Impact of air quality guidelines on COPD sufferers

Figures & data

Table 1 Outdoor air pollution and COPD-related mortality in both high- and low- to middle-income countries

Authors and yeara	City and country	Number and age of subjects	Study timeperiod	Pollutants and concentrations	Lag days analyzed	Risk type and per unit increase	Risk level (95% CI)
Schwartz and Dockery^Citation55 1992	Philadelphia, USA	≤65 years, >65 years	7 years (1973–1980)	24-hour mean: TSP (µg/m^3) =77 SO2 (µg/m^3) =21	0–1 days	Percent increase in total, cardiorespiratory, and COPD mortalities per increase in TSP and SO2 (100 µg/m^3)	TSP: All causes =7 (4–10) COPD =19 (0–42) SO2: All causes =5 (3–7)
Xu et al^Citation56 1994	Beijing, People’s Republic of China	1,419,123 All ages	1 year (1989)	Seasonal and annual means: TSP (µg/m^3) =375 SO2 (µg/m^3) =102	None	Percent increase in total, cardiorespiratory, and COPD mortalities per doubling increase in natural log of concentration	TSP: All causes =4 (−2–11) Cardiorespiratory =8 COPD =38 SO2: All causes =11 (5–16) Cardiorespiratory =19 COPD =29
Rossi et al^Citation57 1999	Milan, Italy	1.5 million	10 years (1980–1989)	24-hour mean: TSP (µg/m^3) =142 SO2 (µg/m^3) =124 NO2 (µg/m^3) =82	0–4 days	Percent increase in totaland COPD mortalities per increase in 100 µg/m^3 of pollutants	All causes: TSP =3.3 (2.4–4.3) SO2 =2.8 (2.1–3.5) NO2 =7.6 (5.6–9.5) COPD: TSP Lag days 3–4 =12 (6–17) TSP (<200 µg/m^3) =18 (9–27)
Xu et al^Citation58 2000	Shenyang, People’s Republic of China	<65 years, 65–74 years, >74 years	1 year (1992)	Overall mean: TSP (µg/m^3) =430 SO2 (µg/m^3) =197	Current and proceeding 3 days	Percent increase in total, cardiorespiratory, and COPD mortalities per increase in TSP and SO2 (100 µg/m^3)	TSP: All causes =1.7 COPD =2.6 SO2: All causes =2.4 COPD =7.4
Tellez-Rojo et al^Citation59 2000	Mexico City, Mexico	8,600,000 ≥65 years	1 year (1994)	24-hour mean: PM10 (µg/m^3) =75 SO2 (ppb) =20 1-hour: NO2 (ppb) =38 1-hour maximum: O3 (ppb) =134	0–7 days Cumulative by 3 days, 5 days, and 7 days	Percent increase in COPD mortality per increase in PM10 (10 µg/m^3) and O3 (40 ppb)	COPD outside medical unit: PM10=4.1 (1.3–6.9), 3-day lag PM10 =6.1 (2.4–9.9), 5-day mean O3 =8.3 (1.0–16.1), 3-day lag
Wong et al^Citation60 2002	Hong Kong, People’s Republic of China	All ages	3 years (1995–1998)	24-hour mean: PM10 (µg/m^3) =52 SO2 (µg/m^3) =17 NO2 (µg/m^3) =56 O3 (µg/m^3) =34	0–3 days	RR increase in total, respiratory and COPD mortalities per increase in all pollutants (10 µg/m^3)	SO2: Respiratory =1.015 (1.001–1.029) COPD =1.010 (0.990–1.029) O3: Respiratory =1.010 (1.004–1.016) COPD =1.034 (1.017–1.052) NO2: Respiratory =1.013 (1.004–1.022) COPD =1.023 (1.006–1.041) PM10: Respiratory =1.008 (1.001–1.014) COPD =1.017 (1.002–1.033)
Kan et al^Citation61 2003	Shanghai, People’s Republic of China	All ages	1.5 years (2000–2001)	24-hour mean: PM10 (µg/m^3) =91 SO2 (µg/m^3) =42 NO2 (µg/m^3) =32	0–5 days	RR increase in COPD mortality per increase in all pollutants (10 µg/m^3)	All ages: PM10 =1.005 (0.999–1.011) SO2 =1.035 (1.015–1.054) NO2 =1.032 (1.009–1.056) Age 65–75: PM10=0.996 (0.986–1.007) SO2 =1.010 (0.977–1.043) NO2 =1.007 (0.967–1.047)
Zeka et al^Citation62 2005	20 US cities	All ages	12 years (1989–2000)	24-hour mean: PM10 (µg/m^3) =29	N/A	Percent increase in COPD mortality per increase in PM10 (10 µg/m^3)	Single lag model: 0-day lag =−0.06 (−0.63–0.51) 1-day lag =0.43 (−0.14–1.00) 2-day lag =0.39 (−0.16–0.94) 3-day cumulative =0.43 (−0.35–1.21)
Naess et al^Citation63 2007b	Oslo, Norway	143,842 51–90 years	6 years (1992–1998)	24-hour mean: PM10 (µg/m^3) =19 PM25 (µg/m^3) =15 NO2 (µg/m^3) =39	N/A	HR in COPD mortality per quartile increase in all pollutants	Crude HR for age 51–70 years: Males: PM10 =1.33 (1.17–1.50) PM2.5 =1.32 (1.17–1.49) NO2 =1.28 (1.13–1.44) Females: PM10 =1.16 (1.02–1.32) PM2.5 =1.18 (1.03–1.34) NO2 =1.13 (1.00–1.29) Crude HR for age 71–90 years: Males: PM10 =1.13 (1.04–1.24) PM2.5 =1.14 (1.04–1.24) NO2 =1.08 (0.99–1.18) Females: PM10 =1.11 (1.01–1.21) PM2.5 =1.09 (1.00–1.18) NO2 =1.09 (0.99–1.18)
Meng et al^Citation64 2013	Beijing, Shanghai, Guangzhou, Hong Kong, People’s Republic of China	N/A	Beijing =1 year Shanghai =3 years Guangzhou =1 year Hong Kong =6 years	24-hour mean: PM10 (µg/m^3) =92 SO2 (µg/m^3) =38 NO2 (µg/m^3) =62	0–1 day 2-day moving average	Percent increase in COPD mortality per increase in 10 µg/m^3 concentrations	PM10 =0.78 (0.13–1.42) SO2 =1.30 (0.61–1.99) NO2 =1.78 (1.10–2.46)
Samoli et al^Citation65 2014	Ten European Mediterranean metropolitan areas	14 million	10 years (2001–2010)	24-hour mean: PM2.5 (µg/m^3) =20 PM2.5–10 (µg/m^3) =12 PM10 (µg/m^3) =33 NO2 (µg/m^3) =49 O3 (µg/m^3) =63	0–1 days 2–5 days 0–5 days	Percent change in COPD mortality for a 10 µg/m^3 increase in pollutants	Lag 0–1: PM2.5 =1.02 (−0.79–2.87) PM2.5–10 =−1.00 (−4.23–2.33) PM10 =−0.06 (−1.25–1.16) Lag 2–5: PM2.5 =2.53 (−0.33–5.48) PM2.5–10 =0.88 (−3.17–5.09) PM10 =1.43 (−0.39–3.29) Lag 0–5: PM2.5 =2.53 (−0.01–5.14) PM2.5–10 =0.01 (−4.91–5.20) PM10 =1.15 (−0.57–2.90)

Notes:

a Most studies were time series studies to evaluate short-term exposure, in which variables of long-term trends, day of the week, temperature, humidity, dew point temperature, and influenza epidemic were controlled.

b Prospective cohort study to evaluate long-term exposure, in which Cox proportional hazard survival model was used and personal characteristics such as smoking, education, marital status, body mass index, occupational exposures, diet, and alcohol use were controlled. Percent increase = (RR −1) ×100.

Abbreviations: CI, confidence interval; TSP, total suspended particles with aerodynamic diameter ≤40 µm; SO₂, sulfur dioxide; NO₂, nitrogen dioxide; PM₁₀, particulate matter with aerodynamic diameter ≤10 µm; O₃, ozone; RR, relative risk; PM_2.5, particulate matter with aerodynamic diameter ≤2.5 µm; HR, hazard ratio; PM_2.5–10, particulate matter with aerodynamic diameter between 2.5 µm and 10 µm; N/A, not available.

Table 2 Outdoor air pollution and COPD-related hospital admissions or emergency room visits in high-income countries

