Skip to main content
NCBI home page
EPA Author Manuscripts logoLink to EPA Author Manuscripts
. Author manuscript; available in PMC: 2021 Dec 16.
Published in final edited form as: J Toxicol Environ Health A. 2020 Oct 5;83(23-24):748–763. doi: 10.1080/15287394.2020.1826375

Peat Smoke Inhalation Alters Blood Pressure, Baroreflex Sensitivity and Cardiac Arrhythmia Risk in Rats

Brandi L Martin 1, Leslie C Thompson 2, Yong Ho Kim 3, Charly King 2, Samantha Snow 2,4,*, Mette Schladweiler 2, Najwa Haykal-Coates 2, Ingrid George 5, M Ian Gilmour 2, Urmila P Kodavanti 2, Mehdi S Hazari 2, Aimen K Farraj 2
PMCID: PMC7682804  NIHMSID: NIHMS1639361  PMID: 33016233

Abstract

Wildland fires (WF) are linked to adverse health impacts related to poor air quality. The cardiovascular impacts of emissions from specific biomass sources, however, are unknown. The purpose of this study was to assess the cardiovascular impacts of a single exposure to peat smoke, a key regional WF air pollution source, and relate these to baroreceptor sensitivity and inflammation. Three-month-old male Wistar-Kyoto rats, implanted with radiotelemeters for continuous monitoring of heart rate (HR), blood pressure (BP), and spontaneous baroreflex sensitivity (BRS), were exposed once, for 1-hr, to filtered air or low (0.38 mg/m3 PM) or high (4.04 mg/m3) concentrations of peat smoke. Systemic markers of inflammation and sensitivity to aconitine-induced cardiac arrhythmia, a measure of latent myocardial vulnerability, were assessed in separate cohorts of rats 24 hr after exposure. PM size (low peat = 0.4 – 0.5 microns vs. high peat = 0.8 – 1.2 microns) and proportion of organic carbon (low peat = 77% vs. high peat = 65%) varied with exposure level. Exposure to high peat and to a lesser extent low peat increased systolic and diastolic BP relative to filtered air. By contrast, only exposure to low peat elevated BRS and aconitine-induced arrhythmogenesis relative to filtered air and increased circulating levels of low-density lipoprotein cholesterol, complement components C3 and C4, angiotensin converting enzyme (ACE), and white blood cells. Taken together, exposure to peat smoke produced overt and latent cardiovascular consequences that were likely influenced by physicochemical characteristics of the smoke and associated adaptive homeostatic mechanisms.

Keywords: wildland fire air pollution, peat biomass smoke, cardiovascular, blood pressure, baroreceptor sensitivity

Introduction

Wildland fire (WF) smoke is an increasingly prominent source of air pollution and has been linked to adverse cardiopulmonary health outcomes (Haikerwal et al. 2015). Due to expected warming and drying conditions, wildland fires are predicted to increase in both frequency and intensity, degrading air quality in more air sheds and potentially affecting more people (US EPA. 2013). Combustion emissions from biomass WF sources consist of a complex mixture of particulate matter (PM), gases, and semivolatile and volatile organic compounds, many of which have been linked to adverse cardiopulmonary health effects (Miranda et al, 2012; Chen and Yang 2018; Liao et al 2011). However, the role of specific WF fire fuel sources and their associated combustion constituents on health from regional WFs needs to be determined.. Further, while the body of epidemiological evidence linking exposure to WF air pollution and adverse clinical outcomes is increasing, only a handful of experimental studies were published to date that describe the cardiopulmonary consequences attributed to exposure.

Peat is a WF biomass source comprised of decaying vegetation and found in wetlands around the world. Unlike forest fires which spread quickly at high temperatures, peat fires take place deeper in the soil, smolder slowly, and may last for months or even years (Rein. 2013). Due to its carbon-rich composition and inherent burn properties, peat fires contribute greater fine PM (PM with an aerodynamic diameter <2.5 microns) than any other type of biomass fuel source (Rein. 2013) and hence are a significant cause for concern. In fact, two separate peat fires in eastern North Carolina (Pocosin Lake in 2008 and Pains Bay in 2011) were associated with elevated number of hospitalizations in affected counties, all attributable to adverse cardiovascular and pulmonary effects (Rappold et al 2011; Tinling et al 2016). In addition, domestic burning of peat in stoves for heating and cooking is a key source of peat-related indoor air pollution (Lin et al. 2017; Wang et al. 2017). Despite the epidemiological evidence linking peat smoke inhalation with adverse health outcomes, the specific biological responses and associated mechanisms driving these effects remain to be examined.

Previously, Martin et al (2018) demonstrated that a single exposure to peat modified systemic responses to a high fat challenge. The purpose of the present study was to (1) characterize the influence of acute peat smoke inhalation in rats on heart rate (HR) and blood pressure (BP), measured using implantable telemetry, (2) determine sensitivity to drug-induced cardiac arrhythmia, and (3) examine systemic inflammation. It was postulated that exposure to peat smoke might initiate concentration-dependent changes in cardiovascular function and that effects might be associated with alterations in baroreceptor sensitivity, a regulatory mechanism responsible for homeostatic control of blood pressure. Previously Hazari et al (2014) showed that exposure to the air pollutant acrolein altered baroreceptor sensitivity (BRS). Thus, the effects of air pollutant exposure were determined on BRS.

Rats were exposed once to filtered air or one of two peat smoke exposure levels containing PM concentrations on par with those reported during peat fires in North Carolina (Tinling et al. 2016) and experienced by firefighters while combating wildland fires (Swiston et al. 2008).

Methods

Animals

Twelve week-old male Wistar Kyoto (WKY) rats, ~250–350 g (Charles River Laboratories Inc., Raleigh, NC) were housed 2/cage in polycarbonate cages, maintained on a 12 hr light/dark cycle at approximately 22°C and 50% relative humidity in our Association for Assessment and Accreditation of Laboratory Animal Care-approved facility, and acclimated for a minimum of one week before exposure. All animals received standard (5001) Purina pellet rat chow (Brentwood, MO) and water ad libitum. The Institutional Animal Care and Use Committee of the U.S. Environmental Protection Agency (U.S. EPA) approved all protocols.

Telemeter Implantation

Animals were anesthetized with ketamine/xylazine (80 mg/ml ketamine HCL and 12 mg/ml xylazine HCL; 1 ml/kg i.p.; Sigma Chemical Co., St. Louis, MO), and implanted with radiotelemeters transmitting electrocardiogram (ECG), aortic BP, and core body temperature (n = 27, 9–10 week old rats weighing ~250–350 g, telemeter model HD-S11-F0, Data Sciences International) at Charles River as described previously (Carll et al. 2013).

Group Size Determinations and Experimental Design

The present study was conducted in parallel with our previous study, which assessed the effects of exposure on responses to a high fat challenge (Martin et al 2018), and included filtered air, low peat and high peat groups. Thus, group size determination for the present study was based upon the calculations reported in Martin et al (2018). Briefly, sample size analysis was performed using open source R Studio software with the ‘pwr’package and ‘pwr.anova.test’ command (https://cran.r-project.org/web/packages/pwr/pwr.pdf). Sample size analysis was based upon the (k) number of experimental groups (k = 3 groups: filtered air, low peat and high peat), a significance level = 0.05, a power = 0.8, and the effect size index (f), which is derived by multiplying the expected effect size (d) by the standard deviation (SD). Based upon these calculations, an n = 8 was selected. For the telemetry cohort, one extra animal per group was added to account for potential for lost telemetry signals.

