Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2026 Mar 27.
Published in final edited form as: Int J Environ Health Res. 2025 Mar 13;35(11):3588–3603. doi: 10.1080/09603123.2025.2477585

Characterizing air pollution exposure methodologies in rhinology: a scoping review

Conner J Massey a, Benton Tullis b, Colin Johnson c, Gretchen Oakley a, Richard Orlandi d, Jeremiah Alt a,e,f,g, Abigail Pulsipher a,d,e, Amarbir Gill h, John Horel c, Kristine Smith a
PMCID: PMC13019986  NIHMSID: NIHMS2146505  PMID: 40079501

Abstract

Characterization of air pollution assessment methodologies in rhinologic disease research is lacking. A scoping review was thus conducted to survey exposure methods in studies examining common rhinologic conditions: allergic rhinitis (AR) and chronic rhinosinusitis (CRS). Several medical databases were queried for variables relating to (1) adults with a diagnosis of CRS or AR and (2) air pollution exposure. Data was extracted for pollutants assessed, method of quantifying exposure, assessment of residential stability, inclusion of authors with expertise in environmental exposure assessment, and disease-related outcomes. Thirty-four articles were included for analysis − 16 for AR and 18 for CRS. Fifteen studies originated from East Asia, 10 from North America, and 6 from Europe. The most common pollutant studied was PM2.5 (28 studies), with most studies investigating multiple pollutants. Twenty-one studies used a nearby air monitor to quantify exposure, 9 studies reported whether subjects had residential stability for the period assessed, and 17 studies included authors with climate science background. Timeframes included both acute and chronic exposure. Current methods to quantify air pollution exposure in rhinology vary considerably and inconsistently employ expertise from environmental scientists. Future investigations may benefit from multidisciplinary collaboration, reporting of residential stability, and standardized reporting metrics.

Keywords: Chronic rhinosinusitis, allergic rhinitis, particulate matter, air pollution

Introduction

Climate change has become an increasingly pressing issue that has resulted in deterioration of air quality. This has largely been driven by combustion of fossil fuels, resulting in air pollution, wide fluctuations in temperature and humidity, and loss of particulate dispersion, among other mechanisms (Fiore et al. 2015). These processes can lead to extreme levels of ambient air pollutants, such as particulate matter (PM) and ozone. Poor air quality has long been known to impact overall health through multiple different organ systems while also leading to increased morbidity and mortality (Pope et al. 2019). In particular, PM and gaseous pollutants have been strongly linked to the development and exacerbation of lower airway diseases, such as asthma, through oxidative stress and the induction of multiple inflammatory pathways (Guarnieri and Balmes 2014).

While the interaction between air quality and lower respiratory health is relatively well characterized, we are only now beginning to understand how these pollutants affect the nasal cavity and paranasal sinuses. A recent systematic review by Leland et al. (2022) found that air pollution correlates strongly with an increased prevalence of chronic rhinosinusitis (CRS) through multiple studies, while also showing that exposures can lead to worsened disease severity and histopathologic changes. For allergic rhinitis (AR), similar associations were seen, with a recent meta-analysis demonstrating an increased risk of developing this condition for both PM and gaseous (e.g. ozone) exposures (Li et al. 2022).

Both of these systematic reviews performed exhaustive and detailed reviews of the literature, however, there was a lack of quality assessment for the collected data with respect to exposure methodologies. This is of critical importance as poorly characterized pollution assessments may lead to faulty associations and conclusions for the role that pollution plays in the development or severity of these illnesses. Furthermore, numerous techniques and approaches exist for measuring pollutant exposures, ranging from the simple (e.g. residential location to nearest air monitor) to the complex and sophisticated (e.g. land-use regression and chemical dispersion modeling); different methods may offer varying levels of accuracy and precision for a given pollutant exposure. As the field of exposure science in rhinology continues to expand, which it is projected to given the growing impact of climate change (Kim et al. 2023), there exists a clear need to utilize sound and robust methods for measuring exposures for a pathology of interest. We thus performed a scoping review on exposure methodologies in rhinology studies with the following objectives: (1) identify knowledge gaps; (2) characterize patterns, inconsistencies, and shortcomings with respect to exposure methodologies for two of the most commonly studied conditions in our field – AR and CRS; (3) provide recommendations for future rhinologic studies that aim to assess ambient air pollution exposure.