Authors and year	City and country	Number and age of subjects	Study time period	Pollutants and concentrations	Lag days analyzed	Risk type and per unit increase	Risk level (95% CI)
Sunyer et al^Citation70 1991	Barcelona, Spain	1.7 million >14 years	1 year (1985–1986)	24-hour mean: BS (µg/m^3) =72.9 SO2 (µg/m^3) =56.5 1-hour maximum: SO2 (µg/m^3) =141.9 CO (mg/m^3) =5.4 O3 (µg/m^3) =63.3 NO2 (µg/m^3) =123.5	N/A	Percent increase in COPD per increase in BS and SO2 (1 µg/m^3) and CO (1 mg/m^3)	SO2 =2 BS =1 CO =11
Sunyer et al^Citation71 1993	Barcelona, Spain	1.7 million >14 years	4 years (1985–1989)	24-hour mean: BS (µg/m^3) =62 (winter) SO2 (µg/m^3) =49 (winter)	0–2 days	Percent increase in COPD per increase in BS and SO2 (25 µg/m^3)	SO2: Winter =6 Summer =9
Pönkä andVirtanen^Citation721994	Helsinki, Finland	,65 years	2 years (1987–1989)	24-hour mean: TSP (µg/m^3) =76 SO2 (µg/m^3) =19 NO2 (µg/m^3) =39 O3 (µg/m^3) =22	0–7 days	RR increase in CB and emphysema admission per 2.7 fold increase in pollutants	SO2: Lag 0 =1.31 (1.01–1.70) Lag 3 =1.39 (1.05–1.86) NO2: Lag 6 =1.31 (1.03–1.66)
Schwartz et al^Citation73 1994	Minneapolis, St Paul, USA	2.46 million ≥65 years	4 years (1986–1989)	24-hour mean: PM10 (µg/m^3) =36 O3 (ppb) =26 1-hour maximum: O3 (ppb)	0–1 day	RR increase in COPD per increase in PM10 (100 µg/m^3) and O3 (50 ppb)	PM10 0–1-day weighted average: Above NAAQS =1.57 (1.20–2.06) PM10 0-day lag =1.22 (0.99–1.52) PM10 1-day lag =1.37 (1.12–1.68) Below NAAQS =1.54 (1.16–2.06)
Schwartz^Citation741994	Detroit, USA	≥65 years	4 years (1986–1989)	24-hour mean: PM10 (µg/m^3) =48 O3 (ppb) =21	N/A	RR increase in COPD per increase in PM10 (10 µg/m^3) and O3 (5 ppb)	PM10 =1.020 (1.009–1.032) O3 =1.028 (1.007–1.049)
Schwartz^Citation751994	Birmingham,USA	≥65 years	4 years (1986–1989)	24-hour mean: PM10 (µg/m^3) =45 O3 (ppb) =25 1-hour maximum: O3 (ppb)	1–2 days	RR increase in COPD per increase in PM10 (100 µg/m^3) and O3 (50 ppb)	PM10 =1.27 (1.05–1.08) O3 =1.17 (0.86–1.60) Lag 1
Burnett et al^Citation76 1994	Ontario, Canada	All ages	6 years (1983–1988)	24-hour mean: SO4 (µg/m^3) =5.3 1-hour maximum: O3 (ppb) =50	0–3 days	RR increase in COPD per unit increase and percent increase in COPD per increase in SO4 (5.3 µg/m^3) and O3 (50 ppb)	RR (standard error) increase: SO4: Lag 0 =1.00216 (0.00061) Lag 1 =1.00330 (0.00062) Lag 2 =1.00205 (0.00062) Lag 3 =1.00143 (0.00061) O3: Lag 0 =1.00034 (0.00017) Lag 1 =1.00097 (0.00018) Lag 2 =1.00093 (0.00018) Lag 3 =1.00074 (0.00018) Percent increase for all ages and SO4and O3 combined =5.8
Schuouten et al^Citation77 1996	Amsterdam, Rotterdam, the Netherlands	1.27 million 15–64 years ≥65 years	12 years (1977–1989)	24-hour mean: BS (µg/m^3) =18 SO2 (µg/m^3) =34 NO2 (µg/m^3) =52 8-hour mean: O3 (µg/m^3) =66 1-hour maximum: O3 (µg/m^3) =78 SO2 (µg/m^3) =82 NO2 (µg/m^3) =78	0–5 days	RR increase in COPD per 100 µg/m^3 increase in pollutants	Rotterdam: Daily mean NO2, Lag 2 =1.051 (0.903–1.223) Daily mean NO2, Lag 0–1 = 1.203 (1.011–1.430) 1-hour NO2, Lag 2 =1.166 (1.070–1.271) 1-hour NO2, Lag 0–1 = 1.196 (1.079–1.326)
Anderson et al^Citation78 1997	Six European cities (Amsterdam, Barcelona, London, Milan, Paris, Rotterdam)	All ages	15 years (1977–1992)	24-hour mean: TSP (µg/m^3) =86 BS (µg/m^3) =22 SO2 (µg/m^3) =33 NO2 (µg/m^3) =53 8-hour mean: O3 (µg/m^3) =47 1-hour maximum: SO2 (µg/m^3) =60 NO2 (µg/m^3) =82 O3 (µg/m^3) =57	Best one day lag out of 3 days Cumulative (mean)	RR increase in COPD per increase in all pollutants (50 µg/m^3)	TSP =1.022 (0.998–1.047) BS =1.035 (1.010–1.060) Lag 1 BS =1.038 (1.008–1.070) Lag 0–3 SO2 =1.021 (0.998–1.045) Lag 0–3 NO2 24 hour = 1.019 (1.002–1.047) Lag 1 NO2 24 hour = 1.026 (1.004–1.036) Lag 0–3 O3 8 hour =1.043 (1.022–1.065) Lag 1 O3 8 hour =1.056 (1.027–1.086) Lag 0–3 O3 1 hour =1.029 (1.011–1.047) Lag 1 O3 1 hour =1.024 (1.011–1.075) Lag 0–3
Morgan et al^Citation79 1998	Sidney, Australia	≥65 years	5 years (1990–1994)	24-hour mean: PM0.01–2.0, bscat/l0^4 m =0.32 NO2 (ppb) =15 1-hour maximum: PM0.01–2.0, bscat/l0^4 m =0.76 NO2 (ppb) =29 O3 (ppb) =25	0–2 days and cumulative	Percent increase in COPD per 10th–90th percentile increase in all pollutants	0-day lag: PM24 hours =2.41 (−0.90–5.84) PM1 hour =3.01 (−0.38–6.52) 1-day lag: NO2–24 hours =4.30 (−0.75–9.61) NO2–1 hour =4.60 (−0.17–9.61)
Chen et al^Citation80 2000	Reno-Sparks, NV, USA	≥65 years	4 years (1990–1994)	24-hour mean: PM10 (µg/m^3) =37	0 day	RR increase in COPD per increase in PM10(26.6 µg/m^3)	PM10 =1.049 (1.011–1.087)
Tobert et al^Citation81 2000	Atlanta, USA	N/A	5 years and 7 months (1993–1998)	24-hour mean: PM10 (µg/m^3) =30 8-hour maximum: O3 (ppb) =53.1 1-hour maximum: SO2 (ppb) =14 NO2 (µg/m^3) =46 CO (ppm) =2	0–2 days	RR increase in COPD per increase in PM10 (26.6 µg/m^3), O3 (25 ppb), and NO2 (20 ppb)	1993–1998 period: PM10 =1.011 O3 =1.064 NO2 =1.003
Fusco et al^Citation82 2001	Rome, Italy	3 million All ages	3 years (1995–1997)	24-hour mean: PM13 (µg/m^3) =66 SO2 (µg/m^3) =9 NO2 (µg/m^3) =87 CO (mg/m^3) =3.6 8-hour mean: O3 (µg/m^3) =27	0–4 days	Percent increase in total respiratory diseases including COPD per increase in PM13 (23.0 µg/m^3), SO2 (6.9 µg/m^3), NO2 (22.3 µg/m^3), CO (1.5 mg/m^3), and O3 (3.9 µg/m^3)	0-day lag: Total respiratory diseases: NO2 =2.5 (0.9–4.2) CO =2.8 (1.3–4.3) COPD: CO =4.3 (1.6–7.1)
Tenías et al^Citation83 2002	Valencia, Spain	0.75 million 1,289 COPD case >14 years	2 years (1994–1995)	24-hour mean: BS (µg/m^3) =39 SO2 (µg/m^3) =27 NO2 (µg/m^3) =58 CO (mg/m^3) =3 1-hour maximum: SO2 (µg/m^3) =56 NO2 (µg/m^3) =100 CO (mg/m^3) =7 O3 (µg/m^3) =63	0–5 days	RR increase in COPD per increase in O3 (10 µg/m^3) and CO (1 mg/m^3)	O3 Lag 5 =1.061 (1.022–1.101) CO Lag 1 =1.039 (1.014–1.066)
Chen et al^Citation84 2004	Vancouver, Canada	2 million ≥65 years with acute COPD	3 years and 10 months (1995–1999)	24-hour mean: PM10 (µg/m^3) =13 PM2.5 (µg/m^3) =8 PM10–2.5 (µg/m^3) =6 COH (µg/m^3) =0.3	1–7 days	RR increase in acute COPD per increase in 3-day average exposure of interquartile range	Single-pollutant model: PM10 =1.13 (1.05–1.21) PM2.5 =1.08 (1.02–1.15) PM10–2.5 =1.09 (1.03–1.16) COH =1.05 (1.01–1.09)
Peel et al^Citation85 2005	Atlanta, USA	N/A	7 years and 8 months (1993–2000)	24-hour mean: PM10 (µg/m^3) =28 1-hour mean: NO2 (ppb) =46 CO (ppm) =2	0–2 days	RR increase in acute COPD per increase in PM10 (10 µg/m^3), NO2 (20 ppb), and CO (1 ppm)	PM10 =1.018 (0.994–1.043) NO2 =1.035 (1.006–1.065) CO =1.026 (1.004–1.048)
Yang et al^Citation86 2005	Vancouver, Canada	2 million ≥65 years with acute COPD	5 years (1994–1998)	24-hour mean: PM10 (µg/m^3) =14 SO2 (ppb) =4 NO2 (ppb) =17 CO (ppm) =0.7 O3 (ppb) =14	0–6 days Average	RR increase in acute COPD per increase in PM10 (8.3 µg/m^3), SO2 (2.8 ppb), NO2 (5.5 ppb), CO (0.3 ppm), and O3 (9.3 ppb)	Single-pollutant model for 7-day average exposure: PM10 =1.13 (1.05–1.21) NO2 =1.11 (1.04–1.20) CO =1.08 (1.02–1.13)
Hinwood et al^Citation87 2006	Perth, Australia	1.2 million All ages	6 years (1992–1998)	24-hour mean: NO2 (ppb) =10 PM10 (ppb) =20 PM2.5 (ppb) =9 Bsp (2.1/10^4 m) =0.2 8-hour maximum: O3 (ppb) =26 CO (ppm) =2 1-hour maximum: Bsp (2.1/10^4 m) =1 NO2 (ppb) =25 O3 (ppb) =32	0–3 days and cumulative	OR for COPD hospitalizations per unit increase of Bsp	2-day lag =1.30 (1.05–1.45)
Dominici et al^Citation88 2006	204 counties, USA	11.5 million >65 years	3 years (1999–2002)	24-hour mean: PM2.5 (µg/m^3) =13	0–1 days	Percentage increase in daily admission rate of COPD per 10 µg/m^3 increase in PM2.5 concentration	Lag 0 =1 (0.2–1.7) Lag 1 =1 (0.2–1.7) Lag 2 =0.2 (−0.6–1.0) Total =1.4 (−0.1–2)
Medina-Ramón et al^Citation89 2006	36 cities, USA	≥35 years	13 years (1986–1999)	24-hour mean: PM10 (µg/m^3) =30 8-hour mean: O3 (µg/m^3) =37	0–1 days	Percent increase in COPD admission per increase in PM10 (10 µg/m^3) and O3 ppb)	PM10: Lag 0 =0.29 (−0.01–0.58) Lag 1 =0.59 (0.30–0.88) O3: Lag 0 =−0.32 (−0.49 to −0.15) Lag 1 =0.33 (0.19–0.47) Lag 0–2 =0.04 (−0.13–0.20)
Sauerzapf et al^Citation90 2009	Norfolk, UK	>18 years	13 months (2006–2007)	24-hour mean: CO (µg/m^3) =205 NO (µg/m^3) =8 NO2 (µg/m^3) =23 NOx (µg/m^3) =35 O3 (µg/m^3) =45 PM10 (µg/m^3) =20	0–7 days 1–8 days	OR in COPD admission per increase in all pollutants (10 µg/m^3)	Lag 0–7, adjusted: CO =1.015 (1.005–1.025) NO =1.076 (1.009–1.148) NO2 =1.196 (1.071–1.334) NOx =1.050 (1.014–1.087) O3 =1.015 (0.964–1.069) PM10 =1.101 (0.988–1.226) Lag 1–8, adjusted: CO =1.018 (1.005–1.031) NO =1.070 (1.040–1.316) NO2 =1.220 (1.092–1.362) NOx =1.093 (1.038–1.151) O3 =0.952 (0.906–1.002) PM10 =1.054 (0.949–1.170)
Belleudi et al^Citation91 2010	Rome, Italy	2.7 million ≥35 years	4 years and 8 months (2001–2005)	24-hour mean: PM10 (µg/m^3) =39 PM2.5 (µg/m^3) =23 PMn (n/cm^3) =37,456	0–4 days	Percent increase in COPD admission per increase in PM10 (14 µg/m^3), PM2.5 (10 µg/m^3), and 9,392 particles/cm^3	Lag 0: PM10 =0.40 (−1.41–2.25) PM2.5 =1.88 (−0.27–4.09) PMn =1.59 (0.03–3.18) Lag 6: PM10 =0.17 (−1.41–1.78) PM2.5 =0.37 (−1.42–2.18) PMn =0.39 (−1.03–1.83)
Cirera et al^Citation92 2012	Cartagena, Spain	185,799 ≥65 years	4 years (1995–1998)	24-hour mean: TSP(µg/m^3) =52 SO2 (µg/m^3) =32 NO2 (µg/m^3) =51 8-hour maximum: O3 (µg/m^3) =80	0–5 days	Percent increase for COPD emergency room visits per increase in all pollutants (10 µg/m^3)	Single pollutant model: TSP (lag 0) =3.2 (0.8–5.7) SO2 (lag 0) =6.9 (3.3–10.7) NO2 (lag 0) =3.8 (2.0–5.6) O3 (lag 4) =7.7 (1.9–13.7)
Liu et al^Citation93 2012	New York State, USA	All ages	15 years (1993–2008)	Living close to fuel-fired power plant or hazardous waste site	0–1 day	RR increase in exposure type	Fuel only =1.17 (1.06–1.29) Waste only =1.16 (1.08–1.26) Fuel and waste =1.26 (1.17–1.37)
Faustini et al^Citation94 2013	Six Italian cities	38,577 ≥35 years	5 years (2001–2005)	24-hour mean: PM10 (µg/m^3) =35–54 NO2 (µg/m^3) =46–66	0–5 days	Percent increase in COPD hospitalizations per 10 µg/m^3 of pollutants	PM10 (lag 0) =0.67 (−0.02–1.35) NO2 (lag 0) =1.20 (0.17–2.23)
Kloog et al^Citation95 2014	Mid-Atlanti region, USA	58 million ≥65 years	6 years (2000–2006)	24-hour mean: PM2.5 (µg/m^3) =11–13	0–1 day	Percent increase in COPD per increase in PM2.5 (10 µg/m^3)	Lag 1 =1.83 (1.18–2.48)
Yorifuji et al^Citation96 2014	Okayama, Japan	6,925 residents ≥65 years	5 years (2006–2010)	1-hour mean: PM7 (µg/m^3) =27 SO2 (ppb) =3 NO2 (ppb) =17 O3 (ppb) =26 CO (ppm) =0.6	0–4 day	OR increase in COPD per interquartile range increase in pollutants	O3 Lag 18–24 hours =1.27 (1.03–1.55)

