Association between microenvironment air quality and cardiovascular health outcomes

Science of the Total Environment 716 (2020) 137027

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Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv

Association between microenvironment air quality and cardiovascular health outcomes Yue Qian Tan a, S.K. Abdur Rashid b, Wen-Chi Pan c, Yu-Cheng Chen d, Liya E. Yu b, Wei Jie Seow a,e,⁎ a

Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore Department of Civil & Environmental Engineering, National University of Singapore and NUS Environmental Research Institute, Singapore Institute of Environmental and Occupational Health Sciences, School of Medicine, National Yang-Ming University, Taipei, Taiwan d National Institute of Environmental Health Sciences, National Health Research Institutes, Miaoli, Taiwan e Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore b c

H I G H L I G H T S

G R A P H I C A L

A B S T R A C T

• Little is currently known about the short-term health effects of PM2.5 exposure. • Hawker centres have the highest PM2.5 levels. • Short-term PM2.5 is associated with increased heart rate, diastolic blood pressure.

a r t i c l e

i n f o

Article history: Received 17 September 2019 Received in revised form 29 January 2020 Accepted 29 January 2020 Available online 30 January 2020 Editor: Pavlos Kassomenos Keywords: Air pollution Blood pressure Heart rate Low-cost particle sensor Microenvironments PM2.5

a b s t r a c t Exposure to fine particulate matter (PM2.5) is associated with cardiovascular disease risk. To date, there are few studies on short-term PM2.5 exposure in different microenvironments and its impact on immediate health effects, particularly in the Southeast Asia region. This study assessed PM2.5 concentrations in different microenvironments in a densely populated city in the tropics using low-cost personal PM2.5 sensors as well as their associations with acute cardiovascular health outcomes. A total of 49 adult participants affiliated with the National University of Singapore (NUS) community were recruited. Personal low-cost sensors were used to measure PM2.5 concentrations between September 2017 and March 2019. Demographic information and time-activity patterns were collected using questionnaires. Wilcoxon pairwise comparisons were used to determine statistical differences between PM2.5 exposures at 18 different microenvironments. Generalized Estimating Equations (GEE) models were used to assess the association between PM2.5 exposure and blood pressure as well as heart rate. All models were adjusted for age, sex, body mass index, physical activity, temperature, duration of exposure, and baseline cardiovascular parameters. Significant differences in PM2.5 concentrations were observed across different microenvironments. Air-conditioned offices and tertiary teaching spaces had the lowest (median = 13.1 μg/m3) and hawker centres had the highest (median = 32.0 μg/m3) PM2.5 concentrations. Significant positive associations between PM2.5 exposure and heart rate (β = 0.40, p = 4.6 × 10−5) as well as diastolic blood pressure (β = 0.16, p = 0.0077) were also observed. Short-term exposure to PM2.5 was significantly associated with higher

⁎ Corresponding author at: 12 Science Drive 2, Singapore 117549, Singapore. E-mail address: [email protected] (W.J. Seow).

https://doi.org/10.1016/j.scitotenv.2020.137027 0048-9697/© 2020 Elsevier B.V. All rights reserved.

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Y.Q. Tan et al. / Science of the Total Environment 716 (2020) 137027

heart rate and blood pressure. Further work is needed to investigate the variations within each type of microenvironment and expand the study to other sub-populations such as the elderly and children. © 2020 Elsevier B.V. All rights reserved.

