Fine Particulate Constituents and Lung Dysfunction - ACS Publications

Subscriber access provided by Fudan University

Article

Fine particulate constituents and lung dysfunction: a time-series panel study Shujing Chen, Yutong Gu, Liping Qiao, Cuicui Wang, Yuanlin Song, Chunxue Bai, Yuchun Sun, Haiying Ji, Min Zhou, Hongli Wang, Renjie Chen, and Haidong Kan Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.6b03901 • Publication Date (Web): 05 Jan 2017 Downloaded from http://pubs.acs.org on January 10, 2017

Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a free service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are accessible to all readers and citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.

Environmental Science & Technology is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

Page 1 of 35

Environmental Science & Technology

Fine particulate constituents and lung dysfunction: a time-series panel study

Shujing Chen1,†, Yutong Gu1,†, Liping Qiao2, Cuicui Wang3, Yuanlin Song1, Chunxue Bai1, Yuchun Sun4, Haiying Ji1, Min Zhou2, Hongli Wang2, Renjie Chen3,5*, Haidong Kan3,5*

†

Co-first authors that contributed equally to this work

1

Department of Pulmonary Medicine, Zhongshan Hospital, Fudan University,

Shanghai 200032, China; 2

State Environmental Protection Key Laboratory of the Formation and

Prevention of Urban Air Pollution Complex, Shanghai Academy of Environmental Sciences, Shanghai 200233, China; 3

School of Public Health, Key Lab of Public Health Safety of the Ministry of

Education and Key Lab of Health Technology Assessment of the Ministry of Health, Fudan University, Shanghai 200032, China; 4

Medical department, Zhongshan Hospital, Fudan University, Shanghai

200032, China; 5

Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention

(LAP3), Fudan University, Shanghai 200433, China.

1

ACS Paragon Plus Environment


Page 2 of 35

*Address correspondence to Dr. Haidong Kan, P.O. Box 249, 130 Dong-An Road, Shanghai 200032, China.Tel/fax: +86 (21) 5423 7908. E-mail: [email protected]; or Renjie Chen, PhD., P.O. Box 249, 130 Dong-An Road, Shanghai

200032,

China.

Tel/fax:

+86

(21)

[email protected].

2


5423

7908.

E-mail:

Page 3 of 35


1

ABSTRACT (196 words):

2

The evidence is quite limited regarding the constituents of fine particulate

3

matter (PM2.5) responsible for lung dysfunction. We designed a time-series

4

panel study in 28 patients to examine the effects of 10 major constituents of

5

PM2.5 on lung function with repeated daily measurements from December 2012

6

to May 2013 in Shanghai, China. We applied a linear mixed-effect model

7

combined with a distributed lag model to estimate the cumulative effects of

8

PM2.5 constituents on morning/evening forced expiratory volume in 1-s (FEV1)

9

and peak expiratory flow (PEF) over a week. The cumulative decreases in

10

morning FEV1, evening FEV1, morning PEF and evening PEF associated with

11

an interquartile range (35.8 µg/m3) increase in PM2.5 concentrations were

12

33.49 [95% confidence interval(CI):2.45,54.53] mL, 16.80 (95%CI:3.75,29.86)

13

mL, 4.48 (95%CI:2.30,6.66) L/min, and 1.31 (95%CI:-0.85,3.47) L/min,

14

respectively. These results were not substantially changed after adjusting for

15

gases in two-pollutant models. The associations of elemental carbon (EC) and

16

nitrates with morning/evening FEV1, and the associations of EC and sulfates

17

with morning PEF were robust after controlling for PM2.5. This study

18

demonstrated that short-term exposure to PM2.5 was associated with reduced

19

pulmonary function. Some constituents (EC, sulfate and nitrate) may be

20

responsible for the detrimental effects.

21

Key words: air pollution; PM2.5 constituents; lung function; time-series; panel

22

study 3



23

Page 4 of 35

INTRODUCTION Although a number of studies have demonstrated that short-term exposure

24 25

to

ambient

air

pollution

26

hospitalizations

27

components of an air pollution mixture that are mainly responsible for these

28

effects remain to be elucidated.1, 2 As an indicator of the severity of respiratory

29

diseases, reduced lung function is associated with exposure to air pollutants in

30

panel studies.1,

31

(PM2.5) is most widely investigated in epidemiological studies. PM2.5 has a very

32

complex chemical composition, making it difficult to determine which

33

constituents dominate the effects of PM2.5 on pulmonary function. However,

34

investigations of the effects of specific constituents on lung function are very

35

scarce or limited to a small fraction of constituents, such as carbonaceous

36

components and several metals.5-8

and

3, 4

is

mortality,

associated the

with

pathogenic

increased mechanisms

respiratory and

the

Among all criteria air pollutants, fine particulate matter

37

Chronic obstructive pulmonary disease (COPD) is one of the major causes

38

of human deaths, and it has been estimated that 328 million people suffer from

39

it.9 In recent decades, the worldwide prevalence has increased and by 2020,

40

COPD is projected to be the third leading cause of death.9 Despite a recent

41

reduction of COPD standardized mortality rates, COPD continues to pose a

42

public health problem because of an aging population, high smoking rates and

43

severe air pollution in developing countries, such as China.10 Although there is

44

now clear evidence that air pollution can increase the incidence, prevalence 4


Page 5 of 35


45

and mortality of COPD, it remains unclear how the lung function of COPD

46

patients responds to the day-to-day variations in PM2.5 and its components.1, 4

47

This kind of information was also beneficial to the daily management of COPD

48

patients, especially in regions with poor air quality.