Notes: The time-series studies evaluated short-term effects and mostly used Poisson regression with generalized additive models for data analysis and controlled for long-term trends, day of the week, temperature, humidity, dew point temperature, influenza epidemic, and other factors. Both single-pollutant and multiple-pollutant models and both crude and adjusted risks were used in most studies. Our report focused on single-pollutant models and crude risks or otherwisenoted in the table. Percent increase = (RR -1) ×100.

Abbreviations: CI, confidence interval; BS, black smoke; SO₂, sulfur dioxide; CO, carbon monoxide; O₃, ozone; NO₂, nitrogen dioxide; TSP, total suspended particles with aerodynamic diameter ≤40 µm; RR, relative risk; observed over expected; CB, chronic bronchitis; PM₁₀, particulate matter with aerodynamic diameter ≤10 µm; NAAQS, National Ambient Air Quality Standards; SO₄, sulfate; PM0.01–2.0, particulate matter with aerodynamic diameter between 0.01 µm and 2.0 µm; PM₁₃, particulate matter with aerodynamic diameter ≤13 µm; PM_2.5, particulate matter with aerodynamic diameter ≤2.5 µm; PM_10–2.5, particulate matter with aerodynamic diameter between 10 and 2.5 µm; COH, coefficient of haze, a measurement of the amount offilterable particulate matter suspended in air; bscat/l0⁴ m or Bsp, nephelometer particulate concentration scale with conversion: PM_2.5 (µg/m³) =30× bscat/10⁴ m; OR, odds ratio; NO, nitric oxide; NO_x, nitrogen oxides; PM_n, particulate matter number concentration; PM₇, particulate matter with aerodynamic diameter ≤7 µm; N/A, not available.

Table 3 Outdoor air pollution and COPD-related hospitalizations or emergency room visits in low- to middle- income countries

Authors and year	City/country	Number and age of subjects	Study period	Pollutants and concentration	Lag days analyzed	Risk type and per unit increase	Risk level (95% CI)
Wong et al^Citation97 1999	Hong Kong, People’s Republic of China	N/A	2 years (1994–1995)	24-hour mean: PM10 (µg/m^3) =31 SO2 (µg/m^3) =12 NO2 (µg/m^3) =51 8-hour mean: O3 (µg/m^3) =24	0–5 days	RR increase in COPD per 10 µg/m^3 increase in pollutants	PM10 (Lag 0–3) =1.019 (1.011–1.027) SO2 (Lag 0) =1.023 (1.011–1.035) NO2 (Lag 0–3) =1.029 (1.019–1.040) O3 (Lag 0–5) =1.032 (1.021–1.042)
Burrillo et al^Citation98 2001	Valentia, Spain	207,602	2 years (1994–1995)	24-hour maximum: BS (µg/m^3) 8-hour maximum: O3 (µg/m^3) 1-hour maximum: CO (mg/m^3)	0–5 days	RR increase in COPD emergency visits per 25 µg/m^3 increase in O3 and 3 mg/m^3 increase in CO	O3 =1.142 (1.016–1.283) Lag 4 CO =1.120 (1.045–1.203) Lag 1
Pande et al^Citation99 2002	Delhi, India	All ages	2 years (1997–1998)	24-hour mean: TSP (µg/m^3) SO2 (µg/m^3) NOx (µg/m^3) CO (µg/m^3)	0–7 days	Percent increase in COPD emergency room visits based on upper permissible level of TSP and SO2	24.9
Gouveia et al^Citation100 2006	São Paolo, Brazil	$65 years	4 years (1996–2000)	24-hour mean: PM10 (µg/m^3) =54 SO2 (µg/m^3) =18 NO2 (µg/m^3) =103 CO (ppm) =3 O3 (µg/m^3) =72	0–2 days	RR increase in COPD per 10 µg/m^3 increase in PM10, SO2, NO2, and O3 or per 1 ppm increase in CO	PM10 =1.043 (1.028–1.058) SO2 =1.179 (1.126–1.235) NO2 =1.024 (1.015–1.034) CO =1.049 (1.023–1.076) O3 =1.015 (1.005–1.025)
Yang et al^Citation101 2007	Taipei, Taiwan	All ages	8 years (1996–2003)	24-hour mean: PM10 (µg/m^3) =49 SO2 (ppb) =4 NO2 (ppb) =31 CO (ppm) =1 O3 (ppb) =20	0–2 days	OR increase in COPD admission per increase in PM10, 26.41 (µg/m^3) SO2, 2.79 (ppb) NO2, 10.05 (ppb) CO, 0.53 (ppm) O3, 11.29 (ppb)	>20°C: PM10 =1.133 (1.098–1.168) SO2 =1.006 (0.970–1.043) NO2 =1.193 (1.158–1.230) CO =1.227 (1.178–1.277) O3 =1.157 (1.118–1.197) <20°C: PM10 =1.035 (0.994–1.077) SO2 =1.071 (1.015–1.129) NO2 =0.972 (0.922–1.024) CO =0.975 (0.921–1.033) O3 =0.936 (0.974–1.003)
Lee et al^Citation102 2007	Kaohsiung, Taiwan	1.46 million All ages	8 years (1996–2003)	24-hour mean: PM10 (µg/m^3) =77 SO2 (ppb) =9 NO2 (ppb) =27 CO (ppm) =0.8 O3 (ppb) =26	0–2 days	OR increase in COPD admission per interquartile increase in PM10, 62.28 (µg/m^3) SO2, 5.79 (ppb) NO2, 17.00 (ppb) CO, 0.29 (ppm) O3, 20.32 (ppb)	≥25°C: PM10 =1.273 (1.153–1.406) SO2 =1.024 (0.973–1.077) NO2 =1.241 (1.117–1.379) CO =1.189 (1.123–1.259) O3 =1.266 (1.193–1.344) <25°C: PM10 =1.503 (1.375–1.643) SO2 =1.190 (1.093–1.295) NO2 =1.975 (1.785–2.186) CO =1.398 (1.306–1.496) O3 =1.222 (1.108–1.347)^Citation3
Ko et al^Citation103 2007	Hong Kong, People’s Republic of China	>65 years	6 years (2000–2005)	24-hour mean: PM10 (µg/m^3) =50 PM2.5 (µg/m^3) =36 SO2 (µg/m^3) =15 NO2 (µg/m^3) =51 8-hour mean: O3 (µg/m^3) =31	0–5 days Cumulative by 2 days, 3 days, and 6 days	RR increase in COPD hospitalizations per 10 µg/m^3 increase in all pollutants	Single pollutant (best log): Lag 0–5: PM10 =1.024 (1.021–1.028) PM2.5 =1.031 (1.026–1.036) NO2 =1.026 (1.022–1.031) O3 =1.034 (1.030–1.040) Lag 0: SO2 =1.007 (1.001–1.014)
Arbex et al^Citation104 2009	São Paulo, Brazil	48,109 patients >40 years	3 years (2001–2003)	24-hour mean: PM10 (µg/m^3) =49 SO2 (µg/m^3) =14 NO2 (µg/m^3) =120 O3 (µg/m^3) =96 CO (ppm) =3	0–6 days Cumulative 2–7 days	Percent increase in COPD emergency room visits per increase in PM10, 28.3 µg/m^3 SO2, 7.8 µg/m^3 CO, 1.3 ppm	Lag 0: PM10 =9.8 (1.0–19.3) SO2 =9.1 (6.6–11.6) Lag 0–6: PM10 =18.6 (3.8–35.5) SO2 =16.3 (0.7–34.3) CO =12.8 (2.2–24.6) Lag 3: CO =3.1 (0.6–5.7)
Milutinović et al^Citation105 2009	Niš, Serbia	171,000 All ages	1 year (2002)	24-hour mean: BS (µg/m^3) =21 SO2 (µg/m^3) =16	0–3 days	OR increase in COPD emergency room visits per 10 µg/m^3 increase in BS and SO2	BS: Lag 1 =1.01603 (1.00006–1.03226) Lag 0–2 =1.02257 (1.00180–1.04378)
Qiu et al^Citation106 2013	Hong Kong, People’s Republic of China	All ages	10 years (1998–2007)	24-hour mean: PM10 (µg/m^3) =53 NO2 (µg/m^3) =58 O3 (µg/m^3) =40 SO2 (µg/m^3) =20	0–3 days	Percent increase in excess RR of COPD admission per increase in pollutants, 10 µg/m^3	PM10: Lag 3 =0.74 (0.53–0.95) Lag 03 =1.18 (0.90–1.47) NO2: Lag 3 =1.01 (0.75–1.27) Lag 03 =1.23 (0.87–1.60) O3: Lag 2 =1.22 (0.98–1.47) Lag 03 =2.05 (1.73–2.36) SO2: Lag 3 =0.71 (0.32–1.10) Lag 03 =0.59 (0.02–1.16)
Tsai et al^Citation107 2013	Taipei, Taiwan	2.64 million	5 years (2006–2010)	24-hour mean: PM10 (µg/m^3) =52 PM2.5 (µg/m^3) =30 SO2 (ppb) =4 NO2 (ppb) =25 CO (ppm) =0.7 O3 (ppb) =25	No lag days	OR increase in COPD per 17.46 µg/m^3 increase in PM2.5	Single pollutant model for PM2.5: ≥23°C=1.12 (1.08–1.16) <23°C =1.03 (1.00–1.07)
Ghozikali et al^Citation108 2015	Tabriz, Iran	N/A	N/A	24-hour mean: SO2 (µg/m^3) =19 NO2 (µg/m^3) =34 1-hour maximum: O3 (µg/m^3) =63	N/A	Attributable proportion (%) and RR increase in COPD per 10 µg/m^3 increase in pollutants	Attributable proportion: SO2 =0.4178 (0–1.038) NO2 =0.8972 (0.952–2.1905) O3 =2.9893 (1.1553–4.7565) RR: SO2 =1.0044 (1–1.011) NO2 =1.0038 (1.0004–1.0094) O3 =1.0058 (1.0022–1.0094)