Methods

Literature search

A literature review was performed. A scoping methodology was chosen to identify knowledge gaps and evaluate the quality of methodologies used in rhinologic environmental exposure studies. We limited our review to air pollution studies in rhinology to generate findings that are more focused and pertinent for researchers within the field. Furthermore, it is well known that particle size is related to dispersion within the airway (Heyder et al. 1986), and we thus wanted to highlight a single anatomical region to determine the relevant pollutants at play. A literature search querying PubMed, Cochrane Library, SCOPUS, EMBASE, and Web of Science databases was completed in December 2023; there were no restrictions for date of publication. Disease terms including “chronic rhinosinusitis,” “chronic sinusitis,” “sinusitis,” “allergic rhinitis,” and “rhinitis” were combined with exposure terms including “particulate matter,” “ozone,” “sulfur dioxide,” “nitrogen dioxide,” and “air pollution.” The complete list of search terms and search strategy can be found in Supplementary Tables S1 and S2. We followed the Preferred Reporting Items for Systematic reviews and Meta-analyses extension for scoping reviews (PRISMA-ScR) (Tricco et al. 2018).

Covidence software (Veritas Health Innovation, Melbourne, Australia) was used for managing study review. All collected titles were evaluated for inclusion based on title and abstract by two independent reviewers (CJM and BT), with disputes resolved by a third independent reviewer (KMS). Studies passing title/abstract screening then underwent full-text review in a similar fashion. Study eligibility criteria are listed in Table 1.

Table 1.

Study eligibility criteria.

Inclusion criteria Exclusion criteria
 ● Adults 18 years or older with chronic sinusitis or allergic rhinitis
 ● Clear documentation of methodology for outdoor air pollution exposure		 ● Pediatric populations
● Studies without physician diagnosed condition of interest (e.g. symptoms only or self-reported)
● Studies including other upper respiratory conditions (e.g. non-allergic rhinitis, pharyngitis, laryngitis)
● Studies including lower respiratory conditions (e.g. asthma) or other non-rhinologic conditions (allergic dermatitis)
● Occupational exposure
● Diesel or traffic related exposure
● Indoor air pollution
● Laboratory-based studies
● Abstracts or brief research notes/letters in which exposure methods could not be extracted
● Non-English studies
Open in a new tab

Data extraction

Studies that met inclusion and exclusion criteria underwent extraction by two independent reviewers (CJM and BT), collecting data on study location, measured pollutants, exposure measurement method and method of spatial assignment. The exposure methods were categorized based on the methodology types presented in Table 2. Study methods were further assessed for exposure timeframe or lag assessment, disease outcomes, and whether studies accounted for residential stability of their subjects during the exposure timeframe. For a study to have assessed for residential stability, the methods section of the paper needed to include a statement that the authors confirmed that the study subjects actually resided at the exposure address during the entirety of the exposure period. Finally, the author list for each study was evaluated for inclusion of a contributor with climate, atmospheric, or environmental science background based on the listed academic affiliation. If the affiliation was too vague (e.g. “University School of Medicine”), then a Google search was performed of the individual to gain insight on their educational or training background and area of expertise. Following the PRISMA-ScR (Heyder et al. 1986) guidelines, a risk of bias assessment was not performed, particularly as the overall goal of this work was to investigate methodological variance rather than determine whether air pollution is a causative or exacerbating factor in sinonasal disease.

Table 2.

Types of exposure methods for environmental studies. Adapted from Xie et al. (2017) and the WHO “overview of methods to assess population exposure to ambient air pollution” (WHO 2023).

Method Description Pros Cons Examples
“Nearest neighbor” Averages from a single monitor are applied to a given geographic area (ZIP code, county, or city) − Easy to use and understand
− Data is often publicly available from state-run monitors
− Good temporal resolution		 − Does not account for spatial variability or land use characteristics
− Becomes less accurate as proximity decreases
− Utility suffers in areas with few monitors (e.g. rural)		 Yang et al. (2022)
Kriging or weighted interpolation Uses data from multiple monitors to generate weighted averages based on relative distance to monitors − May be more accurate than “nearest neighbor”
− May allow for standard error of exposure estimates		 − Requires more complex mathematical modeling
− Does not account for land use characteristics		 Kwon et al. (2019)
Land-use regression modeling Incorporates GIS-derived variables (e.g. traffic patterns, industrial vs agricultural areas, population density, wind speed, elevation) with monitor data to generate exposure models − May be more accurate than “nearest neighbor”
− Allows for nuanced spatial variation in pollution concentrations		 − More difficult to apply over long timeframes
− Requires GIS data, the resolution of which may vary
− Does not factor in long-range transport of secondary pollutants		 Velasquez et al. (2020)
Chemical dispersion/transport modeling Blends meteorological data with emission inventories (e.g. traffic, industrial, agricultural sources) − Does not rely on monitoring stations
− Can be used over a wide geographic area		 − Requires complex mathematical models and computing power
− Models dependent on the quality of the input data
− Potential biases should be crosschecked against real-world observations		 Bédard et al. (2020)
Open in a new tab

Abbreviations: geographic information systems (GIS).