There were three cohorts of rats, each containing 3 groups of rats (i.e. filtered air, low peat and high peat). The telemetry cohort, consisting of rats with implanted telemeters, was used to monitor cardiovascular function. The aconitine challenge cohort was employed to monitor sensitivity to the arrhythmogenic agent aconitine. To avoid potential effects of surgical implantation and aconitine on circulating markers, a separate final cohort was utilized to measure systemic indicators of inflammation and injury described below.

Acclimation to Exposure Chambers and Peat Smoke Concentrations

All animals were acclimated twice in 30-min increments to a full-body inhalation chamber at room temperature over a 2-day period leading up to exposure. Irish peat briquettes were used from County Clare, Ireland for our biomass fuel source (Glynn Bros., Boston, MA). Animals were exposed once for a duration of 1 hr to smoke generated using an automated control tube furnace system wherein PM was diluted to low (target concentration = 0.4 mg/m3) or high (target concentration = 4 mg/m3) concentrations (actual concentrations are listed in Table 1). The low concentration target was in the range of ambient particulate concentrations reported during peat fires in North Carolina (Tinling et al. 2016) and in order to establish a dose response, the high concentration was selected to be one order of magnitude higher. Importantly, this high concentration is on par with respirable PM exposure levels experienced by firefighters while combating wildland fires (Swiston et al. 2008).

Table 1:

Particulate Matter and Gas Concentrations

Filtered Air Low Peat High Peat
PM2.5 (mg/m3) BLD 0.38 ± 0.00 4.04 ± 0.03
Particle Size range (microns) -- 0.4 to 0.5 0.8 to 1.2
Particle (11 nm − 0.4 μm) Number (#/cc)a -- 55831179* 110650 ±24031
Particle (0.4 μm – 9 μm) Number (#/cc)b -- 1995 ± 455 1440 ± 220
Organic Carbon (% PM Mass) -- 77.0% 64.9%
Elemental Carbon (% PM Mass) -- 0.0% 0.4%
Carbon Monoxide (ppm) -- 4.53 ± 0.2 13.14 ± 0.19
Carbon Dioxide (ppm) -- 431.2 ± 3.23 551 ± 3.1
Volatile Organic Carbon (ppb) -- 72 382
Open in a new tab

PM2.5 - particles < 2.5 microns. BLD – below limit of detection. Values shown are means ± standard errors of the means from two separate exposure days for each concentration. An NO/NO2/NOx monitor was not available for these exposures. NOx values were reported in our previous peat study (Martin et al. 2018).

a -

particle number measured by an Optiscan.

b -

particle number measured by a TSI nanoscan.

* -

We were only able to obtain a reading during one of the two low peat exposure days. The reading taken during the other exposure day was overloaded with particles and yielded an unreliable number.

PM Sampling during Exposure:

Particulate matter sampling was conducted through ports on the inhalation chamber during combustion for chemical speciation. PM in the biomass smoke and filtered air (control) were collected on a pre-baked quartz filter for analysis of carbon species. The flow rate of PM sampling controlled by a vacuum controller (Model: XC-40; Apex Instruments Inc., Fuquay-Varina, NC) was approximately 1 L/min.

Tube Furnace Exposure System and PM and Gas Monitoring

Smoldering peat smoke was generated using an automated quartz-tube furnace system. An automated mass flow controller (Mass-Flo, MKS Instrument, Inc., Andover, MA) based upon a proportional-integral-derivative (PID) feedback loop was incorporated into the system to precisely control smoke concentration. Biomass smoke (2 L/min) generated from the tube furnace system was diluted with air (approximately 3 and 60 L/min for 1st and 2nd dilution air, respectively) and then delivered to a whole body inhalation chamber (0.3 m3 Hinners style stainless steel and glass exposure chamber). The smoke concentration was continuously monitored and adjusted by the PID feedback control loop linked to a continuous PM monitor in the chamber to an exhaust flow control valve in a smoke inlet line. Time of flight changes in PM concentration in the chamber were detected and the dilution air flow rate was automatically adjusted to maintain PM concentration close to its set point (<10% of the target set point). The peat smoke in the chamber was maintained at a temperature of approximately 72 °F, and a relative humidity of approximately 40 %, controlled by a humidifier. A pressure gauge (Magnehelic, Dwyer Instruments Inc., Michigan City, IN) was placed in the chamber to ensure constant pressure throughout the inhalation exposure. Carbon dioxide (CO2) and carbon monoxide (CO) levels were monitored utilizing a non-dispersive infrared analyzer (Model: 602 CO/CO2; CAI Inc., Orange, CA).

Volatile organic carbons (VOCs) in the peat smoke and filtered air were sampled using SUMMA canisters and carbonyls were sampled with 2,4-dinitrophenylhydrazine (DNPH)-coated silica cartridges (PN 505323, Sigma-Aldrich Co., St. Louis, MO). The sampling flow rates through the evacuated canister (filled to approximately 0.7 atm) were controlled using a critical orifice at a flow rate of approximately 70 ml/min. Cartridge sampling flow rates were controlled with an SKC Aircheck Sampling Pump (Model: 224-PCXR8, SKC Inc., Eighty-Four, PA) with flow rates in the range of 0.5–0.7 L/min. VOCs in the canisters were analyzed by gas chromatography–mass spectrometry (GC-MS) in accordance with the U.S. EPA TO-15 Method. DNPH cartridge samples were extracted with 6 ml carbonyl-free acetonitrile (Burdick & Jackson, VWR International, Radnor, PA). Carbonyl hydrazones were analyzed in the extracts by high-performance liquid chromatography (HPLC) according to the U.S. EPA Method TO-11A. Detailed descriptions of the TO-15 and TO-11A analytical procedures were reported previously (George et al., 2014). PM were also collected on a glass-fiber filter installed in an exhaust line of the inhalation chamber to determine mean PM concentrations gravimetrically by weighing the filter before and after inhalation exposure. The real-time measurements of peat smoke properties and engineering parameters including temperature, relative humidity, static pressure, and flow rate were continuously monitored, recorded, and displayed using data acquisition software (Dasylab version 13.0, National Instruments, Austin, TX).

Particle number per cubic centimeter was determined for particles in the size range 0.4 to 9 microns (Optical Particle sizer 3330; TSI INC, Shoreview, MN) and 11 nm to 0.4 microns (Nanoscan Scanning Mobility Particle Sizer Nanoparticle sizer 3910; TSI INC, Shoreview, MN).

Radiotelemetry of Physiological Parameters

Radiotelemetry was used to monitor BP, HR, and QA interval in conscious unrestrained rats. The QA interval provides an inverse index of contractility measured by the delay between onset of left ventricular (LV) depolarization and ejection, indicated by initializations of the R wave and a rise in aortic pressure, respectively (Cambridge and Whiting, 1986). Figure 1 illustrates a timeline of exposure and cardiovascular monitoring using telemetry. Rats implanted with telemeters that monitor BP and HR were exposed for 1 hr to low (0.38 mg/m3) or high (4.04 mg/m3) concentrations of peat smoke or filtered air. Data during exposure (i.e. while the rats were in the inhalation exposure chamber) were recorded continuously for the entirety of the 1 hr exposure period. The first 5 minutes of data collected during the exposure period, reported every 10 seconds, were analyzed to assess the immediate impacts of exposure. Data during the entire 1 hr exposure period were averaged and compared to the corresponding time-matched pre-exposure period (while rats were in their home cages) from the previous day. Data during the first 3 hr post-exposure after the rats were returned to their home cages were recorded for 1 min every 15 min. Data collected during this 3-hr post-exposure period immediately following exposure were compared to the corresponding time-matched pre-exposure period from the previous day. Arterial BP (systolic and diastolic pressures), HR, and QA interval were automatically calculated by software (DataART 3.01; DSI) from pressure and ECG waveforms sampled at 1000 Hz.

