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Both ambient fine particulate matter (PM2.5) and aging are important urban concerns. However, the associations between PM2.5 constituents and the acceleration of aging (AA) remain unclear. We included 16,051 adults (aged 25–80 years) with 19,252 medical observations in Taiwan during 2008−2017. 2-year average PM2.5 and its five major constituents were assessed using a two-stage machine learning model at a resolution of 1 km2. AA was determined by the difference between the Klemera–Doubal biological age and chronological age. A linear mixed model (LMM) with inverse probability weights was used to examine the associations between AA and air pollution. In a semi-randomized study design, we applied a post-matching LMM to assess the impacts of changes in air pollution exposure on AA. Each interquartile range increase in ambient PM2.5, SO4-2, NO3–, NH4+, organic matters (OM), and black carbon (BC) was associated with a 0.20 (95 %confidence interval [CI]: 0.17–0.24), 0.19 (0.15–0.23), 0.14 (0.11–0.18), 0.21 (0.17–0.24), 0.22 (0.19–0.26) and 0.25 (0.21–0.28) year increase in AA, respectively. BC was generally associated with the greatest increase in AA as compared to other constituents. We did not find evident thresholds in their concentration–response associations. Participants exposed to increased levels of PM2.5, SO4-2, NO3–, NH4+, OM, and BC experienced an increase in AA of 0.11 (−0.07–0.29), 0.20 (0.02–0.39), 0.15 (−0.02–0.33), 0.12 (−0.07–0.31), 0.24 (0.07–0.41), and 0.30 (0.07–0.52) years, respectively, compared to those exposed to decreased/unchanged levels. Long-term exposure to ambient PM2.5 and its constituents may accelerate biological aging among Chinese adults. Exposed to increased levels may further aggregate the aging process. This study suggests that reducing exposure to air pollution is beneficial, even for residents within moderately-to-highly polluted regions, such as Taiwan. Rigorous regulation of PM2.5 and its constituents may prevent the acceleration of biological age.

Background: Temperature affects influenza transmission; however, currently, limited evidence exists about its effect in China at the national and city levels as well as how temperature can be integrated into influenza interventions. Methods: Meteorological, pollutant, and influenza data from 201 cities in mainland China between 2013 and 2018 were analyzed at both the city and national levels to investigate the relationship between temperature and influenza prevalence. We examined the impact of temperature on the time-varying reproduction number (Rt) using generalized additive quasi-Poisson regression models combined with the distributed lag nonlinear model. Threshold temperatures were determined for seven regions based on the early warning threshold of serious influenza outbreaks, set at Rt = 1.2. A multivariate random-effects meta-analysis was employed to assess region-specific associations. The excess risk (ER) index was defined to investigate the correlation between Rt and temperature, modified based on seasonal and regional characteristics. Results: At the national level and in the central, northern, northwestern, and southern regions, temperature was found to be negatively correlated with relative risk, whereas the shapes of the data curves for the eastern, southwestern, and northeastern regions were not well defined. Low temperatures had an observable effect on influenza prevalence; however, the effects of high temperatures were not obvious. At an Rt of 1.2, the threshold temperatures for reaching a warning for serious influenza outbreaks were − 24.3 °C in the northeastern region, 16.6 °C in the northwestern region, and between 1℃ and 10 °C in other regions. Conclusion: The study findings revealed that temperature had a varying effect on influenza transmission trends (Rt) across different regions in China. By identifying region-specific temperature thresholds at Rt = 1.2, more effective early warning systems for influenza outbreaks could be tailored. These findings emphasize the significance of the region-specific adaptation of influenza prevention and control measures.