49

We therefore examined the effects of various PM2.5 constituents on lung

50

function in a time-series panel of COPD patients with repeated daily

51

measurements over a 6-month period in Shanghai, China.

52 53

MATERIALS AND METHODS

54

Design and population.

55

time-series measurements of lung function from December 10th, 2012 to May

56

20th, 2013 among 30 participants who resided in different urban districts of

57

Shanghai, China. This specific period was chosen to empirically capture a

58

moderate-to-high level of air pollution and a low-to-moderate lung function.

59

They are all male patients with clinically-diagnosed COPD admitted to

60

Zhongshan Hospital Fudan University. Inclusion criteria were the ratio of

61

forced expiratory volume in 1-s (FEV1) to forced vital capacity (FVC) less than

62

70%, FEV1% predicted < 80% and no exacerbations in the previous 4 weeks.

63

We excluded those with clinically-diagnosed cardiovascular comorbidities (i.e.,

64

hypertension, coronary heart diseases and stroke) to avoid possible influences

65

of complex medications and weakened health conditions on our results. The

66

Institutional Review Board of Zhongshan Hospital Fudan University approved

This is a longitudinal panel study with daily

5



67

the study protocol (NO. 2011-205). We obtained informed written consent from

68

all subjects. Two participants quit the follow-ups due to acute exacerbations of

69

COPD, so a total of 28 subjects were finally evaluated in this analysis.

70

At the baseline survey, we collected data on individual information such as

71

age, weight, height, education, medication use and smoking (status and

72

pack-years). We also recorded their information on COPD including the

73

classifications of Global Initiative for Chronic Obstructive Lung Disease (GOLD)

74

at baseline. All patients received bronchodilator treatment (Tiotropium Bromide,

75

etc.) once per day (in the morning) and were requested to record any change

76

of medication and whether they were experiencing an exacerbation of COPD

77

using a simple questionnaire.

78

Pulmonary function test (PFT)

79

spirometry using JAEGER MasterScreen PFT (CareFusion, Hoechberg,

80

Germany) according to the standardized procedures of the American Thoracic

81

Society and European Respiratory Society.11 During each follow-up

82

appointment, we requested each subject to measure their morning and

83

evening lung function every day using the Peak Flow Meter AM3. The morning

84

PFTs were taken between 7 a.m. and 9 a.m. before they inhaled

85

bronchodilators and the evening tests were performed 12 hours later. This

86

device can automatically record forced expiratory volume in 1 second (FEV1)

87

and peak expiratory flow (PEF) together with the date and time in its memory.

88

At recruitment, we measured baseline

To perform a valid PEF measurement with the AM3, patients were instructed 6


Page 6 of 35

Page 7 of 35


89

how to take a “standardized” PFT at the outset of the study, and were

90

reminded by telephone regularly and/or re-educated when they visited the

91

hospital. In brief, subjects inhale deeply and hold their breath until they have

92

positioned the mouthpiece of the instrument into his/her mouth. Then, the

93

subject exhales as quickly as possible with maximum effort for at least 2

94

seconds to obtain a satisfactory measurement. For each morning or evening

95

test, the subjects were mandated to perform three consecutive valid

96

measurements, which were defined by 1) breathing volume >0.47 L and 50 L/min; and 3) FVC >FEV1. This test was repeated if the

98

range of three measurements were > 5%. The best values of three valid

99

measurements were stored in the AM3. The daily data on PM2.5 constituents were collected by

100

Environmental data

101

a fixed-site monitor located on the rooftop of a five-story building at the

102

Shanghai Academy of Environmental Sciences in the southwest region of the

103

central urban area of Shanghai. It is a representative urban monitoring site

104

which was mostly surrounded by commercial properties and residential

105

dwellings.12 The mass concentration of PM2.5 was measured by an online

106

particulate monitor (FH 62 C14 series, Thermo Fisher Scientific Inc.) using

107

beta attenuation equipped with a verified PM2.5 cyclone. Organic carbon (OC)

108

and elemental carbon (EC) in PM2.5 were measured by a semi-continuous

109

OC/EC analyzer (model RT-4, Sunset Laboratory Inc.) equipped with a PM2.5

110

cyclone and an upstream parallel-plate organic denuder (Sunset Laboratory 7



111

Inc.). Briefly, PM2.5 was sampled on a quartz filter in the oven at a flow rate of

112

5.0 L/min, and analyzed by the thermal-optical transmittance method with a

113

two-stage thermal procedure (600-840 °C in a He atmosphere and

114

550-650-870 °C in an oxidizing atmosphere of 2% O2 with He as dilute gas).

115

The concentrations of 8 major water-soluble inorganic ions in PM2.5, including

116

chlorine (Cl−), nitrate (NO3−), sulfate (SO42−), ammonium (NH4+), sodium (Na+),

117

potassium (K+), magnesium (Mg2+), calcium (Ca2+) were measured by a

118

commercial instrument for online Monitoring of Aerosols and Gases (MARGA,

119

model ADI 2080, Applikon Analytical B.V.). The detailed principle and

120

operation of this instrument has been described in detail elsewhere.13 The

121

Quality Assurance / Quality Control procedures, including maintenance /

122

cleaning for this instrument as well as calibrations for air flow rate, mass foil,

123

and temperature/pressure were conducted according to the Technical

124

Guideline of Automatic Stations of Ambient Air Quality in Shanghai based on

125

the national specification HJ/T193–2005. External standard solution calibration

126

for MARGA and sucrose solution calibration for OC/EC analyzer were carried

127

out quarterly. OC/EC analyzer automatically performed a blank check at each

128

midnight each day. For ions, blank correction was also regularly performed

129

based on absorption solutions.