Figure 1:

Figure 1:

Open in a new tab

Exposure and monitoring timeline. Rats implanted with telemeters that monitor blood pressure and heart rate were exposed for 1 hr to low (0.38 mg/m3) or high (4.04 mg/m3) concentrations of peat smoke or filtered air. The first 5 minutes of data during exposure were analyzed to assess immediate responses. In addition, the averages over the 1-hr exposure period were compared to the corresponding time-matched pre-exposure period from the previous day. Finally, the averages over the 3-hr post-exposure period immediately following exposure were compared to the corresponding time-matched pre-exposure period from the previous day.

Baroreflex Analysis

Baroreflex sensitivity (BRS) was calculated using the sequence method (Bertinieri et al. 1985; DiRienzo et al. 1985) (HemoLab). The sequence method identifies sequences of 4 or more heart beats during which BP and pulse interval (PI) change in the same direction. Linear regression lines were calculated for all individual sequences of BP and PI, and the mean of the slopes of all lines was then used as an index of BRS. Irregular sequences were manually removed. Up gain, down gain and overall gain, which describe tachycardic, bradycardic and combined responses, respectively, are reported. BRS analysis could only be done on continuously acquired data; thus, analysis of BRS was limited to physiological data collected during exposure.

Serum collection and analysis.

Animals were euthanized after intraperitoneal injection of 1 ml/kg pentobarbital (Fatal Plus, Dearborn, MI) diluted 1:1 approximating 200 mg/ml. When animals were completely non-responsive to hind paw pinch, blood was collected through the abdominal aorta in serum separator tubes and EDTA tubes, which were inverted and placed on ice. Before spinning the EDTA tubes, complete blood cell counts (CBC) were determined. Red blood cells, white blood cells, hemoglobin, hematocrit, mean corpuscular volume, and platelets, were measured utilizing a Beckman-Coulter AcT blood analyzer (Beckman-Coulter Inc., Fullerton, CA). The serum and EDTA tubes were then centrifuged at 1500g and serum and plasma samples were stored at −80 °C until further analysis. Several serum factors were measured using the Konelab Arena 30 Clinical Chemistry Analyzer (Thermo Clinical LabSystems, Espoo, Finland) including total cholesterol, low and high density lipoprotein cholesterol (LDL and HDL), triglycerides, creatine kinase, complement components C3 and C4, alanine aminotransferase, alkaline phosphatase, angiotensin converting enzyme (ACE), and glucose. D-dimer and fibrinogen were measured in plasma.

Aconitine Challenge Cardiac Arrhythmia Sensitivity Test

Aconitine challenge was performed in rats approximately 24 hr after exposure to low peat, high peat, or filtered air. Rats were anesthetized with urethane (1.5 g/kg, i.p.) and surgically catheterized into the left jugular vein with saline-filled PE50 tubing for administration of aconitine. The experiments were performed immediately following implantation of the i.v. catheter. Body temperature was supported during and after surgery. Ten mg/ml aconitine (in saline) was infused into the jugular vein at a flow rate of 0.2 ml/min employing an ISMATECH IPC infusion pump while the ECG was continuously monitored and timed (using an external telemeter attached to the skin). Susceptibility to aconitine-induced arrhythmia was measured as the threshold dose of aconitine required to produce ventricular premature beats (VPB), ventricular tachycardia (VTach), and ventricular fibrillation (VFib) and calculated using the following formula:

Threshold dose(μg/kg)for arrhythmia=10μg/ml×0.2ml/min×time required forincluding arrhythmia(min)/body weight(kg)=1μg/min×time(min)/body weight(kg)

Statistics

Data are reported as boxplots with all data points shown. Box edges mark the interquartile range, middle line marks the median, the ‘‘+’’ marks the mean, and the whiskers mark the minimum and maximum data values. GraphPad Prism (GraphPad Software version 7.02, San Diego, CA) was used for all statistical analyses. Normality of data distributions was assessed with normality tests (D’Agostino-Pearson omnibus test or, in the instances of smaller ‘n,’ Shapiro-Wilk or Kolmogorov-Smirnov tests) with significance set at p < 0.05. All data (except the functional values collected during the first 5 min of exposure) that met this assumption were assessed using a one-way ANOVA with Tukey’s post-test and multiplicity-adjusted p values with linear trend analyses for multiple comparisons. Data that did not meet the normality assumption (i.e. HR data during the first 5 min, post-exposure HR, Vfib acontine data, serum alkaline phosphatase, serum total cholesterol, serum HDL cholesterol, and whole blood red blood cells, hematocrit, and platelets) were tested using the nonparametric Kruskal-Wallis test with Dunn’s multiple comparisons post-test. A p value < 0.05 was considered statistically significant. Functional values collected during the first 5 min (i.e. systolic and diastolic BP, and QA interval) were assessed with a repeated measures ANOVA with Tukey’s post-test and multiplicity-adjusted p values with p values < 0.05 considered statistically significant.

Results

Peat smoke exposure concentrations and characteristics

Table 1 presents the composition of peat smoke at both exposure concentrations. Fine PM concentrations for high and low peat, (i.e. 4.04 mg/m3 and 0.38 mg/m3, respectively) approximated the target concentrations of 4 mg/m3 and 0.4 mg/m3. Low peat contained smaller particle size, higher particle number and a greater ratio of organic carbon compared to high peat. High peat possessed approximately 3-fold greater amount of CO as low peat and numerically higher CO2 levels. High peat also contained approximately 5-fold greater levels of VOCs.

Figure 2 shows the top 14 VOC components by concentration. Major VOC components were acetaldehyde, acetone, propylene, propane, benzene, toluene, and acrolein among others. Peat smoke VOC concentrations were generally in proportion with smoke PM concentration. For example, high peat possessed approximately 7-fold and 5.5-fold higher levels of the irritants acetaldehyde and acrolein, respectively, compared to low peat. Concentrations of other VOCs such as toluene and benzene were also proportionate with exposure concentration. Supplementary Table S1 provides the concentrations of the remaining VOCs measured.

Figure 2:

Figure 2:

Open in a new tab

The top 14 volatile organic carbon (VOC) species by concentration in peat smoke. VOCs in the smoke and filtered air control were sampled using SUMMA canisters and carbonyls were sampled with 2,4-dinitrophenylhydrazine (DNPH)-coated silica cartridges. VOCs in the canisters were analyzed by gas chromatography-mass spectrometry (GC-MS) in accordance with U.S. EPA TO-15 Method. The two sets of air groups represent separate air exposures that took place during runs of either low or high peat exposures.