1. Introduction

2.2. Assessment of PM2.5 levels

Air pollution is one of the leading causes of mortality, and accounts for N3 million deaths per year globally (World Health Organization, 2016). PM2.5 refers to fine particulate matter 2.5 μm and smaller in diameter. Due to their miniscule size, these particles are able to penetrate deep into the lungs and enter the bloodstream. They have been found to be associated with the development of chronic diseases such as hypertension, cardiovascular diseases, and lung cancer (Cai et al., 2016; Liang et al., 2014; Lin et al., 2017; Meng et al., 2016; Raaschou-Nielsen et al., 2013; Shah et al., 2013). Cardiometabolic outcomes such as blood pressure and heart rate are risk factors for the development of other serious chronic diseases that are highly prevalent across various age groups and ethnicities such as cardiovascular diseases, type 2 diabetes, and various cancers (American Diabetes Association, 2014; Gress et al., 2000; Stocks et al., 2012). Previous studies have reported significant associations between PM2.5 exposure and cardiovascular and respiratory disease risk, as well as mortality (Brook Robert et al., 2010; Newell et al., 2017; Samet and Krewski, 2007; Yang et al., 2019). Most prior studies of the health effects due to PM2.5 exposure used fixed-site monitoring (FSM) network readings with extrapolation of outdoor air pollutant concentrations combined with spatial maps (Steinle et al., 2013). However, individuals normally spend most of their time indoors and at home (Brasche and Bischof, 2005; Leech et al., 2002), hence the indoor environment contributes a substantial portion of personal air quality exposure. Area monitoring data are often poor surrogates for personal exposure levels, as it fails to consider the indoor environment, nor the change between microenvironments as people move around during the day (J. Huang et al., 2012; Steinle et al., 2013). Therefore, the use of personal real-time air quality monitors is more appropriate for assessing actual human exposure, and may be better suited to identify potential determinants of exposure for downstream applications in personalized prevention and timely intervention. There are limited studies on short-term exposure in various microenvironments associated with potential immediate cardiovascular effects in the general population. This pilot study aims to bridge this gap by employing a low-cost, portable personal air quality monitor to examine real-time PM2.5 levels across different microenvironments in Singapore, and their associations with short-term cardiovascular health parameters.

A compact and low-cost PM2.5 sensor (HabitatMap.org, Brooklyn, NY, USA) was carried by each participant to assess temporal and spatial variations in PM2.5 concentration. The PM2.5 sensor is a palm-sized air quality monitor which measures mass concentration of PM2.5, temperature, and relative humidity. It is an optical particle counter built with original equipment manufacturer (OEM) sensor components, Shinyei PPD60PV (Jiao et al., 2016), which uses the light scattering method to detect PM2.5. Air is drawn through a sensing intake chamber wherein light from a light-emitting diode (LED) bulb scatters off particles in the airstream, then a photodiode array with a lens detects the scattered light at 45 degrees (Johnson et al., 2016). The light scatter is registered by a detector and then converted into a measurement that estimates the number of particles in the air. These measurements were then communicated every second to the AirCasting Android application via Bluetooth and converted to an estimated particle mass concentration (μg/m3) by their proprietary algorithm.

2. Methods 2.1. Study population A total of 49 adult participants affiliated with the National University of Singapore (NUS) community (students, staff, alumni) were recruited and assessed for PM2.5 exposure between September 2017 and March 2019. The participants were 18 years of age or above, generally healthy with no serious medical conditions, no diagnosed history of heart attack, or major cardiovascular conditions or any current conditions requiring in-patient care. Questionnaires were administered to each participant and information such as personal demographics, smoking habits, physical activity and medical history was collected. Participants also recorded their time-activity patterns using an activity diary that included time-location-activity information. Informed consent was obtained from all participants before commencement of the study. This study was approved by the NUS Institutional Review Board (IRB).

2.3. Calibration of the PM2.5 sensor The data collected via the portable PM2.5 sensors were calibrated against PM2.5 concentrations measured by two gold-standard instruments for a total of 15 days across March, August and September 2018. The portable PM2.5 sensor and the 3-hourly beta-ray attenuation PX-375 (HORIBA Ltd., Japan) and the in-house built 24-hour bulk filter-based gravimetric reference method device (NUS, Singapore), were co-located on the rooftop of a building in the Faculty of Engineering at NUS (1.299028°N, 103.7713°E). The portable PM2.5 sensor data at per-second frequency was aggregated to relevant intervals in order to compare with the data measured by the gold-standard instruments. The processes and discussion on the calibration of the portable PM2.5 sensor data are documented in detail elsewhere (SK Abdur et al., 2019). In brief, the portable PM2.5 sensor data were calibrated to simulate the trends of filter-based PM2.5 concentrations via the Eq. (1) below. ½PM2:5 corrected ¼ 0:535  ½PM2:5 AirBeam þ 10:45

ð1Þ

where [PM2.5]corrected is the corrected PM2.5 concentration; [PM2.5] AirBeam is the original PM2.5 concentration sensor output. Eq. (1) is established through the following steps. The 3-hourly measurements of beta ray attenuation (or beta ray gauge, BRG) were aggregated to 24-hour mean to compare against the USEPA reference gravimetric data (n = 53) to account for the loss of semi-volatile organic and inorganic compounds such as nitrates. The corrected BRG data then serve as the reference to benchmark the 3-hour mean of the PM2.5 sensor data (n = 14) to make up the mass contribution of PM0.3 underestimated by the optical method.