Results

Database search efforts yielded 6225 articles. 3328 articles underwent title/abstract screening, ultimately yielding 34 articles for data extraction and analysis. See Figure 1 for PRISMA flow chart. There were 18 articles studying CRS (Wolf 2002; Mady et al. 2018, 2018; Lu et al. 2020; Velasquez et al. 2020, 2022; Padhye et al. 2021; Patel et al. 2021; Wee et al. 2021; Zhang et al. 2021; Elam et al. 2022; Chen et al. 2022; Han et al. 2022; Peeters et al. 2022; Yang et al. 2022; Hagedorn et al. 2023; Wang et al. 2023; Salman et al. 2023), and 16 on AR (Zhang and Zhang 2014; Chen et al. 2016; Chu et al. 2019; Kwon et al. 2019; Park et al. 2019; Bédard et al. 2020; Todkill et al. 2020; Wang et al. 2020, 2023; Jiang and Yan 2022; Li et al. 2022; Wu et al. 2022; Yang et al. 2022; Dąbrowiecki et al. 2023; Kim et al. 2023; Tang et al. 2023).

Figure 1.

Figure 1.

Open in a new tab

PRISMA flow chart for study selection.

Chronic rhinosinusitis studies (Table 3)

Table 3.

Data extraction table for CRS.