Heart rate and blood pressure during and after exposure

Figure 3 shows HR, BP, and QA interval data measured every 10 seconds during the first 5 minutes of the exposure period. Low peat caused significant decreases in HR relative to both filtered air and high peat almost immediately after commencing with exposure. In addition, low peat also caused increases in systolic BP and to a lesser extent diastolic BP relative to filtered air during segments of the first 5 minutes of exposure. These increases in BP were not as sustained as the increases with high peat exposure over the course of the 5-minute period. There was no significant change in QA interval in either exposure group relative to filtered air.

Figure 3:

Figure 3:

Open in a new tab

Cardiovascular function during the first 5 minutes of exposure to low peat, high peat, or filtered air (n = 9/group for low peat; the filtered air and high peat groups had only 7 rats/group due to the exclusion of animals because of incomplete data during the first 5 minutes for these animals). Values represent mean values collected every 10 seconds for heart rate (A), systolic (B) and diastolic blood pressure (C), and QA interval (D). Functional values were obtained from implanted telemeters. a-significantly less than filtered air (p<0.05). b-significantly less than high peat (p<0.05). c-significantly greater than filtered air (p<0.05). d-significantly greater than low peat (p<0.05). e-significantly less than low peat (p<0.05).

Figure 4 shows HR, BP and QA interval data during and immediately after exposure, averaged over the entire exposure and post-exposure monitoring periods, respectively. There were no significant effects of exposure on % change in HR both during exposure and after exposure. High peat produced a significant increase in systolic BP relative to filtered air during exposure. There was also a significant positive linear trend in systolic BP with increasing peat concentration during exposure. There was no significant change in systolic BP after exposure. There was, however, a tendency towards a positive linear trend in systolic BP with rising concentration after exposure. High peat also produced a significant elevation in diastolic BP relative to filtered air controls during exposure. There was also a significant positive linear trend in diastolic BP with increasing peat concentration during exposure. This change in diastolic BP persisted after exposure with high peat initiating a significant rise relative to filtered air and low peat. In addition, there was a significant positive linear trend in diastolic BP following exposure. There was no marked effect of exposure on QA interval either during or after exposure. Raw values used to derive % change values for systolic and diastolic BP, HR, and QA interval during and after exposure are shown in Supplementary Tables S2 and S3, respectively.

Figure 4:

Figure 4:

Open in a new tab

Cardiovascular function during and after exposure to low peat, high peat, or filtered air (n = 9/group except for filtered air, which was 8/group due to the exclusion of one animal because of incomplete post-exposure data for this animal). Values represent % change during exposure from the corresponding time-matched pre-exposure period from the previous day and % change during the post-exposure period from the corresponding time-matched pre-exposure period from the previous day for heart rate (A), systolic (B) and diastolic blood pressure (C), and QA interval (D). Functional values were obtained from implanted telemeters. Significant p-values for group differences and linear trends are shown.

Baroreflex sensitivity (BRS) during exposure

Figure 5 illustrates BRS data during exposure. There was no significant difference in up or down gain during treatment, although there was a tendency towards an increase in down gain in low peat relative to high peat. Low peat exposure produced an elevation in total gain relative to filtered air controls.

Figure 5:

Figure 5:

Open in a new tab

Baroreflex sensitivity (BRS) during exposure to low peat, high peat, or filtered air (n = 9/group). BRS was calculated using the sequence method and shows up gain (A) or the sequences in which the blood pressure (BP) and pulse interval (PI) increased and the down gain (B) in which the BP and PI decreased. Total gain is also shown (C). BP and PI data were derived from implanted blood pressure telemeters. Significant p-values for group differences are shown.

Sensitivity to aconitine-induced cardiac arrhythmia

Figure 6 shows the cumulative dose of infused aconitine needed to trigger VPBs, VTach, and VFib in rats approximately 24 hr after a single exposure to high peat, low peat, or filtered air. There were no significant differences among groups in the dose required to elicit VPBs or VTach. Low peat, however, required significantly less total dose of aconitine to elicit VFib than filtered air control. There was also a tendency towards a negative linear trend with increasing peat smoke exposure concentration in the dose of aconitine required to elicit VFib.

Figure 6.

Figure 6.

Open in a new tab

Cumulative dose of infused aconitine necessary to trigger ventricular premature beats (VPB), ventricular tachycardia (VTach), and ventricular fibrillation (VFib) in rats one day after a single exposure to low peat, high peat, or filtered air (n = 8 for low peat and high peat; the filtered air group had data from only 6 animals because data for the final two filtered air animals was incomplete). Significant p-values for group differences are shown.

Systemic Inflammation and Injury

Figure 7 shows changes in several circulating markers of inflammation. Although not significantly different from filtered air controls, low peat produced a significant rise in serum C4 relative to high peat. There was a tendency towards an increase in C3 levels in low peat relative to high peat. Low peat also initiated a significant elevation in serum LDL cholesterol relative to filtered air controls and high peat. In addition, low peat induced a significant increase in serum ACE relative to high peat and a tendency towards a rise in levels of ACE relative to filtered air controls. Low peat also produced a significant elevation in whole blood white blood cells (WBC) relative to high peat and a tendency towards an increase in levels of whole blood white blood cells relative to filtered air controls. There were little to no effects of exposure on other indicators of systemic injury and inflammation (Supplementary Table S4).

Figure 7:

Figure 7:

Open in a new tab

Systemic indicators of inflammation and injury in rats measured one day after a single exposure to low peat, high peat, or filtered air (n = 8/group; filtered air had an n = 7 because 1 rat was excluded due to enlarged heart (more than twice the mass of the remaining hearts, which was assumed to be due to a pre-existing lesion). Indicators include serum C3 (A), C4 (B) LDL cholesterol (C), ACE (D), and whole blood white blood cells (E). Significant p-values for group differences are shown.

Discussion

The present findings demonstrate that a single exposure to peat smoke modified cardiovascular function. The peat smoke combustion chemistry was similar in physico-chemical composition to that reported in previous studies (Kim et al. 2019) in terms of type and quantity of gases, proportions of organic carbon and acetaldehyde as the major VOC component. Although containing plenty of reactive aldehydes and organic-rich PM, it was not possible to determine the specific components that drove toxicity with the current experimental design. The impacts of real peat wildland fire smoke PM alone in mice (Kim et al. 2014) and rats (Thompson et al. 2018) have been previously assessed. In addition, the cardiovascular responses to whole and particle-free diesel exhaust have also been compared, with particle-free diesel exhaust generally eliciting more adverse cardiovascular responses (Carll et al. 2012; Hazari et al. 2011; Lamb et al. 2012). Future studies should use similar approaches to identify the most offending chemical constituents of biomass combustion emissions such as peat smoke.