2.4. Assessment of cardiovascular health outcomes Participant cardiovascular health parameters such as systolic blood pressure, diastolic blood pressure and heart rate were collected. Participants were advised to take two blood pressure measurements upon arrival to and departure from each microenvironment. The average of these two readings at each timepoint was then obtained.

Y.Q. Tan et al. / Science of the Total Environment 716 (2020) 137027

2.5. Statistical analyses

Table 1 PM2.5 concentration (μg/m3) in different microenvironments.

Descriptive statistics of PM2.5 concentrations at each microenvironment were calculated. Wilcoxon rank-sum tests were used to test for differences in PM2.5 concentrations between microenvironments. Pairwise Wilcoxon tests were also run with False Discovery Rate (FDR) correction. Generalized estimating equations (GEE) models were used to obtain the estimates, 95% confidence intervals (CIs) and p-values of the associations between PM2.5 concentration and each health parameter. All models were adjusted for age, sex, body mass index (BMI), average temperature during sampling, number of steps tracked per minute, and the duration spent in the microenvironment; these covariates were selected a priori. The number of steps tracked per minute recorded by FitBit served as a proxy for activity intensity, which is a major confounder of heart rate and blood pressure (Melanson, 2000; Nelson et al., 1986; Strath et al., 2000). The corresponding baseline measurements of each cardiovascular health parameter taken at the beginning of each microenvironment were included in the respective models. Participant IDs were used as the cluster identifiers in the GEE models. Quasi-likelihood under Independence Model Criterion (QIC) was used to determine the optimal correlation structure (independence, unstructured, or exchangeable) for each GEE model. All analyses were conducted using RStudio Version 1.1 (Rstudio Team, 2016) implementing R Version 3.4 (R Development Core Team, 2017). p-Values of b0.05 were considered as statistically significant.

Microenvironment

3. Results The study participants had an average age of 28.6 years old and the majority (96%) of them were Chinese (Supplementary Table 1). All the participants completed at least secondary school, whereas 26 (53%) of them completed university and above. The majority (94%) of the participants were never smokers. A total of 525 sampling sessions were collected over the recruitment period. The locations were grouped into 18 distinct microenvironments: homes (air-conditioned, non-air-conditioned), hawker centres, wet markets, food courts (air-conditioned, non-air-conditioned), shopping malls/indoor public spaces (air-conditioned), offices/tertiary teaching spaces (air-conditioned), hospitals, restaurants, shophouses, parks, outdoor environments, miscellaneous sheltered open-air premises, as well as transportation vehicles (bus, Mass Rapid Transit (MRT), car) (Table 1). Pictures of the microenvironments were shown in Supplementary Fig. 1. Personal exposure to PM2.5 was significantly different across microenvironments (p b 0.05) (Supplementary Fig. 2). In general, as expected, air-conditioned premises had lower PM2.5 levels than non-airconditioned premises. Air-conditioned offices and tertiary teaching spaces had the lowest PM2.5 concentration (mean (SD) = 13.1 (5.61) μg/m3) whereas hawker centres had the highest PM2.5 concentration (mean (SD) = 32.0 (11) μg/m3) (Table 1). MRT has the highest mean PM2.5 levels (mean (SD) = 25.0 (7.85) μg/m3) among all transportation vehicles. There were significant differences between different pairs of microenvironments. Hawker centres have significantly higher PM2.5 levels than in buses, cars, air-conditioned homes, school and office environments as well as on the streets (p b 0.05) (Supplementary Fig. 3). However, we did not observe significant differences in PM2.5 levels between the different types of eateries, except between hawker centres and nonair-conditioned food courts. Air-conditioned school and office environments have significantly lower average PM2.5 levels than most of the other microenvironments (p b 0.05). Using GEE models, every 1 μg m−3 increase in PM2.5 concentration was significantly associated with 0.16 mmHg (95% CI: 0.029–0.28) higher diastolic blood pressure. Similarly, every 1 μg m−3 increase in PM2.5 was significantly associated with 0.40 bpm (95% CI: 0.29–0.50)