130

The time resolution was 1 hour for each sample with 45 min sampling and

131

15 min analysis. The instrument can effectively measure the total PM2.5 mass

132

at a concentration as low as 4 µg/m3. The limits of detection (LOD) for all ions 8


Page 8 of 35

Page 9 of 35


133

are 0.10 µg/m3, except for K+ (0.16 µg/m3), Mg2+ (0.12 µg/m3) and Ca2+ (0.21

134

µg/m3). The LODs for OC and EC are 0.5 µg/m3 and 0.2 µg/m3, respectively 14.

135

For the data below the LODs, we still introduced them into the main analysis

136

by giving them the values of 0.5 × LODs.14 We then calculated the daily

137

average concentrations when at least 75% of hourly measurements were

138

available; otherwise, all measurements on that particular day were excluded

139

from the analysis.

140

To allow for the adjustment of weather conditions, we collected daily mean

141

temperature and relative humidity from a meteorological station (Xujiahui

142

station) of the Shanghai Meteorological Bureau, located in the central urban

143

area of Shanghai.

144

To allow for the sensitivity analysis by the simultaneous exposure to

145

gaseous pollutants, we also collected daily concentrations of sulfur dioxide

146

(SO2), nitrogen dioxide (NO2), carbon monoxide (CO) and ozone (O3) from the

147

Shanghai Environmental Monitoring Center. The 24 h mean concentrations for

148

SO2, NO2 and CO and maximum-8 h average concentration of O3 were simply

149

averaged from 9 state-controlling monitoring stations, which are located in 7

150

urban districts (Putuo, Yangpu, Huangpu, Hongkou, Jing'an, Xuhui, and

151

Pudong).

152

Statistical analysis

153

commonly applied to evaluate the associations between air pollutants and

154

quantitative clinical or subclinical outcomes in previous repeated-measure

The linear mixed-effect (LME) model was most

9



Page 10 of 35

155

panel studies.8, 15-18 It has the advantage of modeling heterogeneity between

156

subjects

157

variance-covariance structure assumption that each pair of repeated

158

measurements is correlated, as well as accounting for within-subject

159

correlations due to repeated measurements by simply including a random

160

intercept for each subject.19 We thus used the LME model to evaluate the

161

associations between PM2.5 concentrations and daily variations in lung

162

function.16 In the present study, lung function parameters followed approximate

163

normal distributions and were considered as response variables one at a time.

164

PM2.5 or one of its constituents was introduced as a fixed-effect independent

165

variable and a random-effect intercept was introduced for each subject.

(for

example,

than

the

General

Linear

Model)

with

the

166

For our main analyses, we incorporated several fixed-effect covariates in the

167

single-constituent model: 1) individual characteristics (age and body mass

168

index), socioeconomic status (educational attainment), and behavioral risk

169

factors (smoking status and pack-years) that can vary among subjects; 2) a

170

factor variable of GOLD classifications to adjust for possible influences of

171

disease severity at baseline; 3) a natural spline of calendar day with 3 degrees

172

of freedom (df) to adjust for the unmeasured time trends in lung function during

173

the study period (about 6 months); 4) an indicator variable for day of the week

174

to adjust for possible variations within a week; 5) a natural spline of daily mean

175

temperature with 3 df and a natural spline of daily mean relative humidity with

176

3 df to adjust for the potential confounding effects of weather conditions.4, 17 In 10


Page 11 of 35


177

order to estimate the potentially lagged effect, we included an air pollutant or

178

constituent in the LME model as a “cross-basis” function, which was

179

constructed using a polynomial distributed lag model (DLM).20 The DLM has

180

the advantage of estimating cumulative effects of an exposure on multiple

181

days after adjusting for its collinearity on neighboring days. In this model, we

182

used a natural spline with 3 df in the lag space and a maximum lag of 7 days

183

based on previous studies.18, 21

184

In addition to the aforementioned basic LME model, we also established a

185

“constituent-PM2.5 model” and “constituent-residual model” to assess the

186

robustness in the effect estimation of a constituent.12,

187

constituent-PM2.5 model, we introduced total PM2.5 mass as a surrogate of all

188

other constituents rather than each constituent alternately to control for their

189

confounding effects. For the constituent-residual model, we replaced the

190

constituent with its residual in the basic model. The residual was obtained by

191

establishing a linear regression model between total PM2.5 and a constituent,

192

and thus may be interpreted as a crude measure of its “independent”

193

contribution to the observed effects of PM2.5 after excluding its collinearity with

194

other constituents.12, 15, 18, 22

15,

18

In the

195

At last, we conducted two sensitivity analyses. Firstly, we fit the

196

single-constituent model by deleting those hourly concentrations lower than

197

the LODs or retaining them without any changes. Secondly, we fit two-pollutant

198

models to assess whether the effects of PM2.5 were dependent by the 11



199

simultaneous exposure to gaseous pollutants.

200

All models were performed using R software (Version 3.1.2, R Foundation

201

for Statistical Computing, Vienna, Austria) with the lme4 package for LMEs

202

and the dlnm package for DLMs. All statistical tests were two-sided with a

203

significant level at p < 0.05. Combing the LME model and DLM, all results were

204

presented as cumulative changes and 95% confidence intervals (CIs) in lung

205

function parameters associated with an interquartile range (IQR) increase in

206

concentrations of PM2.5 or its constituents over lags of 0 to 7 days.

