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Vital Surveillances: Interactive Effects Between Temperature and PM2.5 on Mortality: A Study of Varying Coefficient Distributed Lag Model — Guangzhou, Guangdong Province, China, 2013–2020

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  • Abstract

    Introduction

    There is a large body of epidemiological evidence showing significantly increased mortality risks from air pollution and temperature. However, findings on the modification of the association between air pollution and mortality by temperature are mixed.

    Methods

    We used a varying coefficient distributed lag model to assess the complex interplay between air temperature and PM2.5 on daily mortality in Guangzhou City from 2013 to 2020, with the aim of establishing the PM2.5-mortality association at different temperatures and exploring synergetic mortality risks from PM2.5 and temperature on vulnerable populations.

    Results

    We observed near-linear concentration-response associations between PM2.5 and mortality across different temperature levels. Each 10 μg/m³ increase of PM2.5 in low, medium, and high temperature strata was associated with increments of 0.73% [95% confidence interval (CI): 0.38%, 1.09%], 0.12% (95% CI: −0.27%, 0.52%), and 0.46% (95% CI: 0.11%, 0.81%) in non-accidental mortality, with a statistically significant difference between low and medium temperatures (P=0.02). There were significant modification effects of PM2.5 by low temperature for cardiovascular mortality and among individuals 75 years or older.

    Conclusions

    Low temperatures may exacerbate physiological responses to short-term PM2.5 exposure in Guangzhou, China.

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  • Funding: Supported by the National Natural Science Foundation of China (No. 82003552), and the Guangdong Basic and Applied Basic Research Foundation (No. 2020A1515011161, 2021A1515110625, 2020A1414010168)
  • [1] Institute for Health Metrics and Evaluation. Ambient particulate matter pollution — Level 4 risk. https://www.healthdata.org/results/gbd_summaries/2019/ambient-particulate-matter-pollution-level-4-risk. [2022-05-24]https://www.healthdata.org/results/gbd_summaries/2019/ambient-particulate-matter-pollution-level-4-risk
    [2] Yang J, Zhou MG, Ren ZP, Li MM, Wang BG, Liu DL, et al. Projecting heat-related excess mortality under climate change scenarios in China. Nat Commun 2021;12(1):1039. http://dx.doi.org/10.1038/s41467-021-21305-1CrossRef
    [3] Murray CJL, Aravkin AY, Zheng P, Abbafati C, Abbas KM, Abbasi-Kangevari M, et al. Global burden of 87 risk factors in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet 2020;396(10258):1223-49. http://dx.doi.org/10.1016/S0140-6736(20)30752-2CrossRef
    [4] Cheng H, Zhu FR, Lei RQ, Shen CW, Liu J, Yang M, et al. Associations of ambient PM2.5 and O3 with cardiovascular mortality: a time-series study in Hefei, China. Int J Biometeorol 2019;63(10):1437-47. http://dx.doi.org/10.1007/s00484-019-01766-2CrossRef
    [5] Li J, Woodward A, Hou XY, Zhu T, Zhang JL, Brown H, et al. Modification of the effects of air pollutants on mortality by temperature: a systematic review and meta-analysis. Sci Total Environ 2017;575:1556-70. http://dx.doi.org/10.1016/j.scitotenv.2016.10.070CrossRef
    [6] Qiu H, Tan K, Long FY, Wang LY, Yu HY, Deng R, et al. The burden of COPD morbidity attributable to the interaction between ambient air pollution and temperature in Chengdu, China. Int J Environ ResPublic Health 2018;15(3):492. http://dx.doi.org/10.3390/ijerph15030492CrossRef
    [7] Jhun I, Fann N, Zanobetti A, Hubbell B. Effect modification of ozone-related mortality risks by temperature in 97 US cities. Environ Int 2014;73:128-34. http://dx.doi.org/10.1016/j.envint.2014.07.009CrossRef
    [8] Zhao X, Chen F, Feng ZJ, Li XS, Zhou XH. Characterizing the effect of temperature fluctuation on the incidence of malaria: an epidemiological study in south-west China using the varying coefficient distributed lag non-linear model. Malar J 2014;13:192. http://dx.doi.org/10.1186/1475-2875-13-192CrossRef
    [9] Gasparrini A, Guo YM, Hashizume M, Kinney PL, Petkova EP, Lavigne E, et al. Temporal variation in heat-mortality associations: a multicountry study. Environ Health Perspect 2015;123(11):1200-7. http://dx.doi.org/10.1289/ehp.1409070CrossRef
    [10] Li Y, Ma ZQ, Zheng CJ, Shang Y. Ambient temperature enhanced acute cardiovascular-respiratory mortality effects of PM2.5 in Beijing, China. Int J Biometeorol 2015;59(12):1761-70. http://dx.doi.org/10.1007/s00484-015-0984-zCrossRef
    [11] Sun SZ, Cao PH, Chan KP, Tsang H, Wong CM, Thach TQ. Temperature as a modifier of the effects of fine particulate matter on acute mortality in Hong Kong. Environ Pollut 2015;205:357-64. http://dx.doi.org/10.1016/j.envpol.2015.06.007CrossRef
    [12] Jiang YX, Chen RJ, Kan HD. The interaction of ambient temperature and air pollution in China. In: Lin HL, Ma WJ, Liu QY, editors. Ambient temperature and health. Singapore: Springer. 2019; p. 105-16. http://dx.doi.org/10.1007/978-981-13-2583-0_7CrossRef
    [13] Yang J, Yin P, Zhou MG, Ou CQ, Guo YM, Gasparrini A, et al. Cardiovascular mortality risk attributable to ambient temperature in China. Heart 2015;101(24):1966-72. http://dx.doi.org/10.1136/heartjnl-2015-308062CrossRef
    [14] Deng LJ, Ma P, Wu Y, Ma YS, Yang X, Li YG, et al. High and low temperatures aggravate airway inflammation of asthma: evidence in a mouse model. Environ Pollut 2020;256:113433. http://dx.doi.org/10.1016/j.envpol.2019.113433CrossRef
    [15] Qian ZM, He QC, Lin HM, Kong LL, Bentley CM, Liu WS, et al. High temperatures enhanced acute mortality effects of ambient particle pollution in the "oven" city of Wuhan, China. Environ Health Perspect 2008;116(9):1172-8. http://dx.doi.org/10.1289/ehp.10847CrossRef
    [16] Simoni M, Baldacci S, Maio S, Cerrai S, Sarno G, Viegi G. Adverse effects of outdoor pollution in the elderly. J Thorac Dis 2015;7(1):34-45. http://dx.doi.org/10.3978/j.issn.2072-1439.2014.12.10CrossRef
  • FIGURE 1.  RR (95% CI) of mortality associated with 10 μg/m3 increase of PM2.5 by a time lag of 0–7 days.