3

k (Total duration sampled, in seconds)

Mean SD

15,770 121,534 318,213 297,587

24.2 20.2 18.9 13.1

5.61 11.8 8.3 2.34

18,938

11.9

0.46

32,484 5648 912,811

24.9 17.9 20.8

8.9 5.71 6.43

Primary and secondary schools

73,533

15.4

3.75

Restaurants

33,411

20.3

5.48

Sheltered open-air Hawker centres Wet markets Others

46,516 23,721 16,907

32.0 27.1 20.7

11.0 6.85 6.07

Outdoors Outdoors (general) Parks

95,304 51,047

21.5 21.6

6.26 7.41

Transportation vehicles MRT Bus Car

71,658 51,300 55,835

25.0 15.2 15.3

7.85 3.28 3.97

Indoors (air-conditioned) Food court Mall and other public spaces Home Offices and tertiary teaching spaces Hospitals Indoors (non-air-conditioned) Food court Shophouses Home

Abbreviation: SD, Standard deviation.

higher heart rate (Table 2). However, no significant association was observed between PM2.5 and systolic blood pressure. 4. Discussion In our pilot study, PM2.5 concentrations were significantly different across microenvironments in Singapore. PM2.5 concentrations were also significantly and positively associated with diastolic blood pressure and heart rate. Therefore, short-term PM2.5 exposure in different microenvironments is potentially associated with immediate cardiovascular effects in individuals. Consistent with previous studies, the highest PM2.5 concentration among microenvironments was found in hawker centres, due to high emission of cooking fumes (Tam, 2017; Wong et al., 2003). In Table 2 Association between PM2.5 levels in microenvironments and cardiovascular parameters among study participants using generalized estimating equations (GEE). Outcome parameters Systolic blood pressurea Diastolic blood pressureb Heart ratec

β estimates for PM2.5

95% CI

P-value

0.025 0.16 0.40

−0.13–0.18 0.029–0.28 0.29–0.50

0.62 0.0077 4.6e-05

Abbreviations: CI, confidence interval. Bolded P-values (b0.05) denote statistical significance. a Model was adjusted for initial systolic blood pressure measurement, sex, age, body mass index, average temperature during sampling, number of steps tracked per minute and the duration spent in the microenvironment; correlation structure chosen was “unstructured”. b Model was adjusted for initial diastolic blood pressure measurement, sex, age, body mass index, average temperature during sampling, number of steps tracked per minute and the duration spent in the microenvironment, correlation structure chosen was “unstructured”. c Model was adjusted for initial heart rate measurement, sex, age, body mass index, average temperature during sampling, number of steps tracked per minute and the duration spent in the microenvironment, correlation structure chosen was “unstructured”.