Abbreviations: 95% CI, 95% confidence interval; PM₁₀, particulate matter with aerodynamic diameter ≤10 µm; SO₂, sulfur dioxide; NO₂, nitrogen dioxide; O₃, ozone; RR, relative risk; BS, black smoke; CO, carbon monoxide; TSP, total suspended particles; NO_x, nitrogen oxides; OR, odds ratio; PM_2.5, particulate matter with aerodynamic diameter ≤2.5 µm; N/A, not available.

Table 4 Outdoor air pollution and respiratory symptoms, lung function, and COPD prevalence and incidence

Authors and year	City/country	Number and age of subjects	Study period	Pollutants and concentration	Risk type and per unit increase	Risk level (95% CI)
Tsonou et al^Citation109 1992	Athens, Greece	110 COPD patients 400 controls 50–60 years	4 months (1984)	Urban living	RR	2.0 (1.2–3.3)
Tashkin et al^Citation110 1994	Los Angeles, USA	621–763 nonsmokers, 317–479 former smokers, 472–691 continuing smokers, 25–29 years	3 years (1986–1989)	Very highly exposed (Glendora) Highly exposed: Long Beach Moderately exposed: Lancaster	OR in large reduction of lung function and actual reduction in FEV1 comparedto Lancaster	OR: Glendora =1.63 (1.63–2.11) Long beach =2.00 (1.53–2.61) FEV1 reduction in males: Long Beach =23.6 mL/year Glendora = 17.2 mL/year FEV1 reduction in females: Long Beach =9.6 mL/year Glendora =8.8 mL/year
Ackermann-Liebrich et al^Citation111 1997	Eight areas, Switzerland	9,651 18–60 years	1 year (1991)	Annual mean: PM30 (µg/m^3) =37 PM10 (µg/m^3) =21 NO2 (µg/m^3) =36 SO2 (µg/m^3) =12 O3 (µg/m^3) =43	Percent decrease in FVC per increase in pollutants, 10 µg/m^3	PM10 =3.4
Avino et al^Citation112 2004	Pietracupa, Rome, Italy	Sannino =132,545 Rome =2,643,581	1 month (2001)	24-hour mean: PM10 (µg/m^3) CO (mg/m^3) NO2 (µg/m^3) O3 (µg/m^3)	Prevalence of COPD admitted to hospitals	Pietracupa (low level) =0.26 Rome (high level) =0.62
Schikowski et al^Citation113 2005	Rhine-Ruhr basin, Germany	4,757 females 54–55 years	9 years (1985–1994)	Annual mean: PM10 (µg/m^3) =44 NO2 (µg/m^3) =39 Home distance to major road: <100 m >100 m	OR for COPD and percent decrease in FVC, FEV1 per increase in PM10 (7 µg/m^3) and <100 m to a major road	COPD prevalence =4.5 PM10: OR =1.33 (1.03–1.72) FEV1 =5.1 (2.5–7.7) FVC =3.7 (1.8–5.5) Females <100 m =1.79 (1.06–3.02)
Sunyer et al^Citation114 2006	21 centers, ten European countries	3,232 males 3,592 females	2 years (2000–2002)	City annual mean: PM2.5 (µg/m^3) =4–45 Sulfur (ng/m^3) =155–2015 Outdoor home: NO2 (µg/m^3) =6–71 Traffic volume	Prevalence (%) and OR in new onset of chronic phlegm (%)	Prevalence: Chronic phlegm =6.9 New onset of chronic phlegm =4.5 OR: Traffic intensity =1.86 (1.24–2.77) Home outdoor NO2 =2.71 (1.03–7.16)
Cesaroni et al^Citation115 2008	Rome, Italy	9,488 25–59 years	5 months (1994–1995)	Self-reported traffic Home distance to major road High traffic roads in 200 m PM emission Estimated NO2 (quartiles, µg/m^3) =34–55	OR in CB and emphysema	CB prevalence 4% No increased risk
Lindgren et al^Citation116 2009	Scania, Sweden	9,319 18–77 years	1 year (2000)	Self-reported heavy traffic Heavy road (cars/min) NOx (µg/m^3) =7–22	OR for COPD prevalence	Self-reported heavy traffic: 1.36 (1.10–1.67) Heavy road: 6–10 cars/min =1.57 (1.15–2.14) >10 cars/min =1.64 (1.11–2.41)NOx (>19 µg/m^3) =1.43 (1.04–1.95)
Bentayeb et al^Citation117 2010	Bordeaux, France	2,104 ≥65 years	3 years (1999–2001)	3-year mean: PM10 (µg/m^3) =19–51 NO2 (µg/m^3) =18–72 SO2 (µg/m^3) =5–14 VOCs (µg/m^3) =0.01–116 Benzene (µg/m^3) =2–7 CO (µg/m^3) =350–1,337	OR and percent increase in bronchitis symptoms per increase in PM10 (10 µg/m^3) and SO2 (1 µg/m^3)	PM10: OR for cough =1.33 (1.00–1.77) % for cough =10 SO2: OR for cough =1.55 (1.16–2.08) % for cough =23
Nuvolone et al^Citation118 2011	Pisa-Cascina, Italy	2,062 Males =45.9 Females =48.9	2 years (1991–1993)	Distance to a major road Highly exposed <100 m Moderately exposed 100–250 m Unexposed 250–800 m	OR on wheeze, COPD diagnosis, and reduced FEV1/FVC for <100 m	Males: Wheeze =1.76 (1.08–2.87) COPD =1.80 (1.03–3.08) FEV1/FVC =2.07 (1.11–3.87) Females: Wheeze =1.67 (0.98–2.84) Dyspnea =1.61 (1.13–2.27)
Andersen et al^Citation119 2011	Aarhus, Copenhagen, Denmark	52,799 people 50–64 years	13 years (1993–2006)	35-year mean: NO2 (µg/m^3) =18 NOx (µg/m^3) =32	HR per interquartile increase in pollutants, 5.8 µg/m^3	HR for NO2: COPD =1.08 (1.02–1.14) COPD with diabetes =1.29 (1.05–1.50) COPD with asthma =1.19 (1.03–1.38)
Salameh et al^Citation120 2012	Beirut, Lebanon	New CB cases =274 Controls =559 ≥40 years	1 year and 2 months	Home distance to a major road, 100 m Living close to a busy road Living close to a busy road >30 years	OR in CB	Home distance to a major road <100 m =2.06 (1.54–2.77) Living close to a busy road =1.61 (1.20–2.15) Living close to a busy road >31 years =2.14 (1.50–3.05)
Rice et al^Citation121 2015	Framingham, USA	6,339	16 years (1995–2011)	Distance to a major road 24-hour mean: PM2.5 (µg/m^3) =11	Percent decrease in FEV1 (%) loss per increase in PM2.5 (2 µg/m^3) and <100 m to a major road	PM2.5: Loss due to PM2.5 exposure: FEV1 =13.5 mL (0.3–26.6) per 2 µg/m^3 FEV1 =2.1 mL (0.2–4.1) per year Loss due to <100 m to a major road: FEV1 =23.2 mL (1.9–44.4) FEV1 =5.0 mL (0.9–9) per year
To et al^Citation122 2015	Ontario, Canada	29,549 45–59 years	33 years (1980–2013)	Long-term average: PM2.5 (µg/m^3) =13	Increase in PR and IR of COPD per increase in PM2.5 (10 µg/m^3)	PR =1.12 (1.01–1.23) IR =1.17 (1.02–1.33)
Adamkiewicz et al^Citation123 2015	Warsaw and control areas, Poland	≥40 years	4 years (2008–2012)	Period of residence close to road traffic stratified into 20 years, 30 years, and 40 years, PM10 (µg/m^3)	RR of lung obstruction per increase in PM10 (10 µg/m^3)	20 years =1.27 30 years =1.24 40 years =1.19

Note: Percent increase = (OR −1) ×100.