Authors/year n Location/study design Pollutants Exposure measurement type*, description, and spatial assignment Exposure timeframe/lag Assessment of residential stability? Climate/environmental scientist collaboration Outcomes
Elam et al. (2022) 399 USA (non-deployed military population); retrospective case-control PM2.5, PM10, NO2, O3 (1) “EPA pre-generated air pollution values”; assignment based on location of military stationing Values averaged over 1 year Yes Yes Risk of CRS diagnosis
Hagedorn et al. (2023) 489 Oregon, Utah, Colorado, South Carolina, USA; prospective cross-sectional SO2, NO2, CO, PM 2.5, PM 10 (1) EPA air quality monitors, assignment based on closest monitor to residential ZIP Subjects enrolled between 2011–2020; Exposure data averaged over 5 year period 2015–2020 No No CRS outcomes
Wang et al. (2023) 1086 China; retrospective cross-sectional PM2.5, PM10 (5) Satellite derived spatiotemporal models assigned based on coordinates of residential address Values averaged over 1 year prior to exposure measure Yes Yes Risk of eosinophilic CRS diagnosis and recurrence
Chen et al. (2022) 160504 Taiwan; retrospective cross-sectional from national database SO2, NO2, O3, PM 2.5, PM 10 (1) 74 monitors throughout Taiwan; assignment based on address of treating hospital Daily means averaged over 10 years No No Incidence of CRS
Han et al. (2022) 1606 South Korea; retrospective cross-sectional from national database SO2, NO2, O3, PM 10 (1) 16 regional state-run monitors throughout Korea; assignment based on residential address Exposure data averaged over 5 years No No Prevalence of AR and CRS
Peeters et al. (2022) 278 Belgium; retrospective cross-sectional PM 2.5, BC, NO2, O3 (2) Detrended kriging interpolation model; spatial assignment based on geolocation recorded through a phone-based symptom app Daily averages for a given symptom day; 0–7 day lags prior to exposure day N/A Yes CRS symptoms
Salman et al. (2023) 2092 Chicago, IL, USA; retrospective cross-sectional PM 2.5 (1) Zip-specific “EPA records combining site monitoring data and air quality modeling” Annual averages over a 5 year period No No Number of high acuity visits for CRS
Velasquez et al. (2022) 234 Pittsburgh, PA, USA; retrospective cross-sectional PM2.5, NO2, BC (3) PM2.5/BC: mobile sampling from 70 sites over Allegheny county during a summer and winter period; values used to create LUR averages NO2: integrated empirical geographic regression modeling at a national level One year Yes Yes Interaction between air pollution, area deprivation index, and CRS outcomes
Yang et al. (2022) 282 Beijing, China; retrospective cross-sectional SO2, NO2, O3, CO, PM 2.5, PM 10 (1) 35 air monitors throughout Beijing; assignment based on residential address to closest monitor 30 day cumulative daily averages prior to study enrollment No No CRS outcomes
Padhye et al. (2021) 132 Chicago, IL, USA; retrospective case-control PM 2.5 (1) EPA EJScreen data (2018 fusion of OAQPS monitor data and CMAQ air quality models), assignment based on census block group Exposure data pulled 4–5 years prior to subject enrollment No No Nasal microbiome, nasal histopathology in CRS
Patel et al. (2021) 291 Chicago, IL, USA; retrospective cross-sectional PM 2.5, O3 (1) EPA EJScreen data (fusion of OAQPS monitor data and CMAQ air quality models), assignment based on census block group Subjects enrolled 2015–2019; exposure data culled from 2018 No No Histopathologic CRS findings
Wee et al. (2021) 30759 Korea; retrospective case-control SO2, NO2, O3, PM 10, CO (1) Nationwide monitors; no spatial assignment stated 1, 3, 6, 12 months before CRS diagnosis No No Prevalence of CRS
Zhang et al. (2021) 6102 “Northeast” USA; retrospective case-control PM 2.5 (5) Machine learning approach incorporating land use, meteorology, satellite measurements, chemical transport model 12, 24, 36, 60 months prior to CRS diagnosis No Yes Diagnosis of CRS
Lu et al. (2021) 183943 hospital visits Xinxiang, China; retrospective cross-sectional SO2, NO2, O3, CO, PM 2.5, PM 10 (1) Averaged values from 4 state-run sensors in Xinxiang; no spatial assignment 0–7 days prior to hospital presentation No Yes Acute presentation of CRS cases to a hospital
Velasquez et al. (2020) 234 Pittsburgh, PA, USA; retrospective cross-sectional PM2.5, BC (3) 37 sensors for creation of hybrid LUR model. Exposure value averaged from a 300 m radius around a residential address during Summer 2012 and Winter 2013 Over 1 year; “temporally adjusted all exposure estimates by daily average from central EPA monitor” over 5 year period to gauge for “chronic exposure” Yes No Interaction between air pollution, occupational exposure, and CRS outcomes
Mady et al. (2020) 234 Pittsburgh, PA, USA; retrospective cross-sectional PM 2.5, BC (3) 37 sensors for creation of hybrid LUR model. Exposure value averaged from a 300 m radius around a residential address during Summer 2012 and Winter 2013 Over 1 year; “temporally adjusted all exposure estimates by daily average from central EPA monitor” over 5 year period to gauge for “chronic exposure” Yes No Need for revision surgery, SNOT-22 scores, LMS for patients with CRSwNP and CRSsNP
Mady et al. (2018) 125 Pittsburgh, PA, USA PM 2.5, BC (3) 37 sensors for creation of hybrid LUR model. Exposure value averaged from a 300 m radius around a residential address during Summer 2012 and Winter 2013 Over 1 year; “temporally adjusted all exposure estimates by daily average from central EPA monitor” over 5 year period to gauge for “chronic exposure” Yes No AR and CRS disease
Wolf (2018) N/A# Cologne, Germany SO2 (measured), NOx (simulated) (1) City district averages from a government monitor, assignment based on address Averaged over 5 years No No CRS prevalence
Open in a new tab
*

As categorized by one of four methods: nearest neighbor (1), kriging (2), land use regression modeling (3), chemical dispersion modeling (4), multiple methods (5). Abbreviations: chronic rhinosinusitis (CRS), allergic rhinitis (AR), black carbon (BC), environmental protection agency (EPA), community multiscale air quality (CMAQ), office of air quality planning and standards (OAQPS); land use regression (LUR).

#

Reported as an age-standardized rate of patients per 100,000 per year undergoing sinus surgery.

Ten studies were conducted in the USA, with six studies performed in Asia (China, South Korea, Taiwan) and two in Europe (Belgium, Germany). The studies from Pittsburgh, USA (Mady et al. 2018; Lu et al. 2020; Velasquez et al. 2020; Salman et al. 2023) likely used the same cohort and methods for multiple papers, but are being counted separately. The most common studied pollutant was PM 2.5 (15 studies), although most studies included multiple particulate and gaseous pollutants. 6 studies included an author with environmental/climate science background.

For exposure methods, 11 studies employed a “nearest neighbor” approach, where the exposure assignment was dictated by the air quality monitor closest to the residential address of the subject, although one study assigned exposures based on the location of the treating hospital rather than the subjects themselves. Other methods used included land use regression models (4 studies), kriging interpolation (1 study), or multiple methods for a hybrid model (2 studies). There were two studies that collected air quality monitor data, but the method of spatial assignment was not described.