    Note: dots and vertical lines represent point estimates and 95% confidence intervals of PM2.5 at individual lag days.

    Abbreviations: RR=relative risk; IHD=ischemic heart disease; COPD=chronic obstructive pulmonary disease; CI=confidence interval.

    FIGURE 2.  Concentration-response associations between PM2.5 and mortality under different temperature conditions.

    Abbreviations: RR=relative risk; IHD=ischemic heart disease; COPD=chronic obstructive pulmonary disease.

    TABLE 1.  Summary statistics for daily weather conditions, air pollution, and mortality in Guangzhou, 2013–2018.

    VariableMeanMinimumPercentilesMaximum
    25th50th75th
    Temperature (℃)22.23.417.423.327.332.0
     Low (<25th)13.64.611.814.015.817.7
     Medium (25th–75th)23.117.820.723.325.727.3
     High (>75th)28.927.427.928.829 .631.9
    Mean humidity (%)80.431.075.081.588.0100.0
    Mean pressure (hPa)1,007.1985.71,000.31,005.41,010.83,276.6
    PM2.5 (μg/m³)35.13.520.030.045.0150.0
    Cause (Number of deaths per day)
     Non-accidental13179115128143238
     Cardiovascular disease5521455362115
     Ischemic heart disease23618222751
     Stroke14011141734
     Respiratory disease20615192448
     COPD80681130
    Abbreviation: COPD=chronic obstructive pulmonary disease.
    Download: CSV

    TABLE 2.  Cumulative (lag 0–4 days) mortality risk of each 10 μg/m³ increase in PM2.5 at different temperature strata (ER, 95% CI).