Background: Particulate matter (PM) has been found to elevate the risk of pulmonary embolism (PE) onset. Among the contributors to PM, dust PM stands as the second natural source, and its emissions are escalating due to climate change. Despite this, information on the effect of dust PM on PE onset is scarce. Hence, this study aims to investigate the impacts of dust PM10, dust PM2.5–10, and dust PM2.5 on PE onset. Methods: A nationwide time-stratified case-crossover study was conducted between 2015 and 2020, using data from 18,616 PE onset cases across 1,921 hospitals in China. The analysis employed a conditional logistic regression model to quantify the associations between dust PM10, dust PM2.5–10, and dust PM2.5 and PE onset. Furthermore, the study explored the time-distributed lag pattern of the effect of dust PM on PE development. Stratified analyses were performed based on sex, age, region, and season. Results: Dust PM10, dust PM2.5–10, and dust PM2.5 exhibited significant health effects on PE onset, particularly concerning exposure on the same day. The peak estimates were observed at lag 01 day, with the odds ratio being 1.011 [95 % confidence interval (CI): 1.003, 1.019], 1.014 (95 % CI: 1.003, 1.026), and 1.039 (95 % CI: 1.011, 1.068), for a 10 μg/m3 increase in the concentration of dust PM10, dust PM2.5–10, and dust PM2.5, respectively. In addition, the study identified a higher risk of PE onset associated with dust PM exposure during the warm season than that in cool season, particularly for dust PM2.5. Conclusions: The findings from this study suggest that short-term exposure to dust PM, particularly dust PM2.5, may trigger PE onset, posing a significant health threat. Implementing measures to mitigate dust PM emissions and protect patients with PE from dust PM exposure is imperative.

Climate change has made disastrous heatwaves more frequent. Heatwave-related health impacts are much more devastating for more intense heatwaves. In the summer of 2017, exceptional heatwaves occurred in many regions, including China. This study aims to evaluate the cardiovascular mortality risk associated with the 2017 exceptional heatwaves and compare the mortality risk of the severe heatwaves with those in other years. Using daily data for a spectrum of cardiovascular mortality and temperature for 102 Chinese counties (2014–2017), we estimated the association between heatwave and mortality by generalized linear mixed-effects models. Compared with matched non-heatwave days, mortality risks on heatwaves days in 2017 increased 27.8% (95% CI, 14.8–42.3%), 26.7% (8.0–48.5%), 30.1% (10.2–53.7%), 27.3% (1.4–59.9%), 32.2% (3.4–68.4%), and 25.2% (1.0–57.7%) for total circulatory diseases, cerebrovascular disease, ischemic heart disease (IHD), acute IHD, chronic IHD, and myocardial infarction. The 2017 exceptional heatwaves impacted ischemic heart disease mortality and myocardial infarction mortality more than heatwaves in 2014–2016. Here we show that the severe heatwaves in 2017 posed catastrophic death threats for those under-studied cardiovascular diseases.

Background: Ambient ozone pollution is steadily increasing and becoming a major environmental risk factor contributing to the global disease burden. Although the association between short-term ozone exposure and mortality has been widely studied, results are mostly reported on deaths from non-accidental or total cardiopulmonary disease rather than a spectrum of causes. In particular, a knowledge gap still exists for the potential thresholds in mortality risks. Methods: This nationwide time-series study in China included 323 counties totaling 230,266,168 residents. Daily maximum 8-hour average was calculated as the ozone exposure metric. A two-stage statistical approach was adopted to assess ozone effects on 21 cause-specific deaths for 2013–2018. The subset approach and threshold approach were utilized to explore potential thresholds, and stratification analysis was used to evaluate population susceptibility. Results: On average, the annual mean ozone concentration was 93.4 μg/m3 across 323 counties. A 10-μg/m3 increase in lag 0–1 day of ozone was associated with increases of 0.12 % in mortality risk from non-accidental disease, 0.11 % from circulatory disease, 0.09 % from respiratory disease, 0.29 % from urinary system disease, and 0.20 % from nervous system disease. There may be a “safe” threshold in the ozone-mortality association, which may be between 60 and 100 μg/m3, and vary by cause of death. Women and older adults (especially those over 75) are more affected by short-term ozone exposure. Populations in North China had a higher risk of ozone-related circulatory mortality, while populations in South China had a higher risk of ozone-related respiratory mortality. Conclusions: National findings link short-term ozone exposure to premature death from circulatory, respiratory, neurological, and urinary diseases, and provide evidence for a potential “safe” threshold in the association of ozone and mortality. These findings have important implications for helping policymakers tighten the relevant air quality standards and developing early warning systems for public health protection in China.

