significant lag effects on HRV indices were observed for these subjects (Fig. S4).

For Taiwanese subjects, HR and most HRV indices showed statistically significant impacts associated with both PM2.5 and PM1, with more statistically significant impacts in summer than in winter (Table 5c, d). In summer, a −1.3% to −4.0% change was observed for SDNN, RMSSD, HF, LF, VLF, and TP, for a 10 µg/m3 increase in PM2.5, and a −2.1% to −6.4% change, for a 10 µg/m3 increase in PM1. For reference, the IQRs for PM2.5 and PM1 exposure of Taiwanese subjects during non-sleeping periods were 12.9 and 12.4 µg/m3, respectively. Again, the percentage changes of these HRV indices and HR are consistently higher when associated with PM1 than for PM2.5; and no statistically significant lag effects were observed (Fig. S5).

Comparing the HRV changes in both countries, more HRV indices with statistically significant were observed in Taiwanese subjects compared to Indonesian scooter riders; this may due to much larger sample size in Taiwan (n = 33,125) than in Indonesia (n = 5946). When comparing the impacts of the dry season in Indonesia with those in the summer in Taiwan, it was found that the majority of percentage changes in significant HRV indices were slightly higher in Indonesia than in Taiwan. Age was already adjusted in the exposure–health models, but the inherent difference in the age distribution of these two groups may be the reason for these differences. Another interesting point is that the outdoor environment is a significant factor in the relationship between PM and HRV for both countries, with higher impacts observed in Indonesia. These models were re-run without considering the “outdoor” factor, and the results showed that most of the coefficients of PM changed only slightly (Tables S4, S5), indicating that the impact of the outdoor environment was associated with other factors. Activity level, which is one potential factor that may differ between indoor and outdoor microenvironments, was adjusted; thus, the underlying reasons for this significant outdoor impact on HRV require further investigation.

A meta-analysis including 26 epidemiological studies with 24-h or 48-h exposure reported that, on average, a −1.25% to −3.17% change in SDNN, RMSSD, LF, and HF were observed for a 10 µg/m3 increase in PM2.5 [61]. A recent meta-analysis focusing on older adults with 19 longitudinal studies found that for short-term exposure (days or weeks), a 10 µg/m3 increase in PM2.5 was associated with a −0.39% to −2.31% change in SDNN, RMSSD, LF, and HF [62]. The HRV changes found in this study fall within the aforementioned range. Additionally, in Taiwan, Huang et al. [39] assessed PM2.5 impacts on HRV for 50 housewives and found that a 1-h mean PM2.5 was associated with a −1.25% change in SDNN for an IQR change (19.8 µg/m3) in household PM2.5 exposure; the HRV change was twice the magnitude of our estimates, possibly due to younger subjects with a mean age of 38 ± 10.5 years in their study.

Most PM and health studies have typically focused on assessing the short-term (days or weeks) or long-term effects (months or longer) of PM exposure based on daily health records and hourly (or daily) exposure. This study stands out as one of the few to demonstrate the immediate health impacts of peak PM in the resolution of minutes, taking advantage of lightweight, low-cost, and portable microsensors in both the environmental and health fields. It was found that both PM2.5 and PM1 have immediate impacts on HRV indices, which are linked to an increased risk of heart attack. The technology and methodology employed in this study offer great potential for researchers in the resource-limited countries with high levels of PM2.5 and PM1. By utilizing these tools, it becomes possible to identify the sources responsible for peak PM exposure and implement targeted interventions to reduce immediate health impacts, whether through source control measures or behavior change strategies.

Our results show that community factories remain important exposure sources in Taiwan over the past 20 years, even though the ambient air quality, measured by monitoring stations situated at 10-m above the ground, has improved significantly. Community factories have been largely unsupervised, as most control strategies have focused on large industrial plants and traffic emissions. Lessons learned from Taiwan are applicable to other Asian countries with similar cultures and living styles. Many of them emphasize economic development with inadequate zoning regulations resulting in factories without proper controls scattered throughout communities. It is crucial to devise adequate zoning regulations to eliminate community factories and enforce proper control strategies for mid- or small-scale factories to reduce residents’ exposure to community factory emissions. To ensure that authorities implement appropriate measures, scientists must play an active role in providing solid evidence. The availability of low-cost microsensors and the methodology presented in this work can serve as examples to facilitate scientists in limited-funding Asian countries in conducting PM exposure assessment, source identification/quantification, and health evaluation. Implementing proper controls for the identified PM sources within communities can lower peak PM exposures and reduce the likelihood of certain lethal health impacts such as stroke.

Our findings highlight the importance of implementing targeted control measures for nearby sources, which may not currently be the primary focus of pollution mitigation efforts. By introducing measures to reduce emissions from these sources, significant reductions in peak exposure and associated immediate health impacts can be achieved. Furthermore, promoting behavior changes that address personal activities contributing to PM exposure, such as reducing the burning of scented candles (aromatic products), agricultural waste, incense, and mosquito coils, can further contribute to lowering individuals’ PM exposure levels. Overall, the application of microsensors, combined with targeted source control and behavior change interventions, presents substantial potential for reducing PM-related health risks, improving air quality in Asian countries, and thus creating a healthier environment for the population.