    VariableLow temperatureMedium temperatureHigh temperature
    ER%95% CIER%95% CIER%95% CI
    Non-accidental mortality0.73* (0.38, 1.09)*0.12 (−0.27, 0.52)0.46* (0.11, 0.81)*
    Cardiovascular mortality0.88* (0.37, 1.39)*0.04 (−0.52, 0.60)0.50* (0.00, 0.99)*
    Stroke mortality1.35* (0.43, 2.29)*0.64 (−0.38, 1.67)1.10* (0.20, 2.02)*
    Ischemic heart mortality0.50 (−0.25, 1.25)−0.52 (−1.33, 0.31)−0.02 (−0.64, 0.77)
    Respiratory mortality1.57* (0.75, 2.39)*0.85 (−0.04, 1.76)1.24* (0.45, 2.05)*
    COPD mortality1.34* (0.10, 2.59)*0.69 (−0.67, 2.07)0.95 (−0.26, 2.17)
    Gender
     Female0.87* (0.37, 1.37)*0.04 (−0.51, 0.60)0.50* (0.01, 1.00)*
     Male0.63* (0.19, 1.07)*0.18 (−0.30, 0.67)0.43* (0.00, 0.86)*
    Age (years)
     0–740.01 (−0.48, 0.50)−0.13 (−0.68, 0.41)−0.09 (−0.57, 0.39)
     ≥751.22* (0.76, 1.68)*0.29 (−0.22, 0.79)0.83* (0.38, 1.28)*
    Education
     Low education0.69* (0.23, 1.15)*−0.04 (−0.56, 0.48)0.40 (−0.05, 0.86)
     High education0.55 (−0.24, 1.35)0.32 (−0.56, 1.22)0.32 (−0.43, 1.14)
    Abbreviations: ER=excess risk; CI=confidence interval; COPD=chronic obstructive pulmonary disease.
    * indicates statistically significant results.
    Download: CSV

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Interactive Effects Between Temperature and PM2.5 on Mortality: A Study of Varying Coefficient Distributed Lag Model — Guangzhou, Guangdong Province, China, 2013–2020

View author affiliations

Abstract

Introduction

There is a large body of epidemiological evidence showing significantly increased mortality risks from air pollution and temperature. However, findings on the modification of the association between air pollution and mortality by temperature are mixed.

Methods

We used a varying coefficient distributed lag model to assess the complex interplay between air temperature and PM2.5 on daily mortality in Guangzhou City from 2013 to 2020, with the aim of establishing the PM2.5-mortality association at different temperatures and exploring synergetic mortality risks from PM2.5 and temperature on vulnerable populations.

Results

We observed near-linear concentration-response associations between PM2.5 and mortality across different temperature levels. Each 10 μg/m³ increase of PM2.5 in low, medium, and high temperature strata was associated with increments of 0.73% [95% confidence interval (CI): 0.38%, 1.09%], 0.12% (95% CI: −0.27%, 0.52%), and 0.46% (95% CI: 0.11%, 0.81%) in non-accidental mortality, with a statistically significant difference between low and medium temperatures (P=0.02). There were significant modification effects of PM2.5 by low temperature for cardiovascular mortality and among individuals 75 years or older.

Conclusions

Low temperatures may exacerbate physiological responses to short-term PM2.5 exposure in Guangzhou, China.

  • 1. Institute for Environmental and Climate Research, Jinan University, Guangzhou, Guangdong Province, China
  • 2. Guangzhou Center for Disease Control and Prevention, Guangzhou, Guangdong Province, China
  • 3. Department of Cancer Prevention, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong Province, China
  • 4. National Center for Chronic and Noncommunicable Disease Control and Prevention, Beijing, China
  • 5. School of Public Health, Guangzhou Medical University, Guangzhou, Guangdong Province, China
  • Corresponding authors:

    Jun Yang, yangjun@gzhmu.edu.cn

    Guozhen Lin, xwkgzcdc@126.com

  • Funding: Supported by the National Natural Science Foundation of China (No. 82003552), and the Guangdong Basic and Applied Basic Research Foundation (No. 2020A1515011161, 2021A1515110625, 2020A1414010168)
  • Online Date: July 01 2022
    Issue Date: July 01 2022
    doi: 10.46234/ccdcw2022.124
    • Ambient air pollution and temperature are leading environmental challenges to global public health. In 2019, PM2.5 was responsible for an estimated 4.14 million deaths and 118 million disability-adjusted life years (DALYs) (1). Temperature is an important predictor of many diseases and has been perceived as a key environmental factor in climate change scenarios (2). Air pollution was identified as the fourth leading risk factor for death worldwide (3). Short-term exposure to PM2.5 can increase the risk of death from chronic diseases (4).