Exposure timeframe also varied considerably. For studies examining incidence or prevalence of CRS, exposure timeframes varied from 1 to 10 years; 5 years was often used for “chronic” exposure. Studies that focused on symptoms, CRS-specific outcomes, or CRS-related hospital presentations used shorter exposure timeframes with 0 to 7 day lag periods. There were only six studies that documented that their study subjects resided at their designated address for the exposure period. The study by Peeters et al. (2022) used geolocation derived from the subject’s cellphone over a 7-day period, so the residential stability assessment did not apply.

Allergic rhinitis studies (Table 4)

Table 4.

Data extraction table for AR.

Authors/year n Location/study design Pollutants Exposure measurement type*, description, and spatial assignment Exposure timeframe/lag Assessment of residential stability? Climate/environmental scientist collaboration Outcomes
Dabrowiecki et al. (2002) 1047 visits 3 largest cities in Poland; retrospective cross-sectional PM2.5, PM10, NO2, SO2, O3 (1) Daily averages from all available monitoring stations for a given city; spatial assignment to treating hospital Collected over 6 years; 0–7 day lag with “time stratified case-crossover analysis” No Yes Number of hospital visits for AR
Kim et al. (2023) >1.4 million Seoul, Korea; retrospective cross-sectional, national database PM10, PM2.5, NO2, CO, O3, SO2 (1) Monthly averages obtained from daily averages from 25 city monitors; spatial assignment to treating hospital Collected over 3 years; “seasonal auto regression integrated analysis moving average” used to address seasonality No No Medical costs associated for visits for AR
Tang et al. (2023) 20653 visits Hangzhou, China; retrospective cross-sectional PM2.5, PM10, CO (1) Daily averages from meteorological bureau; spatial assignment to treating hospital Data collected over 2 years; 0–7 day lag No Yes Number of outpatient visits for AR
Wang et al. (2023) N/A# Hohhot, China; retrospective cross-sectional PM2.5, PM10, SO2, NO2, CO, O3 (1) Daily average obtained from China Air Quality Online Monitoring and Analyzing Platform; spatial assignment to treating hospital Data collected over 3 years; 0–7 day lag Yes No Number of outpatient visits for AR
Jiang et al. (2023) 10838 visits Shenyang, China; retrospective cross-sectional PM2.5, PM10, SO2, NO2, CO, and O3 (1) Daily values from single monitoring station; spatial assignment to treating hospital Collected over 6 months No Yes Number of outpatient visits for AR, symptom severity
Li et al. (2022) 68861 visits Beijing, China; retrospective cross-sectional PM10, PM2.5, NO2, CO, O3, SO2 (1) 35 urban monitors to create a daily average; spatial assignment to treating hospital Data collected over 6 years; 0–5 day lag times Yes Yes Number of outpatient visits for AR
Wu et al. (2022) 33599 visits Beijing, China; retrospective cross-sectional PM2.5, PM10, SO2, NO2 (1) Daily averages; spatial assignment to treating hospital Data collected over 5 years; 0–6 day lag Yes Yes Number of outpatient visits for AR
Yang et al. (2022) 12868 visits Chongching, China; retrospective cross-sectional PM2.5, PM10, SO2, NO2, CO, O3 (1) Daily averages from 17 monitors; spatial assignment to treating hospital Data collected over 2 years; 0–30 day lag No No Number of outpatient visits for AR
Bédard et al. (2022) 3323 Northern and Central Europe; retrospective cohort PM2.5, O3 (4) SILAM global-to-mesoscale dispersion model correlated to geolocation of subject’s phone app Day of app use and 1 day lag N/A Yes Rhinitis symptoms as logged using a phone app
Todkil et al. (2020) 186401 visits London, UK; retrospective cross-sectional PM2.5, NO2, O3 (1) Single monitor used as proxy for entire city, missing data filled from nearby monitor; spatial assignment to treating clinic Collected over 2 years No Yes Rate of change of outpatient visits for AR
Wang et al. (2020) 229685 visits Beijing, China; retrospective cross-sectional PM2.5 (1) Daily average from a single monitor; spatial assignment to treating hospital Data collected over 2 years; 0–2 day lag No No Number of outpatient visits for AR
Chu et al. (2020) 33063 visits Nanjing, China; retrospective cross-sectional PM10, PM2.5, NO2, SO2, CO, O3 (1) Daily averages taken from city monitors; spatial assignment to treating hospital Collected over 2 years; 0–3 day lags No Yes Number of outpatient visits for AR
Kwon et al. (2019) N/A# Seoul, Korea; retrospective cross-sectional SO2, PM10, O3, NO2, CO (2) 128 urban monitors with kriging interpolation; assignment by municipal district Annual average from 2014 No Yes Number of outpatient visits for AR, by municipal district
Park et al. (2019) 7399 Korea; retrospective cross-sectional PM2.5 (1) Readings from 251 monitors for an annual average; spatial assignment by administrative region Annual average from 2009 No No AR and CRS prevalence
Chen et al. (2019) 124773 visits Taipei, Taiwan; retrospective cross-sectional PM10, SO2, NO2, CO, O3 (1) Daily averages from 6 stations across the city; assignment based on treating hospital Collected over 6 years; cumulative 2 day lag period with “time stratified case-crossover analysis” No Yes Number of outpatient visits for AR
Zhang et al. (2016) 31829 visits Beijing, China; retrospective cross-sectional PM10, SO2, NO2 (1) Daily averages from 11 monitors; assignment based on treating hospital Data collected over 1 year; 0–3 day lag No Yes Number of outpatient visits for AR
Open in a new tab
*