Based on the assessment from multiple angles, it was found that community factories and mosquito coil burning are two prominent exposure sources that contribute significantly to subjects’ exposure in both countries. Surprisingly, their contributions were even greater than those of ETS and traffic emissions, which are typically emphasized in the literature. The two sources were associated with the elevated levels of PM2.5 and PM1 peaks, accounting for a substantial proportion of the 24-h exposure, and contributed statistically significant increments compared to non-exposed periods. Given these findings, prioritizing interventions to mitigate the impacts of these sources is crucial for public health protection. While measures targeting industrial parks and motor vehicles remain important, focusing on these identified sources will provide an additional layer of protection. By effectively addressing these significant exposure sources, public health can be better safeguarded against the adverse effects of PM2.5 and PM1 pollution.

In terms of immediate health impacts, it was found that the impacts in the dry season and summer for Indonesian and Taiwanese subjects, respectively, were more severe than those in the wet season and winter, respectively. For scooter riders in Indonesia, a −3.1% to −5.7% and a −2.5% to −6.7% change in HRV indices were observed for a 10 µg/m3 increase in PM2.5 and PM1, respectively, in the dry season. For all Taiwanese subjects, the corresponding changes were −1.3% to −4.0% and −2.1% to −6.4%, respectively, in the summer, except for LF/HF. These findings align with previous studies conducted mostly in developed countries. Moreover, the immediate health impacts of PM1 were more severe than those of PM2.5, again emphasizing the urgent need to assess exposure and health impacts of PM1. Microsensors are valuable and affordable tools for low- and middle-income countries to evaluate the exposure and health impacts of PM2.5 and PM1, especially during their developing stages when pollution levels tend to be higher. This work serves as a good demonstration.

The comparison between the two panels is summarized here. Indonesian subjects were generally exposed to higher PM2.5 and PM1 levels than those in Taiwan; nevertheless, the PM1/PM2.5 ratios of the subjects’ exposure were similar (around 0.9) in both countries. Even though traffic emissions were the most frequently observed exposure sources, community factories and mosquito coil burning were the two most important exposure sources in both countries. These sources were associated with the highest medians and 95th percentiles of PM2.5 levels among all sources and the highest average incremental contribution to PM exposure after adjusting for concurrent ambient levels. Moreover, the HRV indices were affected by 5-min peak PM2.5 and PM1 exposure for subjects in both countries, with seasonal variations. The group of scooter riders in Indonesia had more HRV indices with statistically significant changes compared to the overall group. When comparing the HRV changes of scooter riders in the dry season of Indonesia with those of all subjects in summer in Taiwan, it was found that most percentage changes were higher in Indonesia than in Taiwan, possibly due to the younger age group in Indonesia. Furthermore, the impacts of HRV changes associated with PM1 were greater than those associated with PM2.5 in both countries.

Wearable sensors have been applied to assess the vital signs of subjects in various studies; however, most of the wearable sensors used in the literature were not medically certified [63,64,65]. Thus, the validity of the measurements is in question. In terms of heart signals, only HR is available, but not HRV indices in those consumer products. Assessing HRV indices is important in studying the impacts of peak PM exposure, since changes in these indices are associated with an increased risk of heart attack [21]. This work used a medically-certified sensor for HRV indices along with PM sensors, providing valuable evidence of the immediate health impacts of peak PM2.5 and PM1 exposure in two Asian countries.

Our study has several limitations. To match the 5-min resolution of HRV indices in health evaluation, the 15-s resolution of PM peaks and the 30-min resolution of TAD records were re-processed as 5-min. Thus, the peak values reported are underestimates. Moreover, it was assumed that all 5-min segments in a 30-min TAD record had the same exposure sources, which may result in underestimated source contributions in the GAMM analysis. Furthermore, only the primary exposure sources were used in the analysis, so the exposure frequency may be underestimated for certain sources. Nevertheless, the above limitations result in underestimates rather than overestimates of the source contribution and health impacts. Thus, these limitations do not affect the validity of our results. Additionally, our findings may have been confounded by other unmeasured air pollutants. Further investigation is needed to better understand the potential confounding effects of these pollutants. Besides, the number of subjects was limited in both countries; therefore, the generalization of the results is also limited to groups with similar characteristics. The occupational distributions of the subjects did not necessarily represent the occupational distribution of the general public in either country. However, the subjects did encompass a range of occupations, including white-collar workers, blue-collar workers, and housewives/unemployed individuals. Consequently, our findings, such as the identified exposure sources, were not biased toward any particular occupation. Overall, while acknowledging these limitations, our study still provides valuable insights into the assessment of exposure sources and the immediate health impacts of PM2.5 and PM1.

This study applied calibrated PM microsensors and medically-certified HRV microsensors to assess peak exposure, exposure sources, and immediate HRV changes due to PM2.5 and PM1 exposure in Indonesia and Taiwan. It demonstrates the advantages of utilizing high-resolution microsensors in PM exposure assessment and health evaluation. These microsensors serve as valuable tools for scientists in resource-limited countries where PM pollution is severe and targeted control measures are urgently needed. The identified important exposure sources serve as a primer for scientists in other countries with similar cultures and living styles to investigate their own exposure sources. The methodology could be applied to these countries to identify sources responsible for peak PM exposure. These findings can prompt authorities to revise zoning and control regulations, ultimately leading to a much healthier community environment.