      In the context of climate change, health risk assessment of the joint effect of air pollution and temperature has attracted growing public concern (5). In Chengdu, China for example, stronger associations between air pollution and hospital admission for chronic obstructive pulmonary disease (COPD) were found at low-temperatures than at moderate temperatures (6). However, other studies have failed to identify synergetic health effects of air pollution and temperature. For example, Jhun and co-authors found that the interaction between ozone and temperature was not statistically significant in 97 US cities (7). In addition, potential variations of exposure-response patterns under various temperature levels have been less well documented. As an extension of distributed lag models, the varying-coefficient distributed lag model has been flexibly applied to explore interactive and time-lagged effects between different exposure hazards (8).

      We aimed to establish the exposure-response association between PM2.5 and mortality at different temperature strata using the varying coefficient distributed lag model in Guangzhou, China, and to explore synergetic mortality risks from PM2.5 and temperature on vulnerable populations.

    • The study period was 2013–2020. We obtained daily mortality data in Guangzhou from Guangzhou Center for Disease Control and Prevention. Causes of death were classified according to International Classification of Diseases, Tenth Revision: non-accidental causes (A00–R99), cardiovascular disease (I00–I99), ischemic heart disease (IHD, I20–I25), stroke (I60–I69), respiratory disease (J00–J98), and COPD (J40–J47). Daily counts of non-accidental deaths were stratified by age (<75 and ≥75 years), gender, and educational level (≤9 and >9 years). We obtained daily concentrations of air pollutants (O3, PM2.5, PM10, NO2, SO2, and CO) from Guangzhou monitoring stations and daily meteorological data from basic weather stations in Guangzhou from the China Meteorological Data Service Center (http://data.cma.cn/).

      The varying coefficient distributed lag model, based on generalized linear models with a quasi-Poisson family (9), was used to estimate the modifying effect of temperature on the association between PM2.5 and mortality. We incorporated several covariates in the model: a natural cubic spline with 7 degrees of freedom (df) per year for a time variable; a natural cubic spline with 3 df for relative humidity, air pressure, and moving average temperature (with time lags of 0–10 days); and holidays and day of the week as indicator variables. The cross-product of categorical temperature levels [low (<25th percentile), medium (25th–75th), and high (>75th percentile)] and PM2.5 was used to examine the interaction between air pollution and temperature. In addition, stratified analyses were conducted by gender, age group, and education. Relative differences of RRs across strata [relative risk ratios (RRR)] were calculated to detect potential effect modifications by temperature. To verify the robustness of our results, we performed a series of sensitivity analyses. Details of the model are provided in the Supplementary Material. All statistical analyses were conducted in the R language environment (R Core Team 2021, Vienna, Austria) using the “dlnm”, “mgcv”, and “splines” packages.

    • Table 1 depicts summary statistics on daily air pollution, weather conditions, and mortality. The average PM2.5 value was 35.1 μg/m3 during 2013–2020. During the study period, there were 403,492 deaths registered in Guangzhou, among which cardiovascular diseases, IHD, stroke, respiratory disease, and COPD accounted for 39.5%, 16.7%, 10.3%, 14.4%, and 6.1%, respectively.

      VariableMeanMinimumPercentilesMaximum
      25th50th75th
      Temperature (℃)22.23.417.423.327.332.0
       Low (<25th)13.64.611.814.015.817.7
       Medium (25th–75th)23.117.820.723.325.727.3
       High (>75th)28.927.427.928.829 .631.9
      Mean humidity (%)80.431.075.081.588.0100.0
      Mean pressure (hPa)1,007.1985.71,000.31,005.41,010.83,276.6
      PM2.5 (μg/m³)35.13.520.030.045.0150.0
      Cause (Number of deaths per day)
       Non-accidental13179115128143238
       Cardiovascular disease5521455362115
       Ischemic heart disease23618222751
       Stroke14011141734
       Respiratory disease20615192448
       COPD80681130
      Abbreviation: COPD=chronic obstructive pulmonary disease.

      Table 1.  Summary statistics for daily weather conditions, air pollution, and mortality in Guangzhou, 2013–2018.

      Supplementary Figure S1 shows Spearman’s correlations between air pollution and weather conditions. There were negative correlations between temperature and relative humidity and air pollutants (except for O3) and positive correlations among air pollutants.

      Figure 1 shows lag patterns of PM2.5 on cause-specific mortality at different temperature levels. Effect of PM2.5 on the daily death toll of different diseases had consistent and evident trends in which mortality risks reached maximum within 1–2 lag days of exposure, then leveled off, and disappeared within 4–5 days.

      Figure 1. 