As categorized by one of four methods: nearest neighbor (1), kriging (2), land use regression modeling (3), chemical dispersion modeling (4), multiple methods (5). Abbreviations: allergic rhinitis (AR), system for integrated modeLing of Atmospheric composition (SILAM).

#

Was reported as an average rate of visits over time.

Of the 16 AR studies, 13 originated from Asia (9 in China, 3 in Korea, and 1 in Taiwan), with the remainder in Europe (Poland, the United Kingdom, and other Northern European countries). 13 studies examined PM2.5, usually in combination with other pollutants. 11 studies included an author with environmental science background.

Fourteen studies used a “nearest neighbor” approach with one or more state-run air quality monitors interrogated for pollutant data. For these studies, the data was spatially assigned to the treating hospital rather than the subjects, with the outcome of interest being the frequency of outpatient visits for AR. Only 3 of these studies documented residential stability of their subjects. One study used a chemical dispersion model with assignment based on the geolocation of a subject’s phone. There was a single use of kriging interpolation.

For studies examining the frequency of AR-specific hospital visits, a lag period ranging from 0 to 30 days was generally assessed with data collected over multiple years.

Discussion

Exposure measurement methods

Several methods exist for measuring environmental exposure to ambient air pollution (Table 2). The most commonly used and easiest to understand is the “nearest neighbor” approach, whereby data collected from an air quality monitor is assigned to a subject based on spatial proximity. Significant variability exists in the quality of the data these monitors collect in terms of reliability, accuracy, precision, and the frequency of measurements for a given pollutant. How the data from these monitors is then analyzed is yet another potential variable, i.e. whether readings are averaged over days/weeks vs. cumulative exposure vs. time spent exceeding acceptable regulatory limits. As the distance between the monitor and the subject’s address increases, accuracy consequently suffers.

Kriging attempts to overcome some of the accuracy limitations of spatial assignment in “nearest neighbor” by weighting the air monitor data based on the distance of the subject’s address to the monitor(s). The complexity of exposure assessment increases significantly with land-use regression and chemical dispersion models, which incorporate known pollution patterns, emission inventories, and meteorological data.

Regardless of which method is used, the accuracy of the data is contingent on assuring that the subject has actually lived at the listed address during the exposure timeframe assessed. Only 9 studies in our review included statements that they accounted for residential stability for their included subjects. Lack of residential stability assessment becomes more problematic for studies that use longer exposure periods, e.g. a study assessing a 5-year exposure window for an enrolled subject who only moved to the area 3 months prior to study enrollment.

Assigning exposure based on the location of the treating hospital is equally problematic as it assumes that subjects reside in close proximity to the hospital, which may or may not be the case. Of the 14 studies that assigned based on the treating facility, only 3 of these assessed for residential stability (e.g. subjects were only included if they resided in the same county as the hospital).

Further complicating matters, spatial assignment based on residential address only partially captures a subject’s actual exposure as it does not account for the location or environment in which the subject works or spends outside of the house. Indoor and/or occupational pollutants may impact an individual’s overall exposure. Wearable or mobile monitoring methods, e.g. using a phone app-based geolocation to more accurately track a subject’s whereabouts over time (Bédard et al. 2020), may allow for measurement of ambient, indoor, and occupational exposures. However, this method is generally not suitable for longer term (e.g. months-years) exposure assessment. Our review specifically did not look at occupational or indoor pollution. It should be noted that only a single study performed occupational exposure assessment as part of their methodology (Velasquez et al. 2020); this was investigated as a separate study outcome rather than as a variable to control for in their ambient air pollution exposures. Indoor and occupational exposures may be exciting areas for further investigation in future studies.