207 208

RESULTS

209

Descriptive statistics. Table 1 shows the basic characteristics of all

210

participants (n=28, males) at enrollment, including age, body mass index,

211

smoking, lung function and GOLD classification. All participants suffered from

212

moderate-to-severe COPD.

213

Table 2 summarizes the statistics on lung function, air pollution and weather

214

conditions throughout the follow-up period (162 days). Because 5% of the

215

morning measurements and 6% of the evening measurements were missing,

216

we finally obtained a total of 8,618 pairs of morning PFTs and 8,528 pairs of

217

evening PFTs. During the study period, pulmonary function measurements

218

were highly consistent in the same subjects with the coefficients of variation

219

ranging from 5% to 25%. On individual level, the averaged morning FEV1,

220

evening FEV1, morning PEF and evening PEF were 530~1930 mL, 550~2020 12


Page 12 of 35

Page 13 of 35


221

mL, 97~368 L/min, and 102~391 L/min, respectively. The medians were quite

222

similar with the means. The measurements were less consistent among

223

subjects with larger coefficients of variation (48% for morning FEV1, 40% for

224

evening FEV1, 46% for morning PEF and 23% for FEV1).

225

During the study period, the average concentrations of PM2.5 were 54.2

226

µg/m3, far beyond the guidelines issued by the World Health Organization

227

(annual mean: 10 µg/m3). There was some missing daily data (2~15%) for

228

various PM2.5 constituents due to system maintenance. There were some

229

hourly data below the LODs (Cl-, 25%; K+, 3%; mg2+, 30%; Ca2+, 3%).

230

According to the results of simple correlation analyses, air pollutants other

231

than O3 were closely correlated. OC, EC, Cl-, NO3-, SO42- and NH4+ were

232

strongly related with PM2.5 total mass (Spearman r: 0.61~0.89).

233

Regression results

234

with morning/evening FEV1 and morning PEF. The association between PM2.5

235

and lung function was stronger in the morning than in the evening. The effect

236

estimates attenuated from lag 0 to lag 7 days and thereafter turned out to be

237

null (data not shown). We then estimated the cumulative effects of PM2.5 and

238

its constituents over lags of 0 to 7 days. For example, an IQR increase (35.8

239

µg/m3) in PM2.5 concentrations was associated with decreases of 33.49

240

(95%CI:12.45, 54.53) mL in morning FEV1, 16.80 (95%CI:3.75, 29.86) mL in

241

evening FEV1, 4.48 (95%CI:2.30, 6.66) L/min in morning PEF and 1.31

242

(95%CI:-0.85, 3.47) L/min in evening PEF.

As shown in Figure 1, PM2.5 was inversely associated

13



243

Figure 1 summarizes the estimated cumulative effects of PM2.5 constituents

244

on lung function measures over lags of 0 to 7 days in single-constituent models.

245

The associations varied appreciably by constituents, lung function parameters

246

and time of measurements. OC, EC, NO3- and NH4+ were significantly

247

associated with reduced FEV1 in the morning. Their associations attenuated

248

but were still statistically significant in evening except for NH4+. OC, EC, Cl-,

249

NO3-, SO42- and NH4+ were significantly associated with decreased PEF in the

250

morning, but there were no associations with evening PEF (See TOC art).

251

After controlling for PM2.5 total mass using the constituent-PM2.5 model, the

252

effects of most constituents decreased with wider confidence intervals (see

253

Figure 2). Notably, the associations of EC and NO3- with FEV1, and the

254

associations of EC and SO42- with morning PEF, were strengthened and

255

remained statistically significant. The constituent-residual model estimated

256

very similar results with the constituent-PM2.5 model (See Figure 3).

257

In sensitivity analyses, our results of PM2.5 were not substantially changed

258

after adjusting for gaseous pollutants using the two-pollutant models (see

259

Table 3). Cl- and Mg2+ had appreciable proportions of hourly values below the

260

LODs. Their effect estimates were similar but the confidence intervals were

261

larger when deleting these data, and were almost the same when retaining

262

them without any changes (data not shown). K+ and Ca2+ had little values

263

below the LODs and thus their results were not changed in this sensitivity

264

analysis. 14


Page 14 of 35

Page 15 of 35


265 266

DISCUSSION

267

This time-series panel study investigated the associations of PM2.5 and its

268

constituents with lung function in a group of COPD patients in a Chinese city

269

with much higher air pollution levels than North America and Europe. We found

270

that PM2.5 was significantly associated with decreased FEV1 or PEF in both

271

single-pollutant

272

constituents (EC, sulfate and nitrate) were robustly associated with reduced

273

lung function in single-constituent models, constituent-PM2.5 models and

274

constituent-residual models. This is one of the few studies in developing

275

countries to examine the effects of PM2.5 components on respiratory health.

and

two-pollutant

models.

Furthermore,

three

PM2.5

276

Although there is strong evidence of a short-term association between air

277

pollution and respiratory morbidity or mortality, investigations on the acute

278

effects of air pollution on lung function in adults with or without established lung

279

diseases (such as COPD) are currently limited and the results are largely

280

inconsistent.1, 3, 4 Similar to some previous studies,23-25 we found significant

281

decrements in FEV1 and PEF associated with PM2.5. Besides, the effects of

282

PM2.5 were still robust after simultaneously controlling for all other pollutants.

283

However, other studies have not shown a link between PM and short-term

284

changes in lung function among those with COPD. For example, in a

285

time-series panel study in 94 COPD patients, all air pollutants (PM10, SO2, NO2,

286

CO, O3) were not significantly associated with lung function.26 Another small 15



Page 16 of 35

287

panel study including 17 subjects with COPD showed no consistent

288

association between PM with lung function over 12 days.6 The inconsistency

289

might be due to the study design, varying sample size, air pollution

290

characteristics and susceptibility of the subjects.