Ambient fine particulate matter (PM2.5) pollution is a major environmental and public health challenge in China. In the recent decade, the PM2.5 level has decreased mainly driven by reductions in particulate sulfate as a result of large-scale desulfurization efforts in coal-fired power plants and industrial facilities. Emerging evidence also points to the differential toxicity of particulate sulfate affecting human health. However, estimating the long-term spatiotemporal trend of sulfate is difficult because a ground monitoring network of PM2.5 constituents has not been established in China. Spaceborne sensors such as the Multi-angle Imaging SpectroRadiometer (MISR) instrument can provide complementary information on aerosol size and type. With the help of state-of-the-art machine learning techniques, we developed a sulfate prediction model under support from available ground measurements, MISR-retrieved aerosol microphysical properties, and atmospheric reanalysis data at a spatial resolution of 0.1°. Our sulfate model performed well with an out-of-bag cross-validation R2 of 0.68 at the daily level and 0.93 at the monthly level. We found that the national mean population-weighted sulfate concentration was relatively stable before the Air Pollution Prevention and Control Action Plan was enforced in 2013, ranging from 10.4 to 11.5 µg m−3. But the sulfate level dramatically decreased to 7.7 µg m−3 in 2018, with a change rate of −28.7 % from 2013 to 2018. Correspondingly, the annual mean total non-accidental and cardiopulmonary deaths attributed to sulfate decreased by 40.7 % and 42.3 %, respectively. The long-term, full-coverage sulfate level estimates will support future studies on evaluating air quality policies and understanding the adverse health effect of particulate sulfate.

Although exposure to fine particulate matter (PM2.5) has been associated with cognitive decline, little is known about which PM2.5 constituents are more harmful. Recent study on the association between PM2.5 and sleep quality prompted us to propose that sleep quality may mediate the adverse effects of PM2.5 components on cognitive decline. Understanding the association between PM2.5 constituents and cognitive function, as well as the mediating role of sleep quality provides a future intervention target for improving cognitive function. Using data involving 1834 participants from a multicenter cross-sectional study in nine cities of the Beijing-Tianjin-Hebei (BTH) region in China, we undertook multivariable linear regression analyses to quantify the association of annual moving-average PM2.5 and its chemical constituents with cognitive function and to assess the modifying role of exposure characteristic in this association. Besides, we examined the extent to which this association of PM2.5 constituents with cognitive function was mediated via sleep quality by a mediation analysis. We observed significantly negative associations between an increase of one interquartile range increase in PM2.5 [−0.876 (95 % CI: −1.205, −0.548)], organic carbon [−0.481 (95 % CI: −0.744, −0.219)], potassium [−0.344 (95 % CI: −0.530, −0.157)], iron [−0.468 (95 % CI: −0.646, −0.291)], and ammonium ion [−0.125 (95 % CI: −0.197, −0.052)] and cognitive decline. However, we didn’t find any individual components more harmful than PM2.5. Poor sleep quality partially mediated the estimated associations, which were explained ranging from 2.28 % to 11.99 %. Stratification analyses showed that people living in areas with lower greenspace were more susceptible to specific PM2.5 components. Our study suggests that the adverse effect of suffering from PM2.5 components is more pronounced among individuals with poor sleep quality, amplifying environmental inequalities in health. Besides reducing environmental pollution, improving sleep quality may be another measure worth considering to improve cognition if our research is confirmed in the future.