Abbreviations: CI, confidence interval; RR, relative risk; OR, odds ratio; FEV₁, forced expiratory volume in the first second, the maximal amount of air forcefully exhaled in 1 second; PM₁₀, particulate matter with aerodynamic diameter ≤10 µm; NO₂, nitrogen dioxide; SO₂, sulfur dioxide; O₃, ozone; FVC, forced vital capacity, the amount of air a person can expire after a maximum inspiration; CO, carbon monoxide; PM_2.5, particulate matter with aerodynamic diameter ≤2.5 µm; CB, chronic bronchitis; NO_x, nitrogen oxides; VOCs, volatile organic compounds; HR, hazard ratio; PR, prevalence rate ratio; IR, incidence rate ratio; PM₃₀, particulate matter with aerodynamic diameter ≤30 µm.

Table 5 Outdoor air pollution on exacerbation of COPD patients

Authors and year	City/country	Number and age of subjects	Study period	Pollutants and concentration	Lag days analyzed	Risk type and per unit increase	Risk level (95% CI)
Lawther et al^Citation124 1970	Various cities, UK	334 patients with bronchitis 27–78 years	Four winters (1954–1959)	24-hour mean: Smoke (μg/m^3) =129–342 SO2 (μg/m^3) =264–296	0 day	Percent increase in worse symptoms over prior day using diary	Overall =28 Baseline =12.5 Peak exposure =37 Decline =7.5
Harré et al^Citation125 1997	Christchurch, New Zealand	40 COPD patients 55 years	3 months Winter 1994	24-hour mean: PM10 (μg/m^3) NO2 (μg/m^3) SO2 (μg/m^3) CO (mg/m^3) 8-hour mean: CO (mg/m^3)	0–1 day	RR increase in symptoms and medication use per interquartile increase in PM10 (35.04 μg/m^3) and NO2 (9.74 μg/m^3)	PM10 and night time chest symptoms =1.38 (1.07–1.78) NO2: Inhaler use =1.42 (1.13–1.79) Nebulizer use (1-day lag) =2.81 (1.81–4.39)
Linn et al^Citation126 1999	Los Angeles, USA	30 COPD patients 16 males 14 females 56–83 years	4 days in fall and winter	24-hour mean and 1-hour maximum: PM10(μg/m^3) =33	0–1 day	Blood pressure increase per unit increase in PM10	Diastolic blood pressure =0.095 mmHg (Lag 0) Diastolic blood pressure =0.122 mmHg (Lag 1) Systolic blood pressure =0.172 mmHg (Lag 1)
Sunyer et al^Citation127 2000	Barcelona, Spain	1,845 males, 460 females COPD patients >35 years	5 years (1990–1995)	24-hour mean: BS (μg/m^3) =44	0–2 days	OR increase in mortality per interquartile increase in BS (20 μg/m^3)	All causes =1.112 (1.017–1.215) Respiratory causes =1.182 (1.025–1.365) Cardiovascular causes =1.077 (0.917–1.264)
Sunyer et al^Citation128 2001	Barcelona, Spain	2,305 COPD patients >35 years	5 years (1990–1995)	24-hour mean: PM10 (μg/m^3	Cumulative 2 days	OR increase in mortality per interquartile increase in PM10 (27 μg/m^3)	All causes =1.11 (1.00–1.24)
Desqueyroux et al^Citation129 2002	Rhine-Ruhr basin in Germany	39 Parisian adults with severe COPD	14 months	Four air pollutants	0–3 days	OR in COPD exacerbation per increase of 10 μg/m^3 for O3	1.44 (1.14–1.82)
Silkoff et al^Citation130 2005	Denver, USA	34 COPD patients ≥40 years	Two winters (1999–2001)	24-hour mean: PM10 (μg/m^3) =25–30 PM2.5 (μg/m^3) =9–14 CO (ppm) =1 NO2 (ppb) =16–29	0–2 days	Percent reduction in lung function and increase in rate ratios of symptom score per SD increase in pollutants	Second winter FEV1 evening: CO Lag 2 =−0.010 (−0.001 to −0.025) Second winter PEF morning: NO2 Lag 0 =−4.8 (−2 to −8) Second winter PEF evening: PM10 Lag 2 =−2.5 (−0.1 to −3.8) NO2 Lag 1 =−4.0 (−1 to −7) Second winter symptom evening: NO2 Lag 0 =1.007 (1.001–1.013)
Trenga et al^Citation131 2006	Seattle, USA	24 COPD 65–89 years 33 control 56–88 years	3 years (1999–2002)	24-hour mean: PM2.5 (μg/m^3): Central =10 Outdoor =9 Personal =8	0–1 day	FEV1 and PEF change per change in outdoor PM2.5 (10 μg/m^3)	FEV1: Lag 0 =−8.9 (−62.2–44.4) Lag 1 =−45.2 (−102.6–12.1) PEF: Lag 0 =−1.8 (−10.6–6.9) Lag 1 =−4.8 (−14.6–4.9)
Lagorio et al^Citation132 2006	Central Rome	29 patients with COPD, asthma, and ischemic heart disease 50–80 years	2×1 months	24-hour mean: PM10 (μg/m^3) =43 PM10–2.5 (μg/m^3) =16 PM2.5 (μg/m^3) =27 NO2 (μg/m^3) =71 SO2 (μg/m^3) =6 O3 (μg/m^3) =43 CO (mg/m^3) =7.4 Zn (ng/m^3) =46 Fe (ng/m^3) =283	0–3 days	Changes in FVC and FEV1 per interquartile increase in pollutants	PM2.5, NO2, Zn, and Fe associated with reduced FEV1 and/or FVC
Peacock et al^Citation133 2011	London, England	94 COPD patients	2 years and 1 month	24-hour mean: PM10 (μg/m^3) =38 BS (μg/m^3) =10 SO2 (ppb) =8 8-hour mean: O3 (ppb) =16 1-hour maximum: NO2 (ppb) =51	0–1 day	Percent change in increased symptoms and reduced lung function per interquartile change in pollutants	Dyspnea and PM10 =13 (4–23) Reduced lung function: NO2 =6 (0–13) PM10 =12 (2–25) BS = 7 (1–13)

Note: Percent increase = (OR −1) ×100.

Abbreviations: CI, confidence interval; SO₂, sulfur dioxide; PM₁₀, particulate matter with aerodynamic diameter ≤10 μm; NO₂, nitrogen dioxide; CO, carbon monoxide; RR, relative risk; BS, black smoke; OR, odds ratio; PM_2.5, particulate matter with aerodynamic diameter ≤2.5 μm; FEV₁, forced expiratory volume in the first second, the maximal amount of air forcefully exhaled in 1 second; PEF, peak expiratory flow; PM10–2.5, particulate matter with aerodynamic diameter between 10 μm and 2.5 μm; O₃, ozone; Zn, zinc; Fe, iron; FVC, forced vital capacity, the amount of air a person can expire after a maximum inspiration; SD, standard deviation.

Table 6 Indoor air pollution and COPD incidence or prevalence in low-income countries

