TABLE 1.

Summary of included studies

Author	Data period and location	Patient population	Sample size	Male %	Environmental pollution/exposure	Confounders	Conclusions
		Diagnostic criteria		Age ± SD		MINORS score
Padhye et al. 34	Jan 2015–July 2016 Chicago, Illinois	CRS Nasal swab taken from middle nasal meatus under endoscopic guidance 2–3 weeks before tissue collection during surgery	CRS 111 (71 underwent surgery) Controls 21	NR	PM2.5 Exposure based on patient's home address. PM2.5 levels from US EPA 2011 Environmental Justice Screen dataset	insurance, race, socioeconomic status, asthma, atopy, age, CRS duration	CRS was associated with higher neighborhood PM2.5 levels than controls (p <.001) PM2.5 was associated with decreased relative abundance of Corynebacterium in CRS (p = .02) and controls (p = .04) PM2.5 levels were associated with eosinophilic aggregation in CRS patients (p <.01). Not associated with other evaluated markers
		Patients diagnosed in tertiary rhinology clinic		NR		18
Patel et al. 35	Jan 2015–May 2019 Chicago, Illinois	Patients with CRS who underwent FESS Age ≥ 18	CRS 291 (CRSsNP 152 CRSwNP 131)	46%	O3, PM2.5 Exposure based on patient's home address Pollutant levels from US EPA 2018 Environmental Justice Screen Dataset	Race, smoking history, inhalant allergy	CRS: In multivariate models, increasing ozone exposure was associated with more severe inflammation (p = .031) and Charcot–Leyden crystals (p = .039). No trends were seen based on PM2.5 exposure Subgroups: There was no difference in exposure to O3 (p = .306) or PM2.5 (p = .815) based in CRSsNP versus CRSwNP subgroups In CRSwNP, increased O3 exposure was associated with increased inflammation (p = .004), presence of eosinophilic aggregates (p = .018), and presence of Charcot–Leyden crystals (p = .036). No associations were noted between PM2.5 exposure and CRSwNP No associations were noted with CRSsNP and O3 or PM2.5 exposure Not associated with other evaluated markers
		Diagnosed based on 12 weeks of continuous symptoms supported by positive endoscopy and CT scan, and requirement of ESS after failure of appropriate medical therapy		49.3 ± 16.1		10
Zhang et al. 36	NR Northeast United States	CRS Age ≥ 18	CRS 2034 Controls 4068	CRS: 41.3% Control: 43.5%	PM2.5 and O3 Exposure based on patient's home address at 12, 24, 36, and 60 months before diagnosis date Machine learning approaches were used to predict daily PM2.5 concentrations. Daily O3 exposure estimated from the National Air Monitoring Stations/State and Local Air Monitoring Stations	Age, gender, race, BMI, alcohol consumption status, smoking status, hypertension, diabetes, COPD, asthma	At all time‐periods measured before diagnosis, PM2.5 exposure was associated with higher odds of CRS diagnosis. This was also noted across all sinusitis locations, most notably in cases of ethmoidal and severe (e.g., four sinus) sinusitis cases
		CRS ICD9/10 code by board‐certified otolaryngologist using nasal endoscopy and CT scans Excluded those with environmental allergies		CRS: 51.5 ± 16.0 Controls: 51.9 ± 17.4		18
Lu et al. 37	Jan 1, 2015–Dec 31, 2018 Xinxiang, China	Chronic sinusitis	183,943 cases	49.6%	NO2, SO2, CO, PM10, PM2.5, and O3 Data from China Environmental Monitoring Centre website, gathered from four fixed site monitoring stations	Co‐pollutants	All pollutants except for O3 had some impact on hospital outpatient cases of chronic sinusitis. Notably, these trends were seen in patients age < 65 (most significantly in pediatric population), but not significant in a subgroup of adults age ≥ 65 After adjusting for co‐pollutants, the relationship between PM2.5 and NO2 and chronic sinusitis cases remained