Background: Air pollution is associated with accelerated biological ages determined by DNA methylation (DNAm) patterns, imposing further risks of age-related adverse effects. However, little is known about the independent and joint effects of exposure to gaseous organic chemicals that may share a common source. Methods: We conducted a panel study with the 3-day exposure assessment monthly among 73 Chinese healthy elderly people aged 60 to 69 years in Jinan, Shandong province during September 2018 to January 2019. Exposure to 26 ambient organic chemical contaminants were measured by wearable passive samplers, including volatile organic compounds, polycyclic aromatic hydrocarbons (PAHs), phthalates (PAEs), nitroaromatics (NIs), polybrominated diphenyl ethers, chlorinated hydrocarbons, and organophosphate esters. The Illumina MethylationEPIC BeadChip was used to measure DNA methylation levels in blood samples, and based on which, epigenetic ageing biomarkers, including Hannum clock, Horvath clock, DNAm PhenoAge, DNAm GrimAge, and DNAm estimator of telomere length (DNAmTL) were calculated. Linear mixed effect models were used to estimate the linear associations between 3-day personal chemical exposure and the epigenetic biomarkers, Weighted quantile sum (WQS) regression and the Bayesian kernel machine regression (BKMR) model were further used to evaluate the effect of chemical mixtures. Results: Multiple linear mixed effects regression models showed that DNAmPhenoAge acceleration was significantly and positively associated with exposure to PAEs, NIs, and PAHs in healthy elderly individuals. Both WQS regression and BKMR models showed a significant positive association with DNAmPhenoAge acceleration with chemical exposures, in which the effect of di-n-butyl phthalate exposure showed the greatest importance. Conclusion: These findings suggest that exposure to a mixture of airborne chemicals significantly increase the acceleration of the epigenetic biomarker of phenotypic age. These findings serve to identify toxic chemicals in the air and facilitate the evaluation of their potentially severe health effects.

Background: Short-term fine particulate matter (PM2.5) exposure and increased circulatory mortality have been well documented. However, there are inconsistent findings on mortality effects of traffic-related pollutants from the perspective of sources or constituents. Few studies have examined such associations using source and constituents simultaneously, and even less are based on large-scale, nationally representative data. We aimed to conduct a comprehensive analysis to investigate source- and constituent-specific mortality effects due to traffic-related PM2.5 pollution in China. Methods: We extracted daily mortality data in 280 counties from the China Disease Surveillance Points system (DSPs) from January 2013 to December 2018. Daily concentrations of traffic-related PM2.5 and specific constituents were simulated using the Community Multiscale Air Quality (CMAQ) model. The downscaling and adjustment methods were carried out to generate a refined exposure assessment. We estimated the circulatory mortality risk using a standard two-stage approach, combining generalized linear model (GLM) with a quasi-Poisson distribution and random-effects meta-analysis. Results: We observed that traffic-related PM2.5 and specific constituents were significantly associated with increased circulatory mortality. An increase of interquartile range of traffic-related PM2.5, elemental carbon (EC), organic carbon (OC), and nitrate (NO3−) were associated with elevated circulatory mortality risks of 1.80 % (95 % confidence interval, CI: 1.27, 2.33), 1.85 % (1.33, 2.37), 1.42 % (0.90, 1.94), and 1.10 % (0.55, 1.66) at 3-day moving average (lag 0-2 days), respectively. We also found relatively high associations between traffic-related PM2.5 and EC exposures and cardiovascular mortality, and OC exposure and cerebrovascular mortality. Moreover, our stratified analysis demonstrated such mortality risks tended to be stronger in males, individuals age 65 years or older, and during the cold season. Conclusion: Our findings provided robust evidence on significant associations of traffic-related PM2.5 and specific constituents with circulatory mortality. Further emissions abatement from the transportation sector and corresponding pollutants should merit a particular focus in China.