291

A better understanding of the PM2.5 chemical constituents that are mainly

292

responsible for such adverse health effects was of the utmost importance to

293

develop more targeted and effective regulations on air quality and to improve

294

public health.18 Nevertheless, these potentially different effects on pulmonary

295

function have not yet been clearly elucidated. Our findings on 10 specific

296

components demonstrated that EC, sulfate and nitrate were robustly

297

associated with reduced FEV1 or PEF. Similarly, Delfino et al found a

298

significant association between personal exposure to EC and daily morning

299

FEV1 in subjects not using bronchodilators.8 However, other studies did not

300

find robust effects of EC, sulfate and nitrate on lung function decrements.6, 18, 27

301

The heterogeneity in these studies might be due to the small magnitude of

302

associations

303

impairments that can be evidenced by virtue of intensively repeated PFTs,

304

personal exposure measurements, and/or in a context of high PM2.5 levels

305

such as our study and Delfino’s study.

between

short-term

PM2.5

exposure

and

lung

function

306

The exact mechanisms behind the robust associations of EC, sulfate and

307

nitrate with lung function decline were unclear, but were somewhat biological

308

plausible. For example, a decline in lung function was usually preceded by 16


Page 17 of 35


309

respiratory inflammation, which can be indicated by the fractional exhaled nitric

310

oxide (FeNO). Our previous studies demonstrated that short-term exposures

311

to the three components could induce significant increments in FeNO and

312

decreases in its encoding gene methylation.15, 28

313

Our findings may have important clinical relevance. COPD will continue to

314

pose an ever-increasing public health problem in developing countries, but

315

limited studies of air pollution have targeted COPD patients specifically. Our

316

results suggest that protection from air pollution is critical for the daily

317

management of COPD. First, we identified that an IQR increase in PM2.5

318

concentrations was associated with statistically significant decrements in lung

319

function, i.e., 4% in morning FEV1, 2% in evening FEV1 and 3% in morning

320

PEF. Although these changes were not substantial, they were still clinically

321

meaningful in that the effects of acute exposure examined in such a study are

322

much lower than of cumulative exposure, which would require a cohort study

323

conducted over years of follow-up to be determined. Also, such a moderate

324

decline of FEV1 and PEF in COPD patients may precede an exacerbation of

325

symptoms such as shortness of breath, chest distress and cough. Second, we

326

found that the adverse effects could last up to one week, implying that

327

protective measures would be expected to last for at least one week after an

328

air pollution episode. Third, our results showed stronger effects in the morning

329

than in the evening, which may be because daily morning lung function was

330

tested before inhaling a long-acting bronchodilator and thereby the 17



331

associations between air pollutants and decrements in morning lung function

332

may be strengthened. These results suggested some potential benefits for the

333

use of bronchodilators in alleviating the hazardous effects of air pollution on

334

lung function. Similarly, a previous study among COPD patients also

335

demonstrated much stronger associations in subjects not taking controller

336

bronchodilators than those taking them.8 Therefore, our findings might provide

337

some useful references for clinicians to better treat COPD patients when

338

encountering an air pollution episode.

339

Our study had two major strengths. First, lung function was intensively

340

tested with 162 pairs of morning/evening measurements per subject on

341

average, lending stable support to exploring the acute effects of air pollution on

342

pulmonary function because of its wide range of variations. Second, this study

343

was based on intensive follow-ups and a relatively wide range of PM2.5

344

constituents, and thus provided a unique opportunity to explore which

345

components of PM2.5 contributed most to the effects on lung function.

346

Some limitations of our study should be noted. Firstly, as in most previous

347

time-series studies and many panel studies, exposure measurement errors

348

were inevitable because data from one or several monitoring stations were

349

utilized to represent the individual exposure for participants residing in an

350

area.8 The results from two-pollutant models were more difficult to be

351

interpreted because of the double measurement errors with different

352

magnitude and the correlations between the pollutants. However, fixed-sited 18


Page 18 of 35

Page 19 of 35


353

measurement may serve as a good surrogate of individual exposure to air

354

pollutants (and components) that have few indoor sources in time-series and

355

panel studies.6, 29 Further, this resulting non-differential error may lead to an

356

underestimate on the effects of PM2.5 and its constituents.30 Secondly, the

357

sample size in the present study was relatively small and consequently did not

358

provide sufficient statistical power to draw definite conclusions about the

359

short-term associations between air pollution and pulmonary function

360

measures. Thirdly, due to the limitations of our monitoring instruments, we did

361

not evaluate metals in PM2.5, which have been associated with adverse

362

respiratory outcomes.5-7 Thus, the effects of metal components on lung

363

function merited further investigations, although they contributed only a very

364

small proportion of PM2.5 total mass. Fourthly, there was some uncertainty

365

when extrapolating our results to females, but we may still expect a little larger

366

effect in females because they are somewhat susceptible to air pollution based

367

on previous literatures.31

368

In summary, this time-series panel study demonstrated that short-term

369

exposure to PM2.5 was significantly associated with reduced pulmonary

370

function. Some constituents (EC, sulfate and nitrate) might be responsible for

371

these detrimental effects. Our results contributed to the very limited scientific

372

literature on respiratory effects of PM2.5 components for high exposure settings

373

typical in developing countries. Further investigations with larger sample size,

374

personal exposure assessment and comprehensive measurements of PM2.5 19



375

compositions are needed to confirm our findings.