Ultimately, an investigator may need to employ multiple methodologies for a comprehensive assessment of the relationship between disease and our environment. This approach is best illustrated by Hill et al. in their study on the air pollution and lung adenocarcinoma (Hill et al. 2023). Here, they used multiple methods to quantify exposure, including land use regression and chemical dispersion, and assessed several global populations, some of which they were able to obtain detailed lifetime residential histories for accurate exposure assignment. Through this detailed work, they were able to show that PM2.5 promotes oncogenesis for certain lung tumors. Environmental exposure studies in rhinology have yet to achieve a comparable level of rigor in their methodologies.

Timeframe: study outcomes dictate acute vs chronic exposure assessment

Acute (short-term) and chronic (long-term) air pollution exposure timeframes are not precisely defined, either in the literature nor by pollution regulating organizations. The EPA considers 90 days to 2 years to be “chronic” and less than 24 hours for “acute” (EPA 2011). The WHO considers “annual” exposure to be “long term” and less than 24 hours for “short term” (WHO 2021). The rhinology literature roughly follows these time frames for air pollution.

The majority of collected AR studies in our review assessed fluctuations in acute presentations of AR at a treating facility as it relates to short-term pollution exposures. These generally had exposure lag periods of 0–7 days, with data collected over 1 year or more. There was only a single study employing a long-term exposure for investigating an AR prevalence outcome. Two studies assessed AR symptom severity, also with short-term exposure.

Outcomes investigated in the CRS literature were more varied and included incidence/prevalence of CRS, microbiome/histopathology associations, and CRS-disease specific measures such as symptom scores and radiological disease severity. These tended to have chronic exposure assessments ranging from 1 month to 10 years. There were only two studies that used short-term exposures of 1 week or less.

Climate scientist collaboration

Our review documents a trend in the rhinology literature of conducting environmental exposure science in the absence of collaboration with experts in this field. Only 6 of 18 studies in the CRS literature had a climate or environmental science/epidemiology author; the AR literature fared somewhat better with 11 of 16 studies including a climate scientist. Our assessment of climate scientist collaboration may underreport the actual number of these instances if authors did not appropriately list their affiliations, either in the manuscript or online.

As the rhinologic environmental exposure field is inherently multi-disciplinary in nature, it is important to include experts from both rhinology and climate science in these studies. Climate scientists can provide critical expertise in the reliability of local air quality monitors, how meteorological phenomena may impact pollution, and how best to quantify and model exposure over a given timeframe. As environmental exposure science is often regional in nature, collaboration with an expert who is intimately familiar with the atmospheric and pollution trends specific to that region becomes paramount. Cross-disciplinary collaboration is key to maximizing scientific success when tackling complex problems and is the foundation to “team science” (Hall et al. 2019).

Knowledge gaps, future directions, and considerations for conducting environmental exposure research in rhinology

Our scoping review illustrates the rapid proliferation of rhinologic environmental exposure studies, especially over the past few years. While this has significantly advanced our understanding of how air pollution impacts CRS and AR, our results indicate that many key areas remain to be explored, particularly with respect to exposure methodologies in this nascent field. For example, numerous spatial assignment methods exist, and there is a notable lack of studies that explore the impact of how different spatial assignment models may alter exposure-disease correlations.

It is also striking to note the difference between the CRS and AR literatures, especially with respect to outcomes. Outcomes within the CRS literature tend to be richer and more varied, examining validated disease metrics, tissue histopathology, and prevalence. The AR literature is much more limited and focuses almost exclusively on frequency of outpatient visits for this condition, which, as noted previously, may be methodologically fraught when the pollution exposure is assigned to the treating facility rather than the subjects themselves. There thus exists an exciting opportunity to improve our understanding of the relationship between AR and air pollution by focusing on elements such as disease severity, response to treatment, or lab-based studies. Of course, our results may be impacted by the eligibility criteria we set and the number of studies that met these criteria.

As the field continues to grow, we propose several suggestions for conducting environmental exposure research in rhinology that will increase methodological quality and transparency:

  1. Environmental exposure studies should include collaboration or consultation with a climate or environmental scientist who can provide expertise on the environmental and meteorological milieu in which the study is being conducted, while also offering insight on the most accurate ways to map or model exposure based on the available data.

  2. When possible, studies should assess residential stability of their subjects, particularly with long-term exposures. If this is not feasible, then lack of stability assessment needs to be acknowledged as a potential limitation in study design.