Authors and year	Study design	City/country	Number and age of subjects	Study year	Exposure measured	Health outcome measured	Risk type	Risk level (95% CI)
Pandey et al^Citation143 1984	Population based, cross-sectional Rural	Hill region, Nepal	1,375 rural residents	N/A	Time spent cooking	BMRC questionnaire	Prevalence for bronchitis	Nonsmoking females: Crude prevalence: 12.57% Age-adjusted: 13.76% Prevalence increased with exposure time
Behera and Jindal^Citation144 1991	Population based, cross-sectional Rural	Chandigarh, India Five villages	3,701 females	N/A	Exposure index (hours multiplied by years spent cooking)	BMRC questionnaire Physical examination Lung function measurement	Prevalence for respiratory symptoms	Overall prevalence =13% Mixed fuel =16.7% Biomass (chulla) =12.6% Kerosene stove =11.4% LPG =9.9% Bronchitis higher and lung function lower in chulla users
Menezes et al^Citation145 1994	Population based, cross-sectional Urban	Pelotas, Brazil	1,053 people ≥40 years	N/A	Exposure scoring: No, moderate and high smoke	ATS-DLD-78 questionnaire	Prevalence and OR for chronic bronchitis	Overall prevalence =12.7% OR for higher exposure =1.86 (1.16–2.99)
Dossing et al^Citation146 1994	Case–control	Saudi Arabia	50 COPD cases 71 healthy controls Female	N/A	Exposed to indoor open fire of wood or biomass >20 years	Questionnaire	Percentage of exposed	2/3 in case exposed 1/20 in control exposed
Dennis et al^Citation147 1996	Hospital-based, case–control	Bogota, Columbia	104 COPD cases 104 healthy controls Female ≥35 years	N/A	Exposed to indoor open fire >20 years	ATS questionnaire	OR for wood used for cooking	Wood use =3.43 (1.69–7.05) Gasoline use =0.52 (0.28–0.95)
Pérez-Padilla et al^Citation148 1996	Urban hospital-based, case–control	Mexico City, Mexico	126 cases (63 CB, 23 COPD, and 41 both) Four control groups (83 with TB, 100 with interstitial lung diseases, 97 with ear, nose, and throat ailments 95 healthy visitors Females ≥35 years	N/A	Exposed to wood smoke and duration (hour-years)	ATS questionnaire	OR for wood use for cooking	CB =3.9 (2.0–7.6) COPD + CB =9.7 (3.7–27) COPD =1.8 (0.7–4.7) Duration >200 hour-years: COPD + CB =75 (18–306) COPD =15.0 (5.6–40)
Ellegård^Citation149 1996	Population based, cross-sectional Suburban	Maputo, Mozambique	1,200 females >14 years	1992	Fuel use type Cumulative exposure PM10 (μg/m^3)	Zambia questionnaire Peak expiratory flow (L/minute)	Frequency of symptoms	Wood use for fuel associated with more cough problems
Albalak et al^Citation150 1999	Population based, cross-sectional Two highland villages (one indoor cooking and one outdoor cooking)	Aymara, Bolivia	241 villagers ≥20 years 102= indoor cooking 139= outdoor cooking	1995	Daily exposure index (PM10 μg/m^3 multiplied by exposure time)	BMRC questionnaire	Prevalence and OR for CB	Prevalence for villages: Indoor cooking =22% Outdoor cooking =13% Unadjusted OR for outdoor cooking =0.5 (0.2–0.9) Adjusted OR for outdoor cooking =0.4 (0.2–0.8) OR for CB in >40 age group =4.3 (2.0–9.3)
Golshan et al^Citation151 2002	Population based, cross-sectional Three suburban villages	Isfahan, Iran	561 females 1 month–81 years	2000	TSP (ppb) PM10 (ppb)	Questionnaire Physical examination	OR for CB	Using kerosene fuel =1.27 (1.02–1.66) Using wood fuel =2.91 (2.08–4.4)
Kiraz et al^Citation152 2003	Population based, cross-sectional Three villages, one city district	Kayseri, Turkey	242 rural 102 city Female ≥25 years	1999	Questionnaire for fuel type, years of use, and stove type	ATS and BMRC questionnaires Physical examination Lung function testing	OR for CB and COPD	Rural vs urban females: CB (%)=20.7 vs 10.8 COPD (%) =12.4 vs 3.9 OR for CB =28.7 (8.7–95.9)
Ekici et al^Citation153 2005	Population based, cross-sectional Ten villages	Kirikkale, Turkey	Rural biomass users =397 Urban LPG control =199 Female ≥40 years	2002	Exposure index (hours multiplied by years of biomass cooking): A ≤68.6 hour-year B =68.8–152.4 hour-year C ≥152.4 hour-year	BMRC questionnaire Physical examination Lung function testing	OR and AP for COPD	Prevalence: Biomass =28.5% LPG =13.6% Crude OR: Group A =1.7 (1.0–3.1) Group B =2.5 (1.4–4.4) Group C =3.3 (1.9–5.7) Adjusted OR: Group A =2.0 (0.9–4.5) Group B =2.3 (1.1–4.5) Group C =2.2 (1.1–4.4) Combined =2.5 (1.5–4.0) Adjusted =1.4 (1.2–1.7) AP =23.1 (13.4–33.2)
Peabody et al^Citation154 2005	Population based, cross-sectional Three provinces 103.4 million people 3,476 households	Shanxi, Hubei, Zhejiang, People’s Republic of China	4,638 adults 2,285 children	N/A	Fuel type Stove type Duration in cooking CO in exhaled air	Adult and children questionnaires Heart rate Respiratory rate Blood pressure Lung function testing (FVC)	Prevalence and OR for COPD	COPD prevalence (%) =3.8 Prevalence higher in coal users OR compared to coal: Wood =0.48 (0.28–0.87) Crop =0.57 (0.34–0.96) Clean fuel =0.43 (0.05–0.36) OR for stove: Tradition vs improved =1.87 (1.30–2.69)
Chapman et al^Citation155 2005	Retrospective cohort Large rural community	Xuanwei, People’s Republic of China	20,453 people with traditional stoves 16,606 people changed to improved stoves	16 years (1976–1992)	Questionnaire for stove type and fuel type	Standard questionnaire Bronchitis Emphysema Death	RR for COPD in improved vs unvented stoves	COPD prevalence (%): 7.3 COPD incidence (%) reduced: Males =42 Females =25 RR: Male =0.58 (0.49–0.70) Females =0.75 (0.62–0.92)
Sezer et al^Citation156 2006	Hospital-based case–control	Sivas, Turkey	74 cases with COPD 74 controls without COPD All housewives	1 year and 3 months (2001–2002)	Questionnaire for fuel type, years of use	COPD determined by hospital record	OR for exposure	≥30 years of biomass exposure =6.61 (2.17–20.18)
Akhtar et al^Citation157 2007	Population-based, cross-sectional Six villages	Peshawar, Pakistan	1,426 females in three study villages 1,131 females in three control villages	10 months (2003–2004)	Questionnaire for fuel type	ATS questionnaire	OR for CB	CB prevalence (%): Study village =7.01 Control village =2.92 OR: Overall =2.51 (1.65–3.83) Rice straws =3.32 (1.11–9.88) Wood =2.38 (2.12–3.01) Dung =2.01 (1.72–2.42) Kai grass =1.96 (1.75–2.45)
Liu et al^Citation158 2007	Population-based, cross-sectional One rural community with 14,000 people One urban community with 520,000 people	Guangzhou, People’s Republic of China	Rural =1,468 Urban =1,818 ≥40 years	7 months (2003–2004)	Geometric mean of biomass cooking in kitchen: PM10 (mg/m^3) =0.5 SO2 (mg/m^3) =0.25 NO2 (mg/m^3) =0.09 CO (mg/m^3) =4.5	Standard questionnaire Physical examination Lung function testing	OR for COPD and respiratory symptoms	COPD prevalence (%):Rural =12 Urban =7.4 OR for fuel type: Biomass =1.72 (1.27–2.35) Coal =1.55 (0.74–3.22) COPD prevalence (%) for nonsmoking females: Rural =7.2 Urban =2.5 OR for fuel type: Biomass =3.11 (1.63–5.94) Coal =2.77 (0.83–9.26) OR for SO2 =1.80 (1.04–3.11)
Zhong et al^Citation159 2007	Population-based, cross-sectional In both urban and rural communities	Seven provinces/cities, People’s Republic of China	25,627 people ≥40 years	0.5 years (2002–2003)	Indoor biomass cooking Kitchen ventilation	BOLD questionnaire Physical examination Lung function testing Chest radiograph	Prevalence and OR for COPD and respiratory symptoms	COPD prevalence (%):Males =12.4 Females =5.1 Combined =8.2 OR: Poor kitchen ventilation =1.28 (1.14–1.43) Indoor biomass cooking and heating =1.35 (1.20–1.52)
Desalu et al^Citation160 2010	Population-based, cross-sectional In rural community	Ekiti State, Nigeria	269 adult females	6 months (2009)	Biomass vs nonbiomass fuel use	ECRHS questionnaire	OR and prevalence for CB	CB prevalence (%):Biomass =10.6 Nonbiomass =2.8 CB OR=3.75 (1.07–13.16) Lower lung function
Johnson et al^Citation161 2011	Population-based, cross-sectional In 45 villages	Tamilnadu, India	900 females ≥30 years	5 months (2007)	Fuel type Years of cooking Daily cooking time	Standard questionnaire Lung function testing	Prevalence and OR of COPD (%)	Prevalence: Overall =2.44 (1.43–3.45) Biomass =2.5 Clean fuel =2 Cooking >2 hours/day =3 OR: Biomass fuel =1.24 (0.36–4.25) Cooking >15 years =2.36 (0.69–8.03)
da Silva et al^Citation162 2012	Population-based, cross-sectional In both urban and rural communities	Joao Camara, Brazil	260 houses 1,402 people ≥20 years <20 years	N/A	PM2.5 direct measurement in 48 houses PM2.5 estimation in the rest Exposure index (PM2.5 multiplied by years)	ISAAC and BMRC questionnaires Lung function testing	OR for respiratory symptoms	Airway obstruction in biomass group =20% OR in indoor biomass group: Cough =2.93 (1.68–5.10) Dyspnea =2.59 (1.32–5.09) Wheezing =2.33 (1.25–4.38) FEV1/FVC in biomass group =0.79 (0.76–0.82)
Mahesh et al^Citation163 2013	Population-based, cross-sectional In rural communities	Mysore and Nanjangud, India	16 villages 3,953 nonsmoking females >30 years	3 years (2006–2009)	Daily hours of cooking EI (hours multiplied by years)	BOLD questionnaire	Prevalence and OR for CB	CB prevalence (%):Mysore =1.79 Nanjangud =4.99 Adjusted OR: EI >60 =1.74 (1.07–2.85) EI >70 =2.15 (1.35–3.41) EI >80 =2.49 (1.64–3.78) EI >90 =2.97 (1.95–4.54) EI >100 =3.27 (2.13–5.02) EI >110 =2.65 (1.73–4.06)

Notes: Case–control and cross-sectional prevalence studies used chi-square test, Mantel–Haenszel method, and logistic regression or multivariate regression for data analysis on OR and trends, adjusting for variables such as age, sex, marital status, education, body mass index, alcohol use, active and passive smoking, occupational exposures, atopy and family history of COPD, place of birth, and residence and family income. Pollutant names are the same as in previous tables.

Abbreviations: CI, confidence interval; BMRC, British Medical Research Council; LPG, liquefied petroleum gas; ATS, American Thoracic Society; OR, odds ratio; CB, chronic bronchitis; PM₁₀, particulate matter with aerodynamic diameter ≤10 μm; TSP, total suspended particles; AP, attributable proportion = (OR −1) × P/OR, where P is the prevalence of the exposure among the cases; CO, monoxide; FVC, forced vital capacity, the amount of air a person can expire after a maximum inspiration; RR, relative risk; SO₂, sulfur dioxide; NO₂, nitrogen dioxide, BOLD, Burden of Obstructive Lung Disease; ECRHS, European Community Respiratory Health Survey; PM_2.5, particulate matter with aerodynamic diameter ≤2.5 μm; ISAAC, International Study of Asthma and Allergies in Childhood; FEV₁, forced expiratory volume in the first second, the maximal amount of air forcefully exhaled in 1 second; EI, exposure index or cumulative exposure = daily cooking hours multiplied by years of cooking; N/A, not available; TB, tuberculosis.

Figure 1 Article identification, screening, evaluation on eligibility and inclusion.

Figure 2 Outdoor air pollution and COPD-related mortality in both high- and low- to middle-income countries: increased risk for COPD per increase in particle exposure (10 µg/m³).

Abbreviations: PM₁₀, particulate matter with aerodynamic diameter ≤10 µm; PM_2.5, particulate matter with aerodynamic diameter ≤2.5 µm; PM_2.5–10, particulate matter with aerodynamic diameter between 2.5 µm and 10 µm; CI, confidence interval.

Figure 3 Outdoor air pollution and COPD-related hospital admissions or emergency room visits: increased risk for COPD per increase in particle exposure (10 µg/m³).

Note: (A) High-income countries and (B) low- to middle-income countries.
Abbreviations: PM₁₀, particulate matter with aerodynamic diameter ≤10 µm; TSP, total suspended particles; PM_2.5, particulate matter with aerodynamic diameter ≤2.5 µm; CI, confidence interval; BS, black smoke.