20


Page 20 of 35

Page 21 of 35


376

Acknowledgements: This work was funded by the Public Welfare Research

377

Program of National Health and Family Planning Commission of China

378

(201502003), the State Key Basic Research Program (973) project

379

(2015CB553404), the Science and Technology Commission of Shanghai

380

Municipality (134119a4900), the National Natural Science Foundation of China

381

(81502775 and 91643205), and China Medical Board Collaborating Program

382

(13-152). We thank the NIH Fellows Editorial Board for their help with

383

language improvements.

384 385

The authors declare they have no actual or potential competing financial interests.

21



REFERENCES (1) Paulin, L.; Hansel, N., Particulate air pollution and impaired lung function. F1000Res 2016, 5. pii: F1000 Faculty Rev-201. (2) Xing, Y. F.; Xu, Y. H.; Shi, M. H.; Lian, Y. X., The impact of PM2.5 on the human respiratory system. J. Thorac. Dis. 2016, 8 (1), E69-74. (3) Ko, F. W.; Hui, D. S., Outdoor air pollution: impact on chronic obstructive pulmonary disease patients. Curr. Opin. Pulm. Med. 2009, 15 (2), 150-7. (4) Hansel, N. N.; McCormack, M. C.; Kim, V., The Effects of Air Pollution and Temperature on COPD. COPD 2015, 13 (3):372-9. (5) Hong, Y. C.; Hwang, S. S.; Kim, J. H.; Lee, K. H.; Lee, H. J.; Lee, K. H.; Yu, S. D.; Kim, D. S., Metals in particulate pollutants affect peak expiratory flow of schoolchildren. Environ. Health Perspect. 2007, 115 (3), 430-4. (6) Hsu, S. I.; Ito, K.; Lippmann, M., Effects of thoracic and fine PM and their components on heart rate and pulmonary function in COPD patients. J. Expo. Sci. Env. Epid. 2011, 21 (5), 464-472. (7) Lagorio, S.; Forastiere, F.; Pistelli, R.; Iavaronem, I.; Ostro, B. D.; Michelozzi, P.; Fano, V., Air pollution and lung function among susceptible adult subjects: A panel study. Epidemiology 2004, 15 (4), S45-S46. (8) Delfino, R. J.; Staimer, N.; Tjoa, T.; Gillen, D.; Kleinman, M. T.; Sioutas, C.; Cooper, D., Personal and ambient air pollution exposures and lung function decrements in children with asthma. Environ. Health Perspect. 2008, 116 (4), 550-558. 22


Page 22 of 35

Page 23 of 35


(9) Lopez-Campos, J. L.; Tan, W.; Soriano, J. B., Global burden of COPD. Respirology 2016, 21 (1), 14-23. (10)

Yin, P.; Feng, X.; Astell-Burt, T.; Qi, F.; Liu, Y.; Liu, J.; Page, A.; Wang,

L.; Liu, S.; Wang, L.; et al., Spatiotemporal Variations in Chronic Obstructive Pulmonary Disease Mortality in China: Multilevel Evidence from 2006 to 2012. COPD 2015, 13 (3):339-344 (11) Miller, M. R.; Hankinson, J.; Brusasco, V.; Burgos, F.; Casaburi, R.; Coates, A.; Crapo, R.; Enright, P.; van der Grinten, C. P.; Gustafsson, P.; et al., Standardisation of spirometry. Euro. Res. J. 2005, 26 (2), 319-38. (12)

Qiao, L.; Cai, J.; Wang, H.; Wang, W.; Zhou, M.; Lou, S.; Chen, R.; Dai,

H.; Chen, C.; Kan, H., PM2.5 constituents and hospital emergency-room visits in Shanghai, China. Environ. Sci. Technol. 2014, 48 (17), 10406-14. (13)

Wang L., D. H. H., Chen J.M., Zhang M., Huang X.Y., Tan H.B., Kong

L.D., Geng F.H., Consecutive transport of anthropogenic air masses and dust storm plume: Two case events at Shanghai, China. Atmos. Res. 2013,

(127),

22-33. (14)

Makkonen U., V. A., Mäntykenttä J., Hakola H., Keronen P., Vakkari V.,

and Aalto P. P., Semi-continuous gas and inorganic aerosol measurements at a Finnish urban site: comparisons with filters, nitrogen in aerosol and gas phases, and aerosol acidity. Atmos. Chem. Phys. 2012, (15)

(12), 5617-5631.

Chen, R.; Qiao, L.; Li, H.; Zhao, Y.; Zhang, Y.; Xu, W.; Wang, C.; Wang,

H.; Zhao, Z.; Xu, X.; et al. , Fine Particulate Matter Constituents, Nitric Oxide 23



Synthase DNA Methylation and Exhaled Nitric Oxide. Environ. Sci. Technol. 2015, 49 (19), 11859-65. (16)

Chen, R.; Zhao, Z.; Sun, Q.; Lin, Z.; Zhao, A.; Wang, C.; Xia, Y.; Xu, X.;

Kan, H., Size-fractionated particulate air pollution and circulating biomarkers of inflammation, coagulation, and vasoconstriction in a panel of young adults. Epidemiology 2015, 26 (3), 328-36. (17)

Wu, S.; Deng, F.; Hao, Y.; Wang, X.; Zheng, C.; Lv, H.; Lu, X.; Wei, H.;

Huang, J.; Qin, Y.; et al., Fine particulate matter, temperature, and lung function in healthy adults: findings from the HVNR study. Chemosphere 2014, 108, 168-74. (18)

Wu, S. W.; Deng, F. R.; Hao, Y.; Shima, M.; Wang, X.; Zheng, C. J.;

Wei, H. Y.; Lv, H. B.; Lu, X. L.; Huang, J.; et al. , Chemical constituents of fine particulate air pollution and pulmonary function in healthy adults: The Healthy Volunteer Natural Relocation study. J. Hazard. Mater. 2013, 260, 183-191. (19)

Lyit N, G. A., Constitution of Linear Mixed Models (LMMs) in the

Analysis of Correlated Data: Random Intercept Model (RIM) for Repeated Measurements Data. J. Modern Mathe. Stat. 2012, (20)

(3), 60-68.