The concentration-dependent changes in systolic and diastolic BP are consistent with other reports in humans and experimental animals exposed to air pollutants. In a recent meta-analysis, Yang et al (2018) demonstrated positive associations between short-term exposure to particulate and gaseous constituents of ambient air pollution and increased BP in humans. Similarly Tsai et al (2018) reported an increase in hospital admissions for hypertension attributed to fine PM contaminants. Further, Groot et al (2019) noted length of career was associated with self-reported hypertension in wildland firefighters. Experimental studies in rodents showed that PM exposure decreased BP (Cheng et al. 2003; Chuang et al. 2017). In contrast, previous studies with the irritant acrolein (Perez et al. 2015) and separately with the well-studied particle-gas mixture diesel exhaust (DE) (Carll et al. 2012) demonstrated elevated BP in hypertensive and heart failure prone rats, respectively. The role of pollutant type, health status, and the specific mechanisms responsible for such changes in BP are not known. The immediacy of the peat-induced rise in BP in the present study, however, suggests modulation of autonomic tone, consistent with previous findings that demonstrated sympathetic mediation of DE induced elevation in BP in heart failure-prone rats (Carll et al. 2013) and potentially triggering by pulmonary neural mechanisms as previously shown (Hazari et al. 2011). Interestingly, the increase in BP with low peat, which was limited to the early part of the exposure period, was accompanied by a pronounced bradycardic response suggesting an increase in parasympathetic tone. The absence of a significant change in HR in the high peat group, however, suggests other mechanisms may have been at play including systemic vasoconstriction as has been shown in previous studies. One key study reported that DE inhalation in healthy volunteers both increased BP and impaired forearm blood flow responses to vasodilators indicating altered endothelial function (Mills et al. 2011). A subsequent meta-analysis of the systemic effects of DE yielded similar conclusions linking DE exposure with impaired forearm blood flow (Vieira et al. 2017). Air pollution also initiates oxidative stress and inflammation, all of which increase arterial vasoconstrictor responsiveness, and ultimately raise BP (Giorgini et al. 2016). Importantly, elevations in BP initiate acute cardiovascular events among susceptible populations (Brook et al. 2010) and sustained increased BP produces cardiac remodeling and increases in cardiovascular morbidity and mortality (Lewington et al 2002). Modification of BP responses after a single peat smoke exposure suggests that even acute exposures to biomass emissions exhibit the potential to induce clinically significant alterations in cardiovascular function.

Exposure to low peat increased BRS gain during exposure, indicating dysregulation of the baroreflex. When functioning normally, the baroreceptor reflex maintains homeostatic control of BP by producing reflex decreases in HR when BP rises and increases in HR when BP falls, changes that are mediated by the autonomic nervous system (Hazari et al. 2014). Exposure to a variety of air pollutants has largely been linked with a reduction in BRS including exposure to sulfur dioxide in humans (Routledge et al. 2006), and acrolein (Hazari et al. 2014), cigarette smoke (Valenti et al. 2010) and carbon nanotubes in rodents (Legramante. 2009). In contrast, exposure to ambient PM in dogs elevated both arterial BP and BRS (Bartoli et al. 2009). One potential explanation for the BRS effects with low peat relates to altered autonomic tone as increased BRS has previously been linked with thoracic sympathectomy (Bygstad et al. 2013)) and drug-induced decreases in sympathetic nervous system activity (Lewandoski et al. 2010). Although heart rate variability was not measured in the current study, the immediate decrease in HR at the beginning of the exposure period with low peat suggests increased parasympathetic tone consistent with early exposure effects reported in previous studies (Farraj et al. 2011). Furthermore, it was previously demonstrated that exposure to low, but not high concentrations of particle-free DE increased markers of heart variability that reflect enhanced parasympathetic tone (Lamb et al. 2012). Thus, low, but not high peat may have elevated parasympathetic tone, which in turn increased BRS. These findings contrast with findings that point to sympathetic mediation of responses to low PM concentrations and mediation by pulmonary neural reflex-triggered elevated parasympathetic tone with repeated exposure or exposure to higher PM concentrations (Carll et al. 2017). The precise reasons for this divergence are unclear but may relate to the greater complexity of responses to whole combustion emissions containing multiple pollutants relative to responses to single pollutants. This is illustrated in findings from previous studies that showed that whole and particle-free diesel exhaust elicit divergent cardiovascular responses (Carll et al. 2012; Hazari et al. 2011; Lamb et al. 2012). The uncertainty resulting from the potential elicitation of unique mechanisms by particulate and gaseous constituents of combustion emissions is increased when factoring in the fact that when compared to higher exposure levels, lower exposure levels at least in the present experimental findings had smaller particle size and higher particle number, both linked to worse cardiovascular responses than their counterparts (Brook et al. 2010).

Only low peat initiated an increase in sensitivity to aconitine induced cardiac arrhythmia, an index of latent vulnerability to cardiac arrhythmia, one day after exposure relative to filtered air controls. These results are similar to our previous findings with particulate and gaseous pollutants (Hazari et al., 2009; Farraj et al., 2012) suggesting that air pollution exposure in a nonspecific fashion enhances sensitivity of the cardiac electrical conduction system. In contrast, high peat, despite inducing significant elevation in BP, failed to affect sensitivity to aconitine. This divergence in responsiveness is consistent with our previous rodent study with DE (Hazari et al. 2011) wherein the low exposure concentration (i.e. 150 μg/m3) produced greater sensitivity to aconitine than the high concentration (i.e. 500 μg/m3). This pattern is a deviation from typical concentration-dependent responses and may relate to physicochemical characteristics of complex air pollution mixtures and associated adaptive homeostatic mechanisms in exposed animals.

Exposure to low peat produced a systemic inflammatory response characterized by a rise in LDL cholesterol relative to filtered air controls, and several differences from exposure to high peat, including increased levels in serum LDL cholesterol C3, C4, and ACE and whole blood WBC counts. Exposure to fine PM air pollution was previously associated with increased LDL levels in children (McGuinn et al. 2020) and the elderly (Mao et al. 2020) and is a significant risk factor for cardiovascular disease. On the other hand, evidence regarding components of the complement system after air pollution exposure is mixed with some studies showing increases (Jin et al. 2019) while others, decreases (Goulart et al. 2020; Tong et al. 2019). The complement system, a crucial player in innate immunity, consists of over 30 effector molecules including C3 and C4, which promote inflammation, opsonize cells and immune complexes, and directly kill pathogens or injured cells (Noris and Remuzzi. 2013). ACE, an enzyme that converts angiotensin I to angiotensin II, a potent vasoconstrictor (Ghelfi et al., 2010), also plays a key role in the adverse health effects attributed to air pollution. Ghelfi et al (2010) demonstrated that pretreatment with an ACE inhibitor prevented cardiac dysfunction and oxidative stress responses mediated by inhalation to ambient PM in rats. Further, increased circulating WBC counts were correlated with occupational exposure to ambient air pollution in commercial drivers (Lawin et al. 2018) and exposure to indoor air pollution due to cookstove biomass burning (Rabha et al. 2018). The magnitude of the systemic inflammatory response is consistent with our previous peat smoke study (Martin et al. 2018), wherein there were very few changes in circulating indicators of inflammation including alpha(1)-acid glycoprotein and alpha-2-macroglobulin, which dominate the acute phase response in rats. Although these changes in markers of inflammation are generally small in magnitude, they in aggregate point to an enhancement of systemic inflammation that may have influenced the functional responses observed. Although the lack of systemic inflammatory responses one day after exposure to high peat may reflect a true absence of an inflammatory response, it is possible that such a response peaked soon after exposure and waned by one day post-exposure. Future studies need to examine time-dependent changes in circulating markers to more comprehensively elucidate potential mechanisms of action.