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Singapore, naturally ventilated hawker centres are food establishments housing multiple food stalls (Supplementary Fig. 1(c)–(d)). It is where most local residents have their daily meals. Chinese stir-fried food has been shown to generate more particles as compared to other cooking methods (Lee et al., 2001; See and Balasubramanian, 2006). Our participants were mostly students and staff from the National University of Singapore, therefore, most tertiary institution teaching spaces and office microenvironments in this study include lecture theatres or tutorial classrooms on campus. As these spaces were generally air-conditioned with no open windows, there was minimal influx of PM2.5 from the outdoor environments (Kousa et al., 2002). Unsurprisingly, this microenvironment has the lowest average PM2.5 concentration in our study. Conversely, other indoor air-conditioned public spaces such as shopping malls and community centres have higher PM2.5 concentrations than offices. This is most likely due to higher human traffic and the influx of PM2.5 from the external environment (Braniš et al., 2005). Studies conducted in other cities also reported similar findings. A previous study in Seoul, South Korea reported that food establishments had the highest PM2.5 concentrations among all microenvironment types, although the average PM2.5 concentration was much higher (mean ± SD = 188.5 ± 306.8 μg/m3) than that in our study (Lim et al., 2012). Similarly, our study had comparable average outdoor PM2.5 concentrations (mean ± SD = 21.5 ± 6.26 μg/m3) with the Lim et al. study (mean ± SD = 21.0 ± 16.5 μg/m3). However, PM2.5 concentrations in Singapore's home environments are slightly higher than in Seoul (Lim et al., 2012), although the ventilation modes at the sampled locations were not specified. An industrialized city, Kocaeli, Turkey, had an average PM2.5 concentration of 23.5 μg/m3 in their outdoor environments during summer (Pekey et al., 2010), which is similar to Singapore's outdoor PM2.5 concentrations. In another densely populated city, Santiago, Chile, vehicles in general had higher PM2.5 concentrations than those in Singapore, particularly buses (mean = 60.4 μg/m3) (Suárez et al., 2014). In this study, we assessed the association of short-term personal PM2.5 exposure in different microenvironments and immediate cardiovascular health outcomes, and found that microenvironments with higher PM2.5 levels may have immediate negative effects on cardiovascular health. Similar associations between short-term PM2.5 exposures, ranging from lab studies that lasted for a few hours, to observed lag effects of one day, and adverse health effects of the exposures, especially on cardiovascular health, have been reported in several studies (Auchincloss et al., 2008; Brook et al., 2011; Chen et al., 2018; Lanki et al., 2008; Liang et al., 2014; Pope et al., 1999; Provost et al., 2016). A regression-based study done in China, using mortality data from the China CDC and national air pollution monitoring system, found shortterm exposures to PM2.5 to be associated with higher cardiovascularrelated mortality risk (Chen et al., 2018). This further justifies the need to study PM2.5 exposure with higher temporal and spatial resolution. A previous meta-analysis of 22 studies, seven of which were done in Asia, concluded that higher PM2.5 is significantly associated with higher blood pressure (Liang et al., 2014). Various mechanisms such as metabolic activation, oxidative stress, mutagenicity and immune response were postulated to mediate the health effects from PM2.5 exposures (Feng et al., 2016). Exposure to PM2.5 triggers acute inflammatory responses, and increases the number of leucocytes in lung tissues and the circulatory system (Salvi et al., 1999; Terashima et al., 1997), hence triggering the heart to pump faster, in order to compensate for the lower efficiency of gas exchange. A toxicology study in rats found PM2.5 to initiate systemic inflammation, endothelial function, and autonomic nervous system injuries (Wang et al., 2013). Even among healthy young adults, there were significant positive associations between air pollution index and inflammatory responses (W. Huang et al., 2012). Chronic inflammation also contributes to atherosclerosis, which ultimately leads to hypertension and other cardiovascular diseases (Feng et al., 2016). Certain subgroups of people are more vulnerable to the adverse effects of air pollution,

particularly young children as their lungs are still developing, and the elderly with weakened cardiorespiratory and immune systems (Anderson et al., 2003; Bateson and Schwartz, 2007; Buonanno et al., 2013a; Chang et al., 2007; Delfino Ralph et al., 2004; Gauderman et al., 2004). From our findings, certain microenvironments such as hawker centres and public transportation modes like the MRT have significantly higher PM2.5 concentrations. A previous risk assessment study conducted in Singapore suggested that air quality at food centres may pose adverse health effects from long-term exposure to cooking emissions, for both workers and patrons visiting the premises (See and Balasubramanian, 2006). Other studies in Asia have reported that Chinese cooking emits higher PM2.5 concentrations in the direct respiratory zone, and that kitchen workers in Chinese restaurants using gas stoves had poorer lung function as compared to those using electric stoves (Lu et al., 2019; Wong et al., 2011). An exposure assessment among Italian children also revealed that transport and cooking activities contributed to almost 20% of their total daily dose of ultrafine particles, and cooking had the highest exposure intensity (Buonanno et al., 2013b). On the other hand, urban trees have been shown to remove large amounts of air pollutants in the US and China (Jim and Chen, 2008; Nowak et al., 2006). Green spaces have also been shown to reduce mortality from respiratory and cardiovascular diseases via reducing air pollution (Shen and Lung, 2016, 2017). Interestingly, our data showed variations in the average PM2.5 levels across different parks or green spaces in Singapore. Vegetation across parks are not homogenous as it is influenced by the complexity of structures (the ratio of trees, shrubs and herbaceous layers), and the degree of human management (planting, pruning, irrigation and fertilization), hence the differences in the extent of air purification, and their benefits for human health (Vieira et al., 2018). As personal exposure is closely related to activity patterns in different microenvironments (Lim et al., 2012; Van Ryswyk et al., 2014), it is likely that there are differences in the associations between PM2.5 concentrations across different microenvironments and adverse health effects (Delfino Ralph et al., 2004). Most previous studies in China and elsewhere which evaluated personal PM2.5 exposures and its association with blood pressure used industrial grade gravimetric PM2.5 measurements from monitors which were bulkier (Baccarelli et al., 2011; Baumgartner et al., 2011; Du et al., 2010; Xie et al., 2016), and only reported accumulative health effects over a longer period of exposure (24 h or longer). In our study, we used optical portable sensors which measures real-time PM2.5 exposure at higher spatial and temporal resolution. The use of more compact devices in this study, specifically low-cost optical portable sensors, will help to improve the feasibility of monitoring a person's typical daily activities (Baumgartner et al., 2011). Similar to our study, one study in China measured individual PM2.5 from 8 am-6 pm for a day, and found an association between PM2.5 exposure and higher blood pressure (Xie et al., 2016). However, they recorded blood pressure only once at the end of the day, recruited older participants (mean age = 56.7), and information on the different microenvironments was not collected. In contrast, we assessed short-term changes in PM2.5 and cardiovascular parameters across different microenvironments that an individual encounters daily. To the best of our knowledge, this is the first study to explore the application and effectiveness of low-cost personal sensors in a highly dense city with a tropical climate, with an emphasis on public health implications. Low-cost personal sensors can be used to supplement air quality monitoring in environments that are not covered by stationary monitoring sites, and also to increase public engagement and awareness about air quality issues (Morawska et al., 2018). The portability of these low-cost sensors makes it possible for large-scale epidemiological studies to quantify real-time personal air quality, and enable a more comprehensive investigation of the association between air quality and health effects, as seen by the wide coverage of different microenvironment types in this study. Furthermore, we addressed potential