Gasparrini, A., Modeling exposure-lag-response associations with

distributed lag non-linear models. Stat. Med. 2014, 33 (5), 881-899. (21)

Steinvil, A.; Fireman, E.; Kordova-Biezuner, L.; Cohen, M.; Shapira, I.;

Berliner, S.; Rogowski, O., Environmental Air Pollution Has Decremental Effects on Pulmonary Function Test Parameters Up to One Week After 24


Page 24 of 35

Page 25 of 35


Exposure. Am. J. Med. Sci. 2009, 338 (4), 273-279. (22)

Mostofsky, E.; Schwartz, J.; Coull, B. A.; Koutrakis, P.; Wellenius, G. A.;

Suh, H. H.; Gold, D. R.; Mittleman, M. A., Modeling the association between particle constituents of air pollution and health outcomes. Am. J. Epidemiol. 2012, 176 (4), 317-26. (23)

Liu, L.; Poon, R.; Chen, L.; Frescura, A. M.; Montuschi, P.; Ciabattoni,

G.; Wheeler, A.; Dales, R., Acute effects of air pollution on pulmonary function, airway inflammation, and oxidative stress in asthmatic children. Environ. Health Perspect. 2009, 117 (4), 668-74. (24)

Trenga, C. A.; Sullivan, J. H.; Schildcrout, J. S.; Shepherd, K. P.;

Shapiro, G. G.; Liu, L. J. S.; Kaufman, J. D.; Koenig, J. Q., Effect of particulate air, pollution on lung function in adult and pediatric subjects in a Seattle panel study. Chest 2006, 129 (6), 1614-1622. (25)

Rice, M. B.; Ljungman, P. L.; Wilker, E. H.; Gold, D. R.; Schwartz, J. D.;

Koutrakis, P.; Washko, G. R.; O'Connor, G. T.; Mittleman, M. A., Short-Term Exposure to Air Pollution and Lung Function in the Framingham Heart Study. Am. J. Resp. Crit. Care. 2013, 188 (11), 1351-1357. (26)

Peacock, J. L.; Anderson, H. R.; Bremner, S. A.; Marston, L.;

Seemungal, T. A.; Strachan, D. P.; Wedzicha, J. A., Outdoor air pollution and respiratory health in patients with COPD. Thorax 2011, 66 (7), 591-596. (27)

Strak, M.; Janssen, N. A. H.; Godri, K. J.; Gosens, I.; Mudway, I. S.;

Cassee, F. R.; Lebret, E.; Kelly, F. J.; Harrison, R. M.; Brunekreef, B.; et al., 25



Respiratory Health Effects of Airborne Particulate Matter: The Role of Particle Size, Composition, and Oxidative Potential-The RAPTES Project. Environ. Health Perspect. 2012, 120 (8), 1183-1189. (28)

Shi, J.; Chen, R.; Yang, C.; Lin, Z.; Cai, J.; Xia, Y.; Wang, C.; Li, H.;

Johnson, N.; Xu, X.; et al, Association between fine particulate matter chemical constituents and airway inflammation: A panel study among healthy adults in China. Environ. Res. 2016, 150, 264-8. (29)

Janssen, N. A. H.; Lanki, T.; Hoek, G.; Vallius, M.; de Hartog, J. J.; Van

Grieken, R.; Pekkanen, J.; Brunekreef, B., Associations between ambient, personal, and indoor exposure to fine particulate matter constituents in Dutch and Finnish panels of cardiovascular patients. Occup. Environ. Med 2005, 62 (12), 868-877. (30)

Zeger, S. L.; Thomas, D.; Dominici, F.; Samet, J. M.; Schwartz, J.;

Dockery, D.; Cohen, A., Exposure measurement error in time-series studies of air pollution: concepts and consequences. Environ. Health Perspect. 2000, 108 (5), 419-26. (31)

Clougherty, J. E., A growing role for gender analysis in air pollution

epidemiology. Environ. Health Perspect. 2010, 118 (2), 167-76.

26


Page 26 of 35

Page 27 of 35


Table 1. Baseline characteristics of the study participants (N=28). Characteristics

Measure (mean ± SD)

Age (years)

68 ± 8

Body mass index (kg/m2)

22 ± 3

Smoking status (N) Current smoker

20

Former smoker

8

Never smoker

0

Pack years for smokers

44 ± 12

FEV1 (ml)

1132 ± 335

FEV1%(FEV1predicted)

40 ± 12

FVC (ml)

3130 ± 615

FEV1/FVC (%)

36 ± 10

GOLD grade (N) 2

4

3

17

4

7

Abbreviations: SD, standard deviation; FEV1, forced expiratory volume in 1 second; FVC, forced vital capacity; GOLD, Global Initiative for Chronic Obstructive Lung Disease.