The precise reasons for the divergent BRS, BP and systemic responses among exposure levels are unclear but may relate to physicochemical differences beyond PM concentration. The proportion of organic carbon varied with exposure level, with low peat and high peat consisting of 77% and 65% organic carbon, respectively. Organic carbon was found to perturb cardiovascular and respiratory endpoints (Kelly and Fussel. 2015). Further, particle size differed significantly between low peat (0.4–0.5 µm diameter) and high peat (0.8–1.2 µm diameter), an effect likely driven by elevated aerosol nucleation at higher PM concentrations (Guo et al. 2014). Although it is possible that these distinct physicochemical profiles are artifacts of the combustion exposure system, cycles of peak particulate pollution are usually associated with larger average mean PM diameter and lower total particle number (Guo et al. 2014). In addition, total particle number was much higher (>500-fold) in the low peat exposure compared to the high peat exposure indicating a much higher total surface area in the low peat exposure that potentially increased the interaction of PM and its components with respiratory tissue. Importantly, deep lung responses and potential for cardiovascular effects generally increases with decreasing particle size within the fine PM range (Brook et al. 2010). The PM was not analyzed for inorganic elements or ionic components, and organic chemical speciation was not performed, so it was unclear if there were any differences in PM composition and whether such differences drove the divergence in responses among exposure concentrations. We previously determined that peat smoke condensates had a greater mass fraction of organic carbon and n-alkanes and a smaller mass fraction of polyaromatic hydrocarbons than other biomass sources (Kim et al. 2018). Finally, the contribution of other factors such as particle charge, which was not explored in this study, to the different responses observed among exposure concentrations is unclear.

These findings were limited by the absence of assessment of pulmonary inflammatory responses, although previously Martin et al. (2018) noted that such responses were minimal. In addition, although the exposure concentrations used were relatively high, the fine PM concentrations in low and high peat in the present study were comparable to ambient concentrations reported in peat bog fires in North Carolina (Tinling et al., 2016) of the U.S.A and in Indonesia (Uda et al. 2019), respectively. Furthermore, the absence of a significant systemic inflammatory response in the high peat group may relate to the possibility that any such response peaked in the high peat group soon after exposure. As such, these finding are also limited by the absence of an assessment of systemic inflammation early after exposure. In addition, assessments of HR and BP responses to increasing doses of phenylephrine, which deliberately alters blood pressure, will yield more reliable baroreceptor sensitivity determinations in future studies.

Conclusions

In conclusion, these findings demonstrate that a single exposure to peat smoke has the potential to produce changes in cardiovascular function that involve alterations of homeostatic mechanisms. Furthermore, exposure may increase “conditional susceptibility” for adverse cardiovascular events in individuals with pre-existing cardiovascular disease and/or the elderly (Cascio 2016). Finally, short-term effects of air pollution exposure may set the stage for development and/or progression of cardiovascular disease with repeated exposure over time.

Supplementary Material

Sup1

Acknowledgments:

The authors would like to thank Dr. Colette Miller and Dr. Jason Sacks of the U.S. EPA for their thorough review of this manuscript prior to submission. The authors would also like to thank Judy Richards of the U.S. EPA for her excellent technical assistance.

Funding: This work was supported by the intramural research program of the Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC.

Footnotes

Disclaimer: This manuscript has been reviewed by the Center for Public Health and Environmental Assessment, United States Environmental Protection Agency and approved for publication. Approval does not signify that the contents necessarily reflect the views and policies of the Agency nor does mention of trade names or commercial products constitute endorsement or recommendation for use.

Disclosure of interest: The authors report no conflict of interest.

Availability of Data and Material: All data (i.e. individual values used to generate means and standard deviations presented in the tables and figures reported in this manuscript) will be made available on the U.S. E.P.A. public data repository located at https://catalog.data.gov/harvest/epa-sciencehub