Projecting future air pollution and related health burdens remains challenging because of the complex interactions among future emissions, population, and climate change. In this study, we estimated the premature deaths attributed to ambient fine particulate matter (PM2.5) and ozone (O3) from 2015 to 2100 under four socioeconomic climate scenarios based on an age-stratified assessment method. We found that PM2.5 will decrease in all shared socioeconomic pathway (SSP) scenarios and O3 will decrease in the SSP1-2.6 and SSP2-4.5 scenarios, contributing to a decrease in premature mortality together with the declining total population in China. However, the benefits of a decline in population size and PM2.5 and O3 concentrations over time will be largely offset by population aging, and premature death caused by PM2.5 and O3 will continue to rise till 2060–2080. This impact was greater for the O3-related deaths than those for PM2.5. Our study highlights the importance of future prevention strategies that must jointly improve air quality and susceptibility to aging.

Background: Evidence for the association between long-term exposure to ozone (O3) and cause-specific cardiovascular disease (CVD) mortality is inconclusive, and this association has rarely been evaluated at high O3 concentrations. Objectives: We aim to evaluate the associations between long-term O3 exposure and cause-specific CVD mortality in a Chinese population. Methods: From 2009 to 2018, 744,882 subjects (median follow-up of 7.72 years) were included in the CHinese Electronic health Records Research in Yinzhou (CHERRY) study. The annual average concentrations of O3 and fine particulate matter (PM2.5), which were estimated using grids with a resolution up to 1 × 1 km, were assigned to the community address for each subject. The outcomes were deaths from CVD, ischemic heart disease (IHD), myocardial infarction (MI), stroke, and hemorrhagic/ischemic stroke. Time-varying Cox model adjusted for PM2.5 and individual-level covariates was used. Results: The mean of annual average O3 concentrations was 68.05 μg/m3. The adjusted hazard ratio per 10 μg/m3 O3 increase was 1.22 (95% confidence interval [CI]: 1.13–1.33) for overall CVD mortality, 1.08 (0.91–1.29) for IHD, 1.21 (0.90–1.63) for MI, 1.28 (1.15–1.43) for overall stroke, 1.39 (1.16–1.67) for hemorrhagic stroke and 1.22 (1.00–1.49) for ischemic stroke, respectively. The study showed that subjects without hypertension had a higher risk for CVD mortality associated with long-term O3 exposure (1.66 vs. 1.15, p = 0.01). Conclusions: We observed the association between long-term exposure to high O3 concentrations and cause-specific CVD mortality in China, independent of PM2.5 and other CVD risk factors. This suggested an urgent need to control O3 pollution, especially in developing countries.

The exposome overhauls conventional environmental health impact research paradigms and provides a novel methodological framework that comprehensively addresses the complex, highly dynamic interplays of exogenous exposures, endogenous exposures, and modifiable factors in humans. Holistic assessments of the adverse health effects and systematic elucidation of the mechanisms underlying environmental exposures are major scientific challenges with widespread societal implications. However, to date, few studies have comprehensively and simultaneously measured airborne pollutant exposures and explored the associated biomarkers in susceptible healthy elderly subjects, potentially resulting in the suboptimal assessment and management of health risks. To demonstrate the exposome paradigm, we describe the rationale and design of a comprehensive biomarker and biomonitoring panel study to systematically explore the association between individual airborne exposure and adverse health outcomes. We used a combination of personal monitoring for airborne pollutants, extensive human biomonitoring, advanced omics analysis, confounding information, and statistical methods. We established an exploratory panel study of Biomarkers of Air Pollutant Exposure in Chinese people aged 60–69 years (China BAPE), which included 76 healthy residents from a representative community in Jinan City, Shandong Province. During the period between September 2018 and January 2019, we conducted prospective longitudinal monitoring with a 3-day assessment every month. This project: (1) leveraged advanced tools for personal airborne exposure monitoring (external exposures); (2) comprehensively characterized biological samples for exogenous and endogenous compounds (e.g., targeted and untargeted monitoring) and multi-omics scale measurements to explore potential biomarkers and putative toxicity pathways; and (3) systematically evaluated the relationships between personal exposure to air pollutants, and novel biomarkers of exposures and effects using exposome-wide association study approaches. These findings will contribute to our understanding of the mechanisms underlying the adverse health impacts of air pollution exposures and identify potential adverse clinical outcomes that can facilitate the development of effective prevention and targeted intervention techniques.