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confounding factors that were not considered previously (Rumchev et al., 2018; Xie et al., 2016), such as physical activity levels and ambient temperature which are potential confounders for blood pressure and heart rate measurements (Melanson, 2000; Nelson et al., 1986; Strath et al., 2000). However, we did not collect information on the stressfulness of each microenvironment, which may act as a potential confounder. Another limitation of this study includes the relatively small number of participants with measured PM2.5 and health measurements in each microenvironment. However, we did a post-hoc power calculation, and our current sample size is sufficient with 80% power for our significant findings in diastolic blood pressure and heart rate, albeit not for systolic blood pressure. Our participants also tend to be younger due to convenience sampling. Therefore, the observed associations may not be representative of the general population. Further studies in other age groups are warranted. Nevertheless, the findings suggest that short-term PM2.5 exposures are associated with immediate cardiovascular health outcome changes such as blood pressure and heart rate in this study population. As gaseous pollutants were not assessed by the portable sensors we used, their health effects (de Paula Santos et al., 2004; Kheirbek et al., 2013) were not included in the scope of this study, and thus our analyses were limited by the lack of this information. To prevent PM2.5-related adverse health effects, there is a need to mitigate its concentration in the urban environment. Open-air eateries where the cooking area is close to the dining area may require sufficient ventilation to reduce PM2.5 concentration in the premises. People who are occupationally exposed to higher PM2.5 concentration, such as kitchen workers and public transport staff, may adopt the use of personal protective equipment such as N95 or certified masks. Indoor environments which have higher PM2.5 should install air filters whenever possible (Laumbach et al., 2015). Custodians may also minimize the exposure time of children and the elderly under their care in microenvironments with high PM2.5 (Laumbach et al., 2015). Microenvironments frequented by young children and the elderly should have air quality monitored to ensure that mitigation efforts are effective. 5. Conclusions Using low-cost real-time PM2.5 sensors, our study assessed personal PM2.5 concentrations at specific microenvironments which people encounter in their daily lives. We identified certain microenvironment hotspots, such as hawker centres, that can increase personal PM2.5 exposures. Mitigations are needed at these hotspots to reduce PM2.5 exposures in the general population. Interestingly, we also found significant positive associations between PM2.5 concentrations and cardiovascular health parameters such as diastolic blood pressure and heart rate. Future work is warranted to expand the study population to include vulnerable populations such as the elderly and children, and to elucidate the biological mechanisms of PM2.5-mediated health effects. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgements This research is funded by the National University of Singapore Startup Grant. Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi. org/10.1016/j.scitotenv.2020.137027.

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