      RR (95% CI) of mortality associated with 10 μg/m3 increase of PM2.5 by a time lag of 0–7 days.

      Note: dots and vertical lines represent point estimates and 95% confidence intervals of PM2.5 at individual lag days.

      Abbreviations: RR=relative risk; IHD=ischemic heart disease; COPD=chronic obstructive pulmonary disease; CI=confidence interval.

      Figure 2 shows the estimates of exposure-response relationships between PM2.5 and mortality at different temperature levels. We found approximately linear associations between PM2.5 and mortality. The highest effect estimates of PM2.5 on mortality were consistently observed at the lower temperatures, while lower effect estimates were seen at the higher temperatures. Each 10 μg/m³ increase of PM2.5 in low, medium, and high temperature strata was associated with respective increments of 0.73% [95% confidence interval (CI): 0.38%, 1.09%], 0.12% (95% CI: −0.27%, 0.52%), and 0.46% (95% CI: 0.11%, 0.81%) in non-accidental mortality (Table 2). There was an RRR of 1.01 (95% CI: 1.00, 1.01) between low and medium temperatures (P=0.02) (Supplementary Table S1). For cause-specific mortality, statistically significant differences between the risk of PM2.5 across temperature levels were only observed for cardiovascular mortality, with effect estimates of 0.88% (95% CI: 0.37%, 1.39%), 0.04% (95% CI: −0.52%, 0.60%) and 0.50% (95% CI: 0.00%, 0.99%) at low, medium and high temperature levels (Table 2), and an RRR of 1.01 (95% CI: 1.00, 1.02) between low temperature and medium temperature (P=0.03). The highest effect of PM2.5 was found in respiratory mortality at low temperatures, with an effect estimate of 1.57% (95% CI: 0.75%, 2.39%); however, difference by temperature was not statistically significant.

      Figure 2. 

      Concentration-response associations between PM2.5 and mortality under different temperature conditions.

      Abbreviations: RR=relative risk; IHD=ischemic heart disease; COPD=chronic obstructive pulmonary disease.
      VariableLow temperatureMedium temperatureHigh temperature
      ER%95% CIER%95% CIER%95% CI
      Non-accidental mortality0.73* (0.38, 1.09)*0.12 (−0.27, 0.52)0.46* (0.11, 0.81)*
      Cardiovascular mortality0.88* (0.37, 1.39)*0.04 (−0.52, 0.60)0.50* (0.00, 0.99)*
      Stroke mortality1.35* (0.43, 2.29)*0.64 (−0.38, 1.67)1.10* (0.20, 2.02)*
      Ischemic heart mortality0.50 (−0.25, 1.25)−0.52 (−1.33, 0.31)−0.02 (−0.64, 0.77)
      Respiratory mortality1.57* (0.75, 2.39)*0.85 (−0.04, 1.76)1.24* (0.45, 2.05)*
      COPD mortality1.34* (0.10, 2.59)*0.69 (−0.67, 2.07)0.95 (−0.26, 2.17)
      Gender
       Female0.87* (0.37, 1.37)*0.04 (−0.51, 0.60)0.50* (0.01, 1.00)*
       Male0.63* (0.19, 1.07)*0.18 (−0.30, 0.67)0.43* (0.00, 0.86)*
      Age (years)
       0–740.01 (−0.48, 0.50)−0.13 (−0.68, 0.41)−0.09 (−0.57, 0.39)
       ≥751.22* (0.76, 1.68)*0.29 (−0.22, 0.79)0.83* (0.38, 1.28)*
      Education
       Low education0.69* (0.23, 1.15)*−0.04 (−0.56, 0.48)0.40 (−0.05, 0.86)
       High education0.55 (−0.24, 1.35)0.32 (−0.56, 1.22)0.32 (−0.43, 1.14)
      Abbreviations: ER=excess risk; CI=confidence interval; COPD=chronic obstructive pulmonary disease.
      * indicates statistically significant results.

      Table 2.  Cumulative (lag 0–4 days) mortality risk of each 10 μg/m³ increase in PM2.5 at different temperature strata (ER, 95% CI).

      In analyses stratified by personal characteristics, we found consistently higher effects of PM2.5 at low temperatures compared with medium temperatures, but the only statistically significant difference was among individuals of 75 years or older. Each 10 μg/m³ increase of PM2.5 in the low, medium, and high temperature strata was associated with increments of 1.22% (95% CI: 0.76%, 1.68%), 0.29% (95% CI: −0.22%, 0.79%), and 0.83% (95% CI: 0.38%, 1.28%) in mortality of the elderly, respectively, with RRR of 1.01 (95% CI: 1.00, 1.02) between low and medium temperature strata (P=0.01). The elderly were more susceptible to PM2.5 compared with younger age groups under both low and high temperature conditions.