The purpose of this work was to assess population vulnerability to heat-related health risks and its relationship with urbanization levels to provide essential information for the future development and policy-making for climate change adaptation. We constructed a heat vulnerability index (HVI), quantified the population heat vulnerability in each county across China by a principal component analysis (PCA) of multiple factors, and assessed urbanization levels in each county using multisource data. Then, the HVI was validated using the heat-attributable fraction (heat-AF) of nonaccidental mortality based on death monitoring data and meteorological data from 95 counties across China. The results showed that our HVI was significantly positively associated with the heat AF of nonaccidental mortality. A negative correlation was observed between the urbanization level and the HVI. The HVI was generally higher in less urbanized western China and lower in the more urbanized eastern regions. The baseline mortality occupies the top position in the importance ranking of the heat-vulnerability indicators at all three urbanization levels, but the other indicators, including the aging rate, agricultural population rate, education, ethnic structure, economic status, air conditioner ownership rate, and number of hospitals, ranked differently among different urbanization levels. This finding indicates that to reduce population heat vulnerability, the most important approach is to improve the health status of the whole population and reduce baseline mortality; additionally, regional-specific measures and emphasis should be adjusted reasonably along with the process of urbanization according to the characteristics and key factors of local heat vulnerability.

Studies worldwide have estimated the number of deaths attributable to long-term exposure to fine airborne particles (PM2.5), but limited information is available on short-term exposure, particularly in China. In addition, most existing studies have assumed that short-term PM2.5-mortality associations were linear. For this reason, the use of linear exposure-response functions for calculating disease burden of short-term exposure to PM2.5 in China may not be appropriate. There is an urgent need for a comprehensive, evidence-based assessment of the disease burden related to short-term PM2.5 exposure in China. Here, we explored the non-linear association between short-term PM2.5 exposure and all-cause mortality in 104 counties in China; estimated county-specific mortality burdens attributable to short-term PM2.5 exposure for all counties in the country and analyzed spatial characteristics of the mortality burden due to short-term PM2.5 exposure in China. The pooled PM2.5-mortality association was non-linear, with a reversed J-shape. We found an approximately linear increased risk of mortality from 0 to 62 μg/m3 and decreased risk from 62 to 250 μg/m3. We estimated a total of 169,862 additional deaths from short-term PM2.5 exposure throughout China in 2015. Models using linear exposure-response functions for the PM2.5-mortality association estimated 32,186 deaths attributable to PM2.5 exposure, which is 5.3 times lower than estimates from the non-linear effect model. Short-term PM2.5 exposure contributed greatly to the death burden in China, approximately one seventh of the estimates from the chronic effect. It is essential and crucial to incorporate short-term PM2.5-related mortality estimations when considering the disease burden attributable to PM2.5 in developing countries such as China. Traditional linear effect models likely underestimated the mortality burden due to short-term exposure to PM2.5. Keywords: PM2.5, Short-term, Mortality burden, Non-linear