		Chronic sinusitis as coded with the ICD‐10 J32 code		70.1% between 15–65 years old		12
Velasquez et al. 38	2013–2015 Pittsburgh, Pennsylvania	CRSsNP CRSwNP ≥18 years old Same residence and occupation for past 5 years Imaging studies within 3 years of study protocol	CRSsNP 96 CRSwNP 113	CRSsNP: 39.6% CRSwNP: 61.1%	PM2.5, BC Utilized home address to estimate exposure based on a spatial model from Pittsburgh air pollution	NR	Air pollutant exposure did not vary between groups. LMS, FESS, and steroid usage were not associated with air pollutant exposure in CRSsNP or CRSwNP. Authors thought due to small subgroup analyses
		CRS diagnosed based on International Consensus Statement on Allergy and Rhinology All patients seen by one of the two rhinologists at institution		CRSsNP: 51.2 CRSwNP: 51		11.5
Park et al. 39	2009 South Korea	Age ≥ 19 Data from KNHANES	NR	NR	PM10, NO2, O3, SO2, CO Data from the Korean National Institute of Environmental Research (2009)	Age, sex, region	No correlation between CRS incidence and air pollution levels For each 1 μg/m3 unit increase in PM10 level, prevalence of CRS increased significantly, with an OR of 1.22 (95% CI 1.02–1.46; p = .031)
		CRS diagnosed by trained residents in those with visible nasal polyps endoscopically or with at least two of the following symptoms: anterior/posterior nasal drip, nasal obstruction, facial pain/tenderness, and olfactory dysfunction more than 3 months in duration (either an anterior/posterior nasal drip or nasal obstruction was required as a presenting symptom)		NR		10
Mady et al. 40	2013–2015 Pittsburgh, Pennsylvania	CRSsNP CRSwNP ≥18 years old Described rhinitis symptoms and had subsequent allergy testing Same residence and occupation for past 5 years Imaging studies within 3 years of study protocol	CRSsNP 58 (22 allergy‐negative) CRSwNP 67 (23 allergy‐negative)	CRSsNP: 41.4% CRSwNP: 53.7%	PM2.5, BC Utilized home address to estimate exposure based on a spatial model from Pittsburgh air pollution	Age, sex	Allergy‐negative patients had higher exposure levels to both PM2.5 (p = .030) and BC (p = .044) than their allergy‐positive counterparts. This trend was carried by CRSwNP patients, in which allergy‐negative patients had higher PM2.5 (p = .032) and BC (p = .017) exposure than allergy‐positive patients. Allergy‐negative CRSwNP patients also had higher PM2.5 exposure than allergy‐positive CRSsNP patients (p = .023) There were no differences in exposure to PM2.5/BC noted between allergy‐positive and allergy‐negative CRSsNP patients. There was no difference in SNOT‐22, LMS, or steroid usage between allergy‐positive and allergy‐negative patients In CRSsNP, BC correlated with SNOT‐22 (r = .55, p = .042, log‐transformed r = .56, p = .039). No correlation with other measured severity metrics In CRSwNP, there was no relationship between PM2.5/BC exposure and any evaluated severity metric
		All patients seen by one of the two rhinologists at institution		CRSsNP: 47 ± 15.7 CRSwNP: 49 ± 15.5		10
Mady et al. 41	2013–2015 Pittsburgh, Pennsylvania	CRSsNP CRSwNP ≥18 years old Same residence and occupation for past 5 years Imaging studies within 3 years of study protocol	CRSsNP 96 CRSwNP 138	CRSsNP: 39.6% CRSwNP: 59.4%	PM2.5, BC Utilized home address to estimate exposure based on a spatial model from Pittsburgh air pollution	Age, sex	Mean PM2.5 levels were higher in Pittsburgh (11.28 vs. 10.98 μg/m3, p = .002). CRSwNP patients living in Pittsburgh had higher PM2.5 exposure versus patients living elsewhere (11.28 vs. 10.95 μg/m3, p = .008), but this was not seen in CRSsNP patients (11.27 vs. 11.03 μg/m3, p = .097). No significant difference based on BC levels Air pollutant exposure was not different between CRSsNP and