      Using different degrees of freedom for time trend analyses adjusting for co-pollutants changed the effect estimates only slightly (Supplementary Tables S2S3), indicating robustness of our main results. Using different temperature cutoffs (Supplementary Table S4) and different PM2.5 time-lags (Supplementary Table S5) did not remarkably change the estimates of temperature-stratified air pollution effects on mortality.

    • To the best of our knowledge, this is one of the few studies exploring exposure-response associations between air pollution and mortality under different temperature conditions. Our study consistently observed greater mortality risks from PM2.5 in lower temperatures than in moderate temperatures across different causes of death. Interaction effects between PM2.5 and low temperatures were more pronounced in the elderly than in younger people.

      We observed the highest effect of PM2.5 on mortality in low temperature strata compared with high and medium temperature strata. Low temperatures have consistently been found to enhance the effect of PM2.5 on cardiovascular mortality in Beijing (10), natural and respiratory mortality in Hong Kong (11), and COPD mortality in Chengdu (6). For instance, Li and coauthors found that each 10 μg/m³ increment of PM2.5 during the lowest temperature range was associated with a 1.27% (95% CI: 0.38%, 2.17%) increase in cardiovascular mortality, compared with 0.59% (95% CI: 0.22%, 1.16%) across the whole temperature range (10). Likewise, the association between PM2.5 and mortality in Hong Kong was stronger at low temperatures than at higher temperatures, with corresponding effect estimates of 0.94% (0.95% CI: 0.65%, 1.24%) and 0.47% (95% CI: 0.65%, 1.24%) for each 10 μg/m³ increment in PM2.5 (11). The reduced beat frequency of nose and trachea cilia on cold days, which affects the clearance rate of particulate matter and makes people more susceptible to PM2.5, is suspected as an underlying mechanism for the greater effect of PM2.5 on mortality at low temperatures in Guangzhou (12). Some studies found that people living in warm regions probably experience a higher mortality risk during cold weather than do people living in cold regions (13). In addition, low temperatures may exacerbate airway inflammation and increase the burden on respiratory functions (14).

      We also found relatively higher effect estimates of PM2.5 on mortality in high temperatures compared to moderate temperatures, although the difference was not statistically significant, consistent with previous studies (6,10). However, another study reported a statistically significant higher health effect of PM2.5 in high temperature strata (15). The discrepant results may be explained by differences in population structure and air pollution exposure patterns.

      In this study, we observed a significant modification of the effect of PM2.5 on cardiovascular mortality by low temperatures. As ambient temperature decreases, cold receptors in the skin are stimulated, the sympathetic nervous system increases catecholamine levels, blood vessels near the skin constrict to reduce heat loss, and blood pressure suddenly increases (10). High blood pressure can lead to oxygen deficiency, myocardial ischemia, or arrhythmia, and become a risk factor for vascular spasms and ruptures of atherosclerotic plaque that cause thromboses (12). Such marked changes make people more susceptible to adverse cardiovascular outcomes caused by PM2.5. The findings are important from a public health perspective, as 39.5% of all non-accidental deaths in Guangzhou were cardiovascular deaths.

      Our analysis also found significant interaction effects of PM2.5 and low temperature among the elderly but not among young people, which is consistent with a previous study (6). The body’s homeostasis and thermoregulatory functions, and the capacity to eliminate chemicals from the body decrease with age (16), which may contribute to the combined health hazards of PM2.5 and temperature change. The elderly also suffer from higher rates of comorbidities, which may further enhance their vulnerability to environmental exposure.

      The study was subject to some limitations. First, we substituted measured air pollution and air temperature at fixed outdoor monitoring stations for personal exposures, which will lead to some exposure measurement errors. Second, only adverse associations of PM2.5 were examined in this study, leaving confounding by other factors unexplored. Last, our results may not generalize to areas with different population structures and air pollution compositions.

      In summary, we observed an interaction between PM2.5 and low temperature on mortality, especially for non-accidental and cardiovascular mortality and among the elderly. Considering the synergetic health risks of air pollution and temperature, cooperation from multiple sectors with the aim of protecting vulnerable populations may mitigate health challenges from climate change and air pollution.

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