SchwartzJDockeryDWIncreased mortality in Philadelphia associated with daily air pollution concentrationsAm Rev Respir Dis199214536006041546841

 PubMed Web of Science ®Google Scholar

XuXGaoJDockeryDWChenYAir pollution and daily mortality in residential areas of Beijing, ChinaArch Environ Health19944942162228031176

 PubMed Web of Science ®Google Scholar

RossiGVigottiMAZanobettiARepettoFGianelleVSchwartzAir pollution and cause-specific mortality in Milan, Italy, 1980–1989Arch Environ Health199954315816410444036

 PubMed Web of Science ®Google Scholar

XuZYuDJingLXuXAir pollution and daily mortality in Shenyang, ChinaArch Environ Health200055211512010821512

 PubMed Web of Science ®Google Scholar

Tellez-RojoMMRomieuIRuiz-VelascoSLezanaMAHernandez-AvilaMMDaily respiratory mortality and PM10 pollution in Mexico City: importance of considering place of deathEur Respir J20001639139611028649

 PubMed Web of Science ®Google Scholar

WongTWTamWSYuTSWongAHAssociations between daily mortalities from respiratory and cardiovascular diseases and air pollution in Hong Kong, ChinaOccup Environ Med2002591303511836466

 PubMed Web of Science ®Google Scholar

KanHChenBAir pollution and daily mortality in Shanghai: a time-series studyArch Environ Health20035836036714992311

 PubMedGoogle Scholar

ZekaAZanobettiASchwartzJShort term effects of particulate matter on cause specific mortality: effects of lags and modification by city characteristicsOccup Environ Med20056271872516169918

 PubMed Web of Science ®Google Scholar

NaessØNafstadPAamodtGClaussenBRoslandPRelation between concentration of air pollution and cause-specific mortality: four-year exposures to nitrogen dioxide and particulate matter pollutants in 470 neighborhoods in Oslo, NorwayAm J Epidemiol2007165443544317135427

 PubMed Web of Science ®Google Scholar

MengXWangCCaoDWongCKanHShort-term effect of ambient air pollution on COPD mortality in four Chinese citiesAtmos Environ201377149154

 Web of Science ®Google Scholar

SamoliEStafoggiaMRodopoulouSMED-PARTICLES Study GroupWhich specific causes of death are associated with short term exposure to fine and coarse particles in southern Europe? Results from the MED-PARTICLES projectEnvironment Int2014675461

 PubMed Web of Science ®Google Scholar

SunyerJAntóJMMurilloCSaezMEffects of urban air pollution on emergency room admissions for chronic obstructive pulmonary diseaseAm J Epidemiol19911343277286 discussion 287–2891877586

 PubMed Web of Science ®Google Scholar

SunyerJSáezMMurilloCCastellsagueJMartínezFAntóJMAir pollution and emergency room admissions for chronic obstructive pulmonary disease: a 5-year studyAm J Epidemiol199313777017058484361

 PubMed Web of Science ®Google Scholar

PönkäAVirtanenMChronic bronchitis, emphysema, and low-level air pollution in Helsinki, 1987–1989Environ Res19946522072178187737

 PubMed Web of Science ®Google Scholar

SchwartzJPM10, ozone, and hospital admissions for the elderly in Minneapolis-St. Paul, MinnesotaArch Environ Health1994493663747944569

 PubMed Web of Science ®Google Scholar

SchwartzJAir pollution and hospital admissions for the elderly in Detroit, MichiganAm J Respir Crit Care Med19941506486558087333

 PubMed Web of Science ®Google Scholar

SchwartzJAir pollution and hospital admissions for the elderly in Birmingham, AlabamaAm J Epidemiol199413965895988172170

 PubMed Web of Science ®Google Scholar

BurnettRTDalesRERaizenneMEEffects of low ambient levels of ozone and sulfates on the frequency of respiratory admissions to Ontario hospitalsEnviron Res19946521721948187735

 PubMed Web of Science ®Google Scholar

SchoutenJPVonkJMde GraafAShort term effects of air pollution on emergency hospital admissions for respiratory disease: results of the APHEA project in two major cities in the Netherlands, 1977–89J Epidemiol Community Health199650suppl 1S22S298758220

 PubMed Web of Science ®Google Scholar

AndersonHRSpixCMedinaSAir pollution and daily admissions for chronic obstructive pulmonary disease in 6 European cities: results from the APHEA projectEur Respir J199710106410719163648

 PubMed Web of Science ®Google Scholar

MorganGCorbettSWlodarczykJAir pollution and hospital admissions in Sydney, Australia, 1990 to 1994Am J Public Health199888176117669842371

 PubMed Web of Science ®Google Scholar

ChenLYangWJennisonBLOmayeSTAir particulate pollution and hospital admissions for chronic obstructive pulmonary disease in Reno, NevadaInhal Toxicol20001228129810715629

 PubMed Web of Science ®Google Scholar

TolbertPEKleinMMetzgerKBInterim results of the study of particulates and health in Atlanta (SOPHIA)J Expo Anal Environ Epidemiol200010544646011051535

 PubMedGoogle Scholar

FuscoDForastiereFMichelozziPAir pollution and hospital admissions for respiratory conditions in Rome, ItalyEur Respir J2001171143115011491157

 PubMed Web of Science ®Google Scholar

TeníasJMBallesterFPérez-HoyosSRiveraMLAir pollution and hospital emergency room admissions for chronic obstructive pulmonary disease in Valencia, SpainArch Environ Health2002571414712071359

 PubMed Web of Science ®Google Scholar

ChenYYangQKrewskiDShiYBurnettRTMcGrailKInfluence of relatively low level of particulate air pollution on hospitalization for COPD in elderly peopleInhal Toxicol2004161212514744661

 PubMed Web of Science ®Google Scholar

PeelJLTolbertPEKleinMAmbient air pollution and respiratory emergency department visitsEpidemiology200516216417415703530

 PubMed Web of Science ®Google Scholar

YangQChenYKrewskiDBurnettRTShiYMcGrailKMEffect of short-term exposure to low levels of gaseous pollutants on chronic obstructive pulmonary disease hospitalizationsEnviron Res2005999910516053934

 PubMed Web of Science ®Google Scholar

HinwoodALDe KlerkNRodriguezCThe relationship between changes in daily air pollution and hospitalizations in Perth, Australia 1992–1998: a case-crossover studyInt J Environ Health Res2006161274616507479

 PubMed Web of Science ®Google Scholar

DominiciFPengRDBellMLFine particulate air pollution and hospital admission for cardiovascular and respiratory diseasesJAMA2006295101127113416522832

 PubMed Web of Science ®Google Scholar

Medina-RamónMZanobettiASchwartzJThe effect of ozone and PM10 on hospital admissions for pneumonia and chronic obstructive pulmonary disease: a national multicity studyAm J Epidemiol2006163657958816443803

 PubMed Web of Science ®Google Scholar

SauerzapfVJonesAPCrossJEnvironmental factors and hospitalisation for chronic obstructive pulmonary disease in a rural county of EnglandJ Epidemiol Community Health200963432432819208692

 PubMed Web of Science ®Google Scholar

BelleudiVFaustiniAStafoggiaMImpact of fine and ultrafine particles on emergency hospital admissions for cardiac and respiratory diseasesEpidemiology201021341442320386174

 PubMed Web of Science ®Google Scholar

CireraLGarcía-MarcosLGiménezJDaily effects of air pollutants and pollen types on asthma and COPD hospital emergency visits in the industrial and Mediterranean Spanish city of CartagenaAllergol Immunopathol (Madr)201240423123721890258

 PubMed Web of Science ®Google Scholar

LiuXLessnerLCarpenterDOAssociation between residential proximity to fuel-fired power plants and hospitalization rate for respiratory diseasesEnviron Health Perspect2012120680781022370087

 PubMed Web of Science ®Google Scholar

FaustiniAStafoggiaMColaisPEpiAir Collaborative GroupAir pollution and multiple acute respiratory outcomesEur Respir J201342230431323314899

 PubMed Web of Science ®Google Scholar

KloogINordioFZanobettiACoullBAKoutrakisPSchwartzJDShort term effects of particle exposure on hospital admissions in the Mid-Atlantic States: a population estimatePLoS One201492e8857824516670

 PubMed Web of Science ®Google Scholar

YorifujiTSuzukiEKashimaSHourly differences in air pollution and risk of respiratory disease in the elderly: a time-stratified case-crossover studyEnviron Health2014136725115710

 PubMed Web of Science ®Google Scholar

WongTWLauTSYuTSAir pollution and hospital admissions for respiratory and cardiovascular diseases in Hong KongOccup Environ Med1999561067968310658547

 PubMed Web of Science ®Google Scholar

BurrilloJMDíezFBPérez-HoyosSUse of different hospital data bases in the estimation of the relation between air pollution and chronic obstructive pulmonary diseaseEpidemiology200112228011246595

 PubMed Web of Science ®Google Scholar

PandeJNBhattaNBiswasDOutdoor air pollution and emergency room visits at a hospital in DelhiIndian J Chest Dis Allied Sci2002441131911845928

 PubMedGoogle Scholar

GouveiaNde FreitasCUMartinsLCMarcilioIOHospitalizações por causas respiratórias e cardiovasculares associadas à contaminação atmosférica no Município de São Paulo, Brasil [Respiratory and cardiovascular hospitalizations associated with air pollution in the city of Sao Paulo, Brazil]Cad Saúde Pública20062226692677 Portuguese17096045

 PubMedGoogle Scholar

YangCYChenCJAir pollution and hospital admissions for chronic obstructive pulmonary disease in a subtropical city: Taipei, TaiwanJ Toxicol Environ Health A200770141214121917573635

 PubMed Web of Science ®Google Scholar

LeeIMTsaiSSChangCCHoCKYangCYAir pollution and hospital admissions for chronic obstructive pulmonary disease in a tropical city: Kaohsiung, TaiwanInhal Toxicol200719539339817365044

 PubMed Web of Science ®Google Scholar

Lung.org [webpage on the Internet]Trends in COPD (Chronic Bronchitis and Emphysema): Morbidity and MortalityChicagoAmerican Lung Association Available from: http://www.lung.org/assets/documents/research/copd-trend-report.pdfAccessed October 26, 2015

 Google Scholar

KoFWTamWWongTWTemporal relationship between air pollutants and hospital admissions for chronic obstructive pulmonary disease in Hong KongThorax200762978078517311838

 PubMed Web of Science ®Google Scholar

ArbexMAde Souza ConceiçãoGMCendonSPUrban air pollution and chronic obstructive pulmonary disease-related emergency department visitsJ Epidemiol Community Health2009631077778319468016

 PubMed Web of Science ®Google Scholar

MilutinovićSNikićDStosićLStankovićABogdanovićDShort-term association between air pollution and emergency room admissions for chronic obstructive pulmonarydisease in Nis, SerbiaCent Eur J Public Health200917181319418713

 PubMedGoogle Scholar

QiuHYuITSWangXTianLTseLAWongTWSeason and humidity dependence of the effects of air pollution on COPD hospitalizations in Hong KongAtmos Environ2013767480