References

  1. Bartoli CR, Wellenius GA, Diaz EA, Lawrence J, Coull BA, Akiyama I, Lee LM, Okabe K, Verrier RL, and Godleski JJ 2009. Mechanisms of inhaled fine particulate air pollution-induced arterial blood pressure changes. Environ Health Perspect. 117:361–366. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Bertinieri G, Di Rienzo M, Cavallazzi A, Ferrari AU, Pedotti A and Mancia G 1985. A new approach to analysis of the arterial baroreflex. J. Hyperten. 3(Suppl. 3): S79–S81. [PubMed] [Google Scholar]
  3. Brook RD, Rajagopalan S, Pope CA 3rd, Brook JR, Bhatnagar A, Diez-Roux AV, Holguin F, Hong Y, Luepker RV, Mittleman MA, Peters A, Siscovick D, Smith SC Jr., Whitsel L, and Kaufman JD 2010. Particulate matter air pollution and cardiovascular disease: An update to the scientific statement from the American Heart Association. Circulation. 121:2331–2378. [DOI] [PubMed] [Google Scholar]
  4. Bygstad E, Terkelsen AJ, Pilegaard HK, Hansen J, Mølgaard H, and Hjortdal VE 2013. Thoracoscopic sympathectomy increases efferent cardiac vagal activity and baroreceptor sensitivity. Eur J Cardiothorac Surg. 44:e193–e199. [DOI] [PubMed] [Google Scholar]
  5. Cambridge D, and Whiting MV 1986. Evaluation of the QA interval as an index of cardiac contractility in anaesthetised dogs: Responses to changes in cardiac loading and heart rate. Cardiovasc. Res. 20: 444–450. [DOI] [PubMed] [Google Scholar]
  6. Carll AP, Crespo SM, Filho MS, Zati DH, Coull BA, Diaz EA, Raimundo RD, Jaeger T, Ricci-Vitor AL, Papapostolou V, Lawrence JE, Garner DM, Perry BS, Harkema JR, & Godleski JJ 2017. Inhaled ambient-level traffic-derived particulates decrease cardiac vagal influence and baroreflexes and increase arrhythmia in a rat model of metabolic syndrome. Particle and fibre toxicology. 14(1), 16. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Carll AP, Hazari MS, Perez CM, Krantz QT, King CJ, Haykal-Coates N, Cascio WE, Costa DL, Farraj AK 2013. An autonomic link between inhaled diesel exhaust and impaired cardiac performance: Insight from treadmill and dobutamine challenges in heart failure-prone rats. Toxicol. Sci. 135:425–436. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Carll AP, Hazari MS, Perez CM, Krantz QT, King CJ, Winsett DW, Costa DL, and Farraj AK 2012. Whole and particle-free diesel exhausts differentially affect cardiac electrophysiology, blood pressure, and autonomic balance in heart failure-prone rats. Toxicol Sci. 128:490–499. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Cascio WE. 2016. Proposed pathophysiologic framework to explain some excess cardiovascular death associated with ambient air particle pollution: Insights for public health translation. Biochim Biophys Acta. 1860:2869–2879. [DOI] [PubMed] [Google Scholar]
  10. Chen CC, Yang CY. 2018. Association between gaseous air pollution and hospital admissions for hypertension in Taipei, Taiwan. J Toxicol Environ Health. Part A. 81(4):53–59. [DOI] [PubMed] [Google Scholar]
  11. Cheng TJ, Hwang J-S, Wang PY, Tsai C-F, Chen C-Y, Lin S-H, Chan C-C. 2003. Effects of concentrated ambient particles on heart rate and blood pressure in pulmonary hypertensive rats. Environ Health Perspect. 111:147–150. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Chuang HC, Lin YJ, Chou CCK, Hwang J-S, Chen C-C, Yan Y-H, Hsieh H-I, Chuang K-J, Cheng T-J 2017. Alterations in cardiovascular function by particulate matter in rats using a crossover design. Environ Pollut. 231:812–820. [DOI] [PubMed] [Google Scholar]
  13. Di Rienzo M, Bertinieri G, Mancia G, and Pedotti A 1985. A new method for evaluating the baroreflex role by a joint pattern analysis of pulse interval and systolic blood pressure series. Med Biol Eng Computing. Suppl.:313–314. [Google Scholar]
  14. Farraj AK, Hazari MS, Winsett DW, Kulukulualani A, Carll AP, Haykal-Coates N, Lamb CM, Lappi E, Terrell D, Cascio WE, & Costa DL 2012. Overt and latent cardiac effects of ozone inhalation in rats: evidence for autonomic modulation and increased myocardial vulnerability. Environmental health perspectives. 120(3), 348–354. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. George IJ, Hays MD, Snow R, Faircloth J, George BJ, Long T, Baldauf RW. 2014. Cold temperature and biodiesel fuel effects on speciated emissions of volatile organic compounds from diesel trucks. Environ. Sci. Technol. 48:14782–14789. [DOI] [PubMed] [Google Scholar]
  16. Ghelfi E, Wellenius GA, Lawrence J, Millet E, and Gonzalez-Flecha B 2010. Cardiac oxidative stress and dysfunction by fine concentrated ambient particles (CAPs) are mediated by angiotensin-II. Inhal Toxicol. 22:963–972. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Giorgini P, Di Giosia P, Grassi D, Rubenfire M, Brook RD, and Ferri C 2016. Air pollution exposure and blood pressure: An updated review of the literature. Curr Pharm Des. 22:28–51. [DOI] [PubMed] [Google Scholar]
  18. Goulart MFG, Alves AG …F, Farhat J, Braga ALF, Pereira LAA, Lichtenfels AJDC, Campos LMD, da Silva CAA, Elias AM, and Farhat SCL 2020. Influence of air pollution on renal activity in patients with childhood-onset systemic lupus erythematosus. Pediatr Nephrol. 35:1247–1255. [DOI] [PubMed] [Google Scholar]
  19. Groot E, Caturay A, Khan Y, and Copes R 2019. A systematic review of the health impacts of occupational exposure to wildland fires. Int J Occup Med Environ Health. 32:121–140. [DOI] [PubMed] [Google Scholar]
  20. Guo S, Hu M, Zamora ML, Peng J, Shang D, Zheng J, Du Z, Wu Z, Shao M, Zeng L, Molina MJ, Zhang R 2014. Elucidating severe urban haze formation in China. Proc Natl Acad Sci U S A. 111:17373–17378. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Haikerwal A, Akram M, Del Monaco A, Smith K, Sim MR, Meyer M, Tonkin AM, Abramson MJ, and Dennekamp M 2015. Impact of fine particulate matter (PM2.5) exposure during wildfires on cardiovascular health outcomes. J Am Heart Assoc. 4: e001653. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Hazari MS, Griggs J, Winsett DW, Haykal-Coates N, Ledbetter A, Costa DL, and Farraj AK 2014. A single exposure to acrolein desensitizes baroreflex responsiveness and increases cardiac arrhythmias in normotensive and hypertensive rats. Cardiovasc Toxicol. 14: 52–63. [DOI] [PubMed] [Google Scholar]
  23. Hazari MS, Haykal-Coates N, Winsett DW, Costa DL, & Farraj AK 2009. A single exposure to particulate or gaseous air pollution increases the risk of aconitine-induced cardiac arrhythmia in hypertensive rats. Toxicological Sciences. 112(2):532–542. [DOI] [PubMed] [Google Scholar]
  24. Hazari MS, Haykal-Coates N, Winsett DW, Krantz QT, King C, Costa DL, & Farraj AK 2011. TRPA1 and sympathetic activation contribute to increased risk of triggered cardiac arrhythmias in hypertensive rats exposed to diesel exhaust. Environmental health perspectives. 119(7), 951–957. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Jin X, Ma Q, Sun Z, Yang X, Zhou Q, Qu G, Liu Q, Liao C, Li Z 2, and Jiang G 2019. Airborne fine particles induce hematological effects through regulating the crosstalk of the kallikrein-kinin, complement, and coagulation systems. Environ Sci Technol. 53:2840–2851. [DOI] [PubMed] [Google Scholar]
  26. Kelly FJ and Fussell JC 2015. Air pollution and public health: emerging hazards and improved understanding of risk. Environ Geochem Health. 37:631–649. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Kim YH, King C, Krantz T, Hargrove MM, George IJ, McGee J, Copeland L, Hays MD, Landis MS, Higuchi M, Gavett SH, and Gilmour MI 2019. The role of fuel type and combustion phase on the toxicity of biomass smoke following inhalation exposure in mice. Arch Toxicol. 93:1501–1513. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Kim YH, Tong H, Daniels M, Boykin E, Krantz QT, McGee J, Hays M, Kovalcik K, Dye JA, & Gilmour MI 2014. Cardiopulmonary toxicity of peat wildfire particulate matter and the predictive utility of precision cut lung slices. Particle and fibre toxicology. 11, 29. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Kim YH, Warren SH, Krantz QT, King C, Jaskot R, Preston WT, George BJ, Hays MD, Landis MS, Higuchi M, DeMarini DM, & Gilmour MI 2018. Mutagenicity and Lung Toxicity of Smoldering vs. Flaming Emissions from Various Biomass Fuels: Implications for Health Effects from Wildland Fires. Environmental health perspectives. 126(1): 017011. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Lamb CM, Hazari MS, Haykal-Coates N, Carll AP, Krantz QT, King C, Winsett DW, Cascio WE, Costa DL, & Farraj AK 2012. Divergent electrocardiographic responses to whole and particle-free diesel exhaust inhalation in spontaneously hypertensive rats. Toxicological Sciences. 125(2), 558–568. [DOI] [PubMed] [Google Scholar]