27



Page 28 of 35

Table 2 Descriptive statistics on lung function, air pollution, and weather conditions during the study period (162 days). N

Mean

SD

Min

Median

Max

IQR

Morning FEV1 (mL)

4309

1002

460

340

910

3200

470

Evening FEV1 (mL)

4264

1012

482

290

920

2840

510

Morning PEF (L/min)

4309

185

42

56

176

475

87

Evening PEF (L/min)

4264

186

75

62

177

426

90

constituents

Total mass

159

54.2

33.1

13.0

44.5

174.3

35.8

(µg/m3)

OC (µg C/m3)

138

10.6

5.7

3.8

8.8

34.6

5.9

EC (µg C/m3)

138

2.0

1.2

0.5

1.6

7.8

1.5

Cl-

144

1.3

1.7

0.0

0.9

12.5

1.8

NO3-

144

12.2

8.9

1.5

9.0

41.5

11.0

SO42-

144

13.1

7.6

2.9

11.6

41.5

10.0

Na+

144

0.4

0.2

0.1

0.4

1.1

0.2

NH4+

144

8.1

6.0

0.6

6.1

29.4

7.5

K+

144

1.3

2.3

0.1

1.0

24.0

0.7

Mg2+

144

0.2

0.2

0.0

0.1

2.7

0.1

Ca2+

144

1.1

0.5

0.1

1.0

2.6

0.7

Temperature (℃)

162

10

6

-2

10

25

10

Lung function

PM2.5

Weather conditions

28


Page 29 of 35


Humidity (%)

162

68

13

31

70

94

17

pollutants

SO2

162

25

14

8

22

76

16

(µg/m3)

NO2

162

52

19

24

49

109

29

CO

162

916

333

370

829

2254

402

O3

162

88

38

8

89

213

45

Gaseous

Abbreviations: SD, standard deviation; IQR, interquartile range; FEV1, forced expiratory volume in 1 second; PEF, peak expiratory flow; PM2.5, particulate matter with an aerodynamic diameter less than or equal to 2.5 µm; SO2, sulfur dioxide; NO2, nitrogen dioxide; CO, carbon monoxide; O3, ozone.

29



Table 3. The cumulative decreases (mean and 95% confidence intervals) in lung function measures associated with an interquartile range increase in PM2.5 concentrations over lags of 0 to 7 days, after controlling for gaseous pollutants when using two-pollutant models. Lung function

Pollutants

Morning

Evening

FEV1 (mL)

-

33.49 (12.45, 54.53)

16.80 (3.75, 29.86)

+ SO2

30.08 (7.62, 52.54)

17.52 (4.07, 30.97)

+ NO2

25.78 (6.10, 45.46)

12.56 (1.60, 23.52)

+ CO

26.51 (6.12, 46.90)

13.65 (1.64, 25.66)

+ O3

37.92 (15.25, 60.59)

15.69 (2.45, 28.93)

-

4.48 (2.30, 6.66)

1.31 (-0.85, 3.47)

+ SO2

4.41 (2.45, 6.37)

1.18 (-0.87, 3.23)

+ NO2

3.26 (0.75, 5.77)

0.85 (-1.11, 2.81)

+ CO

3.98 (0.96, 7.00)

1.45 (-1.17, 4.07)

+ O3

4.81 (1.24, 8.38)

1.93 (-1.72, 5.58)

PEF (L/min)

Abbreviations and sample size same as in Table 2.

30


Page 30 of 35

Page 31 of 35


Figure legends Figure 1. The cumulative changes (mean and 95% confidence intervals) in morning FEV1 (A), evening FEV1 (B), morning PEF (C) and evening PEF (D) associated with an interquartile range increase in concentrations of various PM2.5 constituents over lags of 0 to 7 days. Abbreviations and sample size same as in Table 2.

Figure 2. The cumulative changes (mean and 95% confidence intervals) in morning FEV1 (A), evening FEV1 (B), morning PEF (C) and evening PEF (D) associated with an interquartile range increase in concentrations of various PM2.5 constituents over lags of 0 to 7 days, after controlling for the total mass of PM2.5. Abbreviations and sample sizes same as in Table 2.

Figure 3. The cumulative changes (mean and 95% confidence intervals) in morning FEV1 (A), evening FEV1 (B), morning PEF (C) and evening PEF (D) associated with an interquartile range increase in concentrations of various PM2.5 constituents over lags of 0 to 7 days, after controlling for the collinearity of a constituent with PM2.5. Abbreviations and sample sizes same as in Table 2.

31



Figure 1. The cumulative changes (mean and 95% confidence intervals) in morning FEV1 (A), evening FEV1 (B), morning PEF (C) and evening PEF (D) associated with an interquartile range increase in concentrations of various PM2.5 constituents over lags of 0 to 7 days. 338x190mm (96 x 96 DPI)


Page 32 of 35

Page 33 of 35


Figure 2. The cumulative changes (mean and 95% confidence intervals) in morning FEV1 (A), evening FEV1 (B), morning PEF (C) and evening PEF (D) associated with an interquartile range increase in concentrations of various PM2.5 constituents over lags of 0 to 7 days, after controlling for the total mass of PM2.5. 338x190mm (96 x 96 DPI)



Figure 3. The cumulative changes (mean and 95% confidence intervals) in morning FEV1 (A), evening FEV1 (B), morning PEF (C) and evening PEF (D) associated with an interquartile range increase in concentrations of various PM2.5 constituents over lags of 0 to 7 days, after controlling for the collinearity of a constituent with PM2.5. 338x190mm (96 x 96 DPI)


Page 34 of 35

Page 35 of 35


TOC Art 147x120mm (96 x 96 DPI)


Fine Particulate Constituents and Lung Dysfunction - ACS Publications

Recommend Documents