 Web of Science ®Google Scholar

TsaiSSChangCCYangCYFine particulate air pollution and hospital admissions for chronic obstructive pulmonary disease: a case-crossover study in TaipeiInt J Environ Res Public Health201310116015602624284359

 PubMed Web of Science ®Google Scholar

GhozikaliMGMosaferiMSafariGHJaafariJEffect of exposure to O3, NO2, and SO2 on chronic obstructive pulmonary disease hospitalizations in Tabriz, IranEnviron Sci Pollut Res Int20152242817282325217280

 PubMed Web of Science ®Google Scholar

TzonouAMaragoudakisGTrichopoulosDUrban living, tobacco smoking, and chronic obstructive pulmonary disease: a study in AthensEpidemiology19923157601554811

 PubMed Web of Science ®Google Scholar

TashkinDPDetelsRSimmonsMThe UCLA population studies of chronic obstructive respiratory disease: XI. Impact of air pollution and smoking on annual change in forced expiratory volume in one secondAm J Respir Crit Care Med1994149120912178173761

 PubMed Web of Science ®Google Scholar

Ackermann-LiebrichULeuenbergerPSchwartzJLung function and long term exposure to air pollutants in Switzerland. Study on air pollution and lung diseases in adults (SAPALDIA) TeamAm J Respir Crit Care Med19971551221299001300

 PubMed Web of Science ®Google Scholar

AvinoPDe LisioVGrassiMInfluence of air pollution on chronic obstructive respiratory diseases: comparison between city (Rome) and hillcountry environments and climatesAnn Chim2004949–1062963515506613

 PubMed Web of Science ®Google Scholar

SchikowskiTSugiriDRanftULong-term air pollution exposure and living close to busy roads are associated with COPD in womenRespir Res2005615216372913

 PubMed Web of Science ®Google Scholar

SunyerJJarvisDGotschiTChronic bronchitis and urban air pollution in an international studyOccup Environ Med2006631283684316847030

 PubMed Web of Science ®Google Scholar

CesaroniGBadaloniCPortaDForastiereFPerucciCAComparison between various indices of exposure to traffic-related air pollution and their impact on respiratory health in adultsOccup Environ Med2008651068369018203803

 PubMed Web of Science ®Google Scholar

LindgrenAStrohEMontnémeryPNihlénUJakobssonKAxmonATraffic-related air pollution associated with prevalence of asthma and COPD/chronic bronchitis. A cross-sectional study in Southern SwedenInt J Health Geogr20098219154599

 PubMed Web of Science ®Google Scholar

BentayebMHelmerCRaherisonCTessierJFAnnesi-MaesanoIBronchitis-like symptoms and proximity air pollution in French elderlyRespir Med2010104688088820129767

 PubMed Web of Science ®Google Scholar

NuvoloneDDella MaggioreRMaioSGeographical information system and environmental epidemiology: a cross-sectional spatial analysis of the effects of traffic-related air pollution on population respiratory healthEnviron Health2011101221362158

 PubMed Web of Science ®Google Scholar

AndersenZJHvidbergMJensenSSChronic obstructive pulmonary disease and long-term exposure to traffic-related air pollution: a cohort studyAm J Respir Crit Care Med2011183445546120870755

 PubMed Web of Science ®Google Scholar

SalamehPSalameJKhayatGExposure to outdoor air pollution and chronic bronchitis in adults: a case-control studyInt J Occup Environ Med20123416517723022867

 PubMedGoogle Scholar

RiceMBLjungmanPLWilkerEHLong-term exposure to traffic emissions and fine particulate matter and lung function decline in the Framingham heart studyAm J Respir Crit Care Med2015191665666425590631

 PubMed Web of Science ®Google Scholar

ToTZhuJVilleneuvePJChronic disease prevalence in women and air pollution – a 30-year longitudinal cohort studyEnviron Int201580263225863281

 PubMed Web of Science ®Google Scholar

AdamkiewiczŁGayerAMuchaDBadydaAJDąbrowieckiPGrabskiPRelative risk of lung obstruction in relation to PM10 concentration as assessed by pulmonary function testsAdv Exp Med Biol2015849839125523626

 PubMed Web of Science ®Google Scholar

LawtherPJWallerREHendersonMAir pollution and exacerbations of bronchitisThorax197055255395489175

 PubMed Web of Science ®Google Scholar

HarréESPricePDAyreyRBToopLJMartinIRTownGIRespiratory effects of air pollution in chronic obstructive pulmonary disease: a three month prospective studyThorax19975212104010449516896

 PubMed Web of Science ®Google Scholar

LinnWSGongHJrClarkKWAndersonKRDay-to-day particulate exposures and health changes in Los Angeles area residents with severe lung diseaseJ Air Waste Manag Assoc1999499 Spec10811511002833

 PubMedGoogle Scholar

SunyerJSchwartzJTobiasAMacfarlaneDGarciaJAntoJMPatients with chronic obstructive pulmonary disease are at increased risk of death associated with urban particle air pollution: a case-crossover analysisAm J Epidemiol20001511505610625173

 PubMed Web of Science ®Google Scholar

SunyerJBasagañaXParticles, and not gases, are associated with the risk of death in patients with chronic obstructive pulmonary diseaseInt J Epidemiol20013051138114011689536

 PubMed Web of Science ®Google Scholar

DesqueyrouxHPujetJCProsperMLe MoullecYMomasIEffects of air pollution on adults with chronic obstructive pulmonary diseaseArch Environ Health200257655456012696653

 PubMed Web of Science ®Google Scholar

SilkoffPEZhangLDuttonSWinter air pollution and disease parameters in advanced chronic obstructive pulmonary disease panels residing in Denver, ColoradoJ Allergy Clin Immunol2005115233734415696092

 PubMed Web of Science ®Google Scholar

TrengaCASullivanJHSchildcroutJSEffect of particulate air pollution on lung function in adult and pediatric subjects in a Seattle panel studyChest200612961614162216778283

 PubMed Web of Science ®Google Scholar

LagorioSForastiereFPistelliRAir pollution and lung function among susceptible adult subjects: a panel studyEnviron Health200651116674831

 PubMedGoogle Scholar

PeacockJLAndersonHRBremnerSAOutdoor air pollution and respiratory health in patients with COPDThorax201166759159621459856

 PubMed Web of Science ®Google Scholar

PandeyMRDomestic smoke pollution and chronic bronchitis in a rural community of the Hill Region of NepalThorax19843953373396740536

 PubMed Web of Science ®Google Scholar

BeheraDJindalSKRespiratory symptoms in Indian women using domestic cooking fuelsChest199110023853881864111

 PubMed Web of Science ®Google Scholar

MenezesAMVictoraCGRigattoMPrevalence and risk factors for chronic bronchitis in Pelotas, RS, Brazil: a population-based studyThorax19944912121712217878555

 PubMed Web of Science ®Google Scholar

DossingMKhanJal-RabiahFRisk factors for chronic obstructive lung disease in Saudi ArabiaRespir Med1994885195227972976

 PubMed Web of Science ®Google Scholar

DennisRJMaldonadoDNormanSBaenaEMartinezGWoodsmoke exposure and risk for obstructive airways disease among womenChest19961091151198549171

 PubMed Web of Science ®Google Scholar

Perez-PadillaRRegaladoJVedalSExposure to biomass smoke and chronic airway disease in Mexican women. A case-control studyAm J Respir Crit Care Med19961547017068810608

 PubMed Web of Science ®Google Scholar

EllegårdACooking fuel smoke and respiratory symptoms among women in low-income areas in MaputoEnviron Health Perspect199610499809858899378

 PubMed Web of Science ®Google Scholar

AlbalakRFrisanchoARKeelerGJDomestic biomass fuel combustion and chronic bronchitis in two rural Bolivian villagesThorax199954111004100810525559

 PubMed Web of Science ®Google Scholar

GolshanMFaghihiMMarandiMMIndoor women jobs and pulmonary risks in rural areas of Isfahan, Iran, 2000Respir Med200296638238812117036

 PubMed Web of Science ®Google Scholar

KirazKKartLDemirRChronic pulmonary disease in rural women exposed to biomass fumesClin Invest Med20032624324814596485

 PubMed Web of Science ®Google Scholar

EkiciAEkiciMKurtipekEObstructive airway diseases in women exposed to biomass smokeEnviron Res200599939816053933

 PubMed Web of Science ®Google Scholar

PeabodyJWRiddellTJSmithKRIndoor air pollution in rural China: cooking fuels, stoves, and health statusArch Environ Occup Health200560869516983861

 PubMed Web of Science ®Google Scholar

ChapmanRSHeXBlairAELanQImprovement in household stoves and risk of chronic obstructive pulmonary disease in Xuanwei, China: retrospective cohort studyBMJ20053317524105016234255

 PubMed Web of Science ®Google Scholar

SezerHAkkurtIGulerNBerkSA case-control study on the effect of exposure to different substances on the development of COPDAnn Epidemiol2006161596215990336

 PubMed Web of Science ®Google Scholar

AkhtarTUllahZKhanMHNazliRChronic bronchitis in women using solid biomass fuel in rural Peshawar, PakistanChest200713251472147517646238

 PubMed Web of Science ®Google Scholar

LiuSZhouYWangXBiomass fuels are the probable risk factor for chronic obstructive pulmonary disease in rural South ChinaThorax20076288989717483137

 PubMed Web of Science ®Google Scholar

ZhongNWangCYaoWPrevalence of chronic obstructive pulmonary disease in China: a large, population-based surveyAm J Respir Crit Care Med2007176875376017575095

 PubMed Web of Science ®Google Scholar

ErratumAm J Respir Crit Care Med200717611116918042661

 PubMed Web of Science ®Google Scholar

DesaluOOAdekoyaAOAmpitanBAIncreased risk of respiratory symptoms and chronic bronchitis in women using biomass fuels in NigeriaJ Bras Pneumol201036444144620835590

 PubMed Web of Science ®Google Scholar

JohnsonPBalakrishnanKRamaswamyPPrevalence of chronic obstructive pulmonary disease in rural women of Tamilnadu: implications for refining disease burden assessments attributable to household biomass combustionGlob Health Action20114722622065945

 PubMed Web of Science ®Google Scholar

da SilvaLFSaldivaSRSaldivaPHBandeira Científica ProjectImpaired lung function in individuals chronically exposed to biomass combustionEnviron Res201211211111722136759

 PubMed Web of Science ®Google Scholar

MaheshPAJayarajBSPrabhakarAKChayaSKVijaysimhaRIdentification of a threshold for biomass exposure index for chronic bronchitis in rural women of Mysore district, Karnataka, IndiaIndian J Med Res20131371879423481056

 PubMed Web of Science ®Google Scholar