  31. Lawin H, Ayi Fanou L, Hinson AV, Stolbrink M, Houngbegnon P, Kedote NM, Fayomi B, Kagima J, Katoto P, Ouendo EMD, and Mortimer K 2018. Health risks associated with occupational exposure to ambient air ollution in commercial drivers: A systematic review. Int J Environ Res Public Health. 15:2039. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Legramante JM, Valentini F, Magrini A, Palleschi G, Sacco S, Iavicoli I, Pallante M, Moscone D, Galante A, Bergamaschi E, Bergamaschi A, and Pietroiusti A 2009. Cardiac autonomic regulation after lung exposure to carbon nanotubes. Human Exp Toxicol. 28: 369–375. [DOI] [PubMed] [Google Scholar]
  33. Lewandowski J, Siński M, Bidiuk J, Abramczyk P, Dobosiewicz A, Ciarka A, and Gaciong Z 2010. Simvastatin reduces sympathetic activity in men with hypertension and hypercholesterolemia. Hypertens Res. 33:1038–1043. [DOI] [PubMed] [Google Scholar]
  34. Lewington S, Clarke R, Qizilbash N, Peto R, Collins R 2002. Prospective Studies Collaboration. Age-specific relevance of usual blood pressure to vascular mortality: a meta-analysis of individual data for one million adults in 61 prospective studies [published correction appears in Lancet. 2003 Mar 22;361(9362):1060]. Lancet. 360:1903–1913. [DOI] [PubMed] [Google Scholar]
  35. Liao D, Shaffer ML, He F, Rodriguez-Colon S, Wu R, Whitsel EA, Bixler EO, & Cascio WE 2011. Fine particulate air pollution is associated with higher vulnerability to atrial fibrillation--the APACR study. Journal of toxicology and environmental health. Part A. 74(11): 693–705. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Lin C, Ceburnis D, Hellebust S, Buckley P, Wenger J, Canonaco F, Prévôt A, Huang RJ, O’Dowd C, & Ovadnevaite J 2017. Characterization of Primary Organic Aerosol from Domestic Wood, Peat, and Coal Burning in Ireland. Environmental science & technology. 51(18), 10624–10632. [DOI] [PubMed] [Google Scholar]
  37. Mao S, Chen G, Liu F, Li N, Wang C, Liu Y, Liu S, Lu Y, Xiang H, Guo Y, Li S 2020. Long-term effects of ambient air pollutants to blood lipids and dyslipidemias in a Chinese rural population. Environ Pollut. 256:113403. [DOI] [PubMed] [Google Scholar]
  38. Martin BL, Thompson LC, Kim Y, Williams W, Snow SJ, Schladweiler MC, Phillips P, King C, Richards J, Haykal-Coates N, Higuchi M, Ian Gilmour M, Kodavanti UP, Hazari MS, & Farraj AK 2018. Acute peat smoke inhalation sensitizes rats to the postprandial cardiometabolic effects of a high fat oral load. The Science of the total environment. 643, 378–391. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. McGuinn LA, Coull BA, Kloog I, Just AC, Tamayo-Ortiz M, Osorio-Yáñez C, Baccarelli AA, Wright RJ, Téllez-Rojo MM, and Wright RO 2020. Fine particulate matter exposure and lipid levels among children in Mexico City. Environ Epidemiol. :e088. [DOI] [PMC free article] [PubMed]
  40. Mills NL, Miller MR, Lucking AJ, Beveridge J, Flint L, Boere AJF, Fokkens PH, Boon NA, Sandstrom T, Blomberg A, Duffin R, Donaldson K, Hadoke PWF, Cassee FR, and Newby DE 2011. Combustion-derived nanoparticulate induces the adverse vascular effects of diesel exhaust inhalation. Eur Heart J. 32:2660–2671. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Miranda AI, Martins V, Cascão P, Amorim JH, Valente J, Borrego C, Ferreira AJ, Cordeiro CR, Viegas DX, & Ottmar R 2012. Wildland smoke exposure values and exhaled breath indicators in firefighters. Journal of toxicology and environmental health. Part A. 75(13–15):831–843. [DOI] [PubMed] [Google Scholar]
  42. Noris M and Remuzzi G 2013Overview of complement activation and regulation. Semin Nephrol. 33:479–492. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Perez CM, Hazari MS, Ledbetter AD, Haykal-Coates N, Carll AP, Cascio WE, Winsett DW, Costa DL, and Farraj AK 2015. Acrolein inhalation alters arterial blood gases and triggers carotid body-mediated cardiovascular responses in hypertensive rats. Inhal Toxicol. 27: 54–63. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Rabha R, Ghosh S, and Padhy PK 2018. Indoor air pollution in rural north-east India: Elemental compositions, changes in haematological indices, oxidative stress and health risks. Ecotoxicol Environ Saf. 165:393–403. [DOI] [PubMed] [Google Scholar]
  45. Rappold AG, Stone SL, Cascio WE, Neas LM, Kilaru VJ, Carraway MS, Szykman JJ, Ising A, Cleve WE, Meredith JT, Vaughan-Batten H, Deyneka L, and Devlin RB 2011. Peat bog wildfire smoke exposure in rural North Carolina is associated with cardiopulmonary emergency department visits assessed through syndromic surveillance. Environ Health Perspect. 119:1415–1420. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Rein G (2013). Smouldering fires and natural fuels. In Fire Phenomena and the Earth System, Belcher CM (Ed.).PAGES???
  47. Routledge HC, Manney S, Harrison RM, Ayres JG, and Townend JN 2006. Effect of inhaled sulphur dioxide and carbon particles on heart rate variability and markers of inflammation and coagulation in human subjects. Heart. 92:220–227. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Swiston JR, Davidson W, Attridge S, Li GT, Brauer M, and van Eeden SF 2008. Wood smoke exposure induces a pulmonary and systemic inflammatory response in firefighters. Eur Respir J. 32:129–138. [DOI] [PubMed] [Google Scholar]
  49. Thompson LC, Kim YH, Martin BL, Ledbetter AD, Dye JA, Hazari MS, Gilmour MI, & Farraj AK 2018. Pulmonary exposure to peat smoke extracts in rats decreases expiratory time and increases left heart end systolic volume. Inhalation toxicology. 30(11–12), 439–447. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Tinling MA, West JJ, Cascio WE, Kilaru V, and Rappold AG 2016. Repeating cardiopulmonary health effects in rural North Carolina population during a second large peat wildfire. Environ Health. 15:12. [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Tong Y, Pei L, Luo K, Zhao M, Xu J, Li A, Li R, Yang M, and Xu Q 2019. The mediated role of complement C3 in PM2.5 exposure and type 2 diabetes mellitus: an elderly panel study in Beijing, China. Environ Sci Pollut Res Int. 26:34479–34486. [DOI] [PubMed] [Google Scholar]
  52. Tong H, Zavala J, McIntosh-Kastrinsky R, & Sexton KG 2019. Cardiovascular effects of diesel exhaust inhalation: photochemically altered versus freshly emitted in mice. Journal of toxicology and environmental health. Part A. 82(17):944–955. [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Tsai SS, Tsai CY, & Yang CY 2018. Fine particulate air pollution associated with increased risk of hospital admissions for hypertension in a tropical city, Kaohsiung, Taiwan. Journal of toxicology and environmental health. Part A. 81(13): 567–575. [DOI] [PubMed] [Google Scholar]
  54. US EPA. 2013. Profile of the 2011 National Air Emissions Inventory. U.S. Environmental Protection Agency; https://19january2017snapshot.epa.gov/sites/production/files/2015-08/documents/lite_finalversion_ver10.pdf. Accessed June 17, 2020. [Google Scholar]
  55. Uda SK, Hein L, Atmoko D 2019. Assessing the health impacts of peatland fires: A case study for Central Kalimantan, Indonesia. Environ Sci Pollut Res Int. 26:31315–31327. [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. Valenti VE, Abreu LC, Saldiva PH, Carvalho TD, and Ferreira C 2010. Effects of sidestream cigarette smoke exposure on baroreflex components in spontaneously hypertensive rats. Int J Environl Health Res. 20: 431–437. [DOI] [PubMed] [Google Scholar]
  57. Vieira JL, Macedo FY, Benjo AM, Guimarães GV, Contreras JP, and Bocchi EA 2017. Systemic effects of controlled exposure to diesel exhaust: A meta-analysis from randomized controlled trials. Ann Med. 49:165–175. [DOI] [PubMed] [Google Scholar]
  58. Wang L, Xiang Z, Stevanovic S, Ristovski Z, Salimi F, Gao J, Wang H, & Li L 2017. Role of Chinese cooking emissions on ambient air quality and human health. The Science of the total environment. 589:173–181. [DOI] [PubMed] [Google Scholar]
  59. Yang BY, Qian Z, Howard SW Vaughn MG, Fan S-J, Liu K-K, Dong G-H 2018. Global association between ambient air pollution and blood pressure: A systematic review and meta-analysis. Environ Pollut. 235:576–588. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Sup1
NIHMS1639361-supplement-Sup1.docx (59KB, docx)

RESOURCES