CRSwNP groups for PM2.5 or BC CRSsNP: PM2.5 exposure was associated with FESS in CRSsNP patents (p = .015), with each unit increased in PM2.5 exposure corresponding to a 1.89‐fold increased risk in proportion of CRSsNP patients needing more surgery (p = .015). BC exposure was associated with SNOT‐22 scores in CRSsNP patients (p = .015), with each 0.1‐unit increase in BC corresponding to a 7.97‐unit increase in SNOT‐22 scores in CRSsNP patients (p = .008). No relationship in other evaluated metrics and PM2.5 /BC exposure in CRSsNP patients CRSwNP: No relationship between PM2.5 /BC exposure and any evaluated metric in CRSwNP patients
		All patients seen by one of the two rhinologists at institution		CRSsNP: 51.2 ± 16.5 CRSwNP: 51.4 ± 15.4		10
Sommar et al. 42	2008–2010 Swedish cities: Umeå, Uppsala, Stockholm, and Gothenburg	CRS Based on GA2LEN survey from 19 European countries	CRS 110 Control 226	CRS: 54% Control: 49%	NO2, NOx Exposure based on patient's home address Pollutant levels estimated based on modeling and local emission data		Exposure to nitric oxides did not differ between controls and CRS European quality of life‐5D was not associated with measures of NO2 and NOx in controls or CRS
		EPOS 2012 criteria		CRS: 45.4 (95% CI: 42.5–48.3) Control: 47.5 (95% CI: 45.5–49.5)		19
Wolf 43	1990–1999 Cologne, Germany	Patients with CRS who underwent FESS and	1435 patients, evaluated based on districts	NR	SO2, TSP, NOx Pollutant levels estimated from modeling programs and simulation study considering meteorological parameters Data then aggregated to level of city “administrative districts.” Exposure status determined based on which administrative district the patient lived in.	Demographic and socioeconomic composition of the city districts—social status, family status Social status, residential stability of city district, distance between university hospital and city district	Evaluated disease prevalence through age‐standardized rates of patients per 100,000 inhabitants per year who underwent surgery in the 1990s. Broken into quintiles from low to high rates. In the overall case, there was no relationship between patient rates and air pollution (r = −.025). When breaking data down into city districts above and below the average air pollution level and controlling for confounders, city districts with an above average air pollution level were associated with patient rates (r = .382, p <.05). In districts with air pollution below average, city rates were not associated with air pollution (r = −.135, p >.10). When broken up by time period, this relationship was maintained in 1990–1994 (r = .427, p <.05), but not in 1995–1999 (p = .265), which authors note was during a period of decreasing air pollution
		Diagnosed at the Otorhinolaryngology Department at the University of Cologne		NR		8.5

Abbreviations: BC, black carbon; BMI, body mass index; CO, carbon monoxide; COPD, chronic obstructive pulmonary disease; CRS, chronic rhinosinusitis; CRSsNP, chronic rhinosinusitis without nasal polyps; CRSwNP, chronic rhinosinusitis with nasal polyps; EPA, Environmental Protection Agency; EPOS, European position paper on rhinosinusitis and nasal polyps; FESS, functional endoscopic sinus surgery; GA²LEN, Global Allergy and Asthma European Network; KNHANES, Korea National Health and Nutrition Examination Survey; LMS, Lund‐Mackay Score; MINORS, methodological index for nonrandomized studies; NO₂, nitrogen dioxide; NO_x, nitrogen oxides; NR, not reported; O₃, ozone; PM_2.5, particulate matter ≤2.5 μm in aerodynamic diameter; PM₁₀, particular matter ≤10 μm in aerodynamic diameter; SNOT‐22, sinonasal outcomes test‐22; SO₂, sulfur dioxide; TSP, total suspended particulate.