Background: Several epidemiological studies have shown that blood pressure changes with temperature based on the daily temperature and linear relationship assumption. However, little is known about the true curve shape of the relationship between temperature and blood pressure. Objectives: The objective of this study was to investigate the non-linear relationship between hourly temperature and blood pressure. Methods: This is a prospective panel study comprising 100 participants in Suzhou, China. The blood pressure of each participant was measured >50 times between October 2013 and January 2016. Hourly temperature data were derived from the nearest monitoring station owned by the China Meteorological Administration. A Distributed Lag Nonlinear Model (DLNM) was used to investigate the relationship between hourly temperature and blood pressure. Results: We found that the relationship between hourly temperature and blood pressure was parabolic. Short-term exposure to hourly temperatures had significant cold and heat effects on systolic blood pressure (SBP), diastolic blood pressure (DBP), pulse pressure (PP), and mean arterial pressure (MAP). The hourly temperature had a significant lag effect on blood pressure, with a lag time of 0–5 h. Alcohol users were more sensitive to the cold effects of hourly temperature, and the diabetic population was more sensitive to the heat effects of hourly temperature on PP. Conclusion: Temperature imparts short-term effects on blood pressure. Therefore, timely protective measures during cold waves or cold weather are beneficial to maintain stable blood pressure levels to reduce the risk of blood pressure related diseases. Keywords: Hourly temperature, Blood pressure, Panel study, Repeated measurement

Personal exposure to air pollution is affected by its concentration in the microenvironment and individual time-activity patterns. To investigate personal black carbon (BC) exposure levels and identify their potential determinants, we conducted a panel study among 67 elderly residents aged 60–69 years in Jinan, China. Personal BC exposure was measured using portable real-time monitors, while corresponding ambient BC concentrations and meteorological conditions were also collected from the local central site. Time-activity and household characteristics were recorded. A linear mixed-effects model was used to identify potential determinants of personal BC exposure. The daily average personal BC exposure concentration was 4.1 ± 2.0 μg/m3 (±standard deviation, SD), which was significantly lower than the ambient concentration (4.6 ± 2.5 μg/m3) (p

High spatiotemporal resolution fine particulate matter (PM2.5) simulations can provide important exposure data for the assessment of long-term and short-term health effects. Satellite-based aerosol optical depth (AOD) data, meteorological data, and topographic data have become key variables for PM2.5 estimation. In this study, a random forest model was developed and used to estimate the highest resolution (0.01° × 0.01°) daily PM2.5 concentrations in the Beijing-Tianjin-Hebei region. Our model had a suitable performance (cv-R2 = 0.83 and test-R2 = 0.86). The regional test-R2 value in southern Beijing-Tianjin-Hebei was higher than that in northern Beijing-Tianjin-Hebei. The model performance was excellent at medium to high PM2.5 concentrations. Our study considered meteorological lag effects and found that the boundary layer height of the one-day lag had the most important contribution to the model. AOD and elevation factors were also important factors in the modeling process. High spatiotemporal resolution PM2.5 concentrations in 2010–2016 were estimated using a random forest model, which was based on PM2.5 measurements from 2013 to 2016. Keywords: PM2.5 estimation, Human exposure, High spatiotemporal resolution, Machine learning

Background: Particle air pollution has adverse health effects, and low-cost monitoring among a large population group is an effective method for performing environmental health studies. However, concern about the accuracy of low-cost monitors has affected their popularization in monitoring projects. Objective: To calibrate a low-cost particle monitor (HK-B3, Hike, China) through a controlled exposure experiment. Methods: Our study used a MicroPEM monitor (RTI, America) as a standard particle concentration measurement device to calibrate the Hike monitors. A machine learning model was established to calibrate the particle concentration obtained by the low-cost PM2.5 monitors, and ten-fold validation was used to test the model. In addition, we used a linear regression model to compare the results of the machine learning model. A calibration method was established for the low-cost monitors, and it can be used to apply the monitors in future air pollution monitoring projects. Results: The values of the random forest model calibration results and observations were more condensed around the regression line y = 0.99x + 0.05, and the R squared value (R2 = 0.98) was higher than that for the linear regression (R2 = 0.87). The random forest model showed better performance than the traditional linear regression model. Conclusions: Our study provided an effective calibration method to support the accuracy of low-cost monitors. The machine learning method based on the calibration model established in our study can increase the effectiveness of future air pollution and health studies. Keywords: PM2.5, Low-cost, Monitor, Calibration, Random forest model