  3. Environmental exposure studies should carefully and completely document the exposure methodology used, including types of air quality monitors used, how data was averaged or interpolated over time, and how spatial assignment was conducted.

  4. Researchers should acknowledge that outdoor air pollution may only be a part of the total environmental exposure for a given individual, which can also be impacted by indoor, travel-related, and occupational exposures. These should be assessed or controlled for, when feasible.

  5. Researchers conducting exposure studies should utilize measurement methods that most accurately capture the exposure of interest and provide rationale for their choices; climate scientist collaboration may facilitate these decisions.

We anticipate that these suggestions will enable the creation of exposure studies in rhinology that more robustly and accurately assess environmental factors while realistically reflecting assumptions and limitations that are inherent to many of the methods used, thus increasing quality and reducing bias within the literature. Future review work in this arena may focus on the impacts of indoor or occupational exposures on sinonasal health.

Conclusion

Progress has been made in our understanding of how environmental air pollution impacts sinonasal health. However, considerable variability exists in the quality of methods that are used to characterize environmental exposure, especially with respect to residential stability assessment and spatial assignment or modeling techniques. Establishing robust collaborations between rhinologists and climate scientists is perhaps one of the most important elements of conducting these cross-disciplinary studies, and will undoubtedly increase quality and reduce bias within the field moving forward.

Supplementary Material

Supp Doc

Supplemental data for this article can be accessed online at https://doi.org/10.1080/09603123.2025.2477585.

Funding

The author(s) reported that there is no funding associated with the work featured in this article.

Footnotes

This work was accepted for poster presentation at the September 2024 American Rhinologic Society meeting in Miami, FL, USA.

Disclosure statement

No potential conflict of interest was reported by the author(s).

References

  1. Bédard A, Sofiev M, Arnavielhe S, Antó JM, Garcia-Aymerich J, Thibaudon M, Bergmann KC, Dubakiene R, Bedbrook A, Onorato GL, et al. 2020. Interactions between air pollution and pollen season for rhinitis using mobile technology: a MASK-POLLAR study. J Allergy Clin Immunol Pract. 8(3):1063–1073. e1064. doi: 10.1016/j.jaip.2019.11.022. [DOI] [PubMed] [Google Scholar]
  2. Chen CC, Chiu HF, Yang CY. 2016. Air pollution exposure and daily clinical visits for allergic rhinitis in a subtropical city: Taipei, Taiwan. J Toxicol Environ Health A. 79(12):494–501. doi: 10.1080/15287394.2016.1182002. [DOI] [PubMed] [Google Scholar]
  3. Chen SW, Lin HJ, Tsai SC, Lin C-L, Hsu CY, Hsieh T-L, Chen C-M, Chang K-H. 2022. Exposure to air pollutants increases the risk of chronic rhinosinusitis in Taiwan residents. Toxics. 10(4):173. doi: 10.3390/toxics10040173. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Chu H, Xin J, Yuan Q, Wang M, Cheng L, Zhang Z, Lu M. 2019. The effects of particulate matters on allergic rhinitis in Nanjing, China. Environ Sci Pollut Res Int. 26(11):11452–11457. doi: 10.1007/s11356-019-04593-5. [DOI] [PubMed] [Google Scholar]
  5. Dąbrowiecki P, Chciałowski A, Dąbrowiecka A, Piórkowska A, Badyda A. 2023. Exposure to ambient air pollutants and short-term risk for exacerbations of allergic rhinitis: a time-stratified, case-crossover study in the three largest urban agglomerations in Poland. Respir Physiol Neurobiol. 315:104095. doi: 10.1016/j.resp.2023.104095. [DOI] [PubMed] [Google Scholar]
  6. Elam T, Raiculescu S, Biswal S, Zhang Z, Orestes M, Ramanathan M. 2022. Air pollution exposure and the development of chronic Rhinosinusitis in the active duty population. Mil Med. 188(7–8):e1965–e1969. doi: 10.1093/milmed/usab535. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. EPA. 2011. Exposure factors handbook glossary. Washington, D.C: U.S. Environmental Protection Agency. https://cfpub.epa.gov/ncea/risk/recordisplay.cfm?deid=236252. [Google Scholar]
  8. Fiore AM, Naik V, Leibensperger EM. 2015. Air quality and climate connections. J Air Waste Manag Assoc. 65 (6):645–685. doi: 10.1080/10962247.2015.1040526. [DOI] [PubMed] [Google Scholar]
  9. Guarnieri M, Balmes JR. 2014. Outdoor air pollution and asthma. Lancet. 383(9928):1581–1592. doi: 10.1016/S0140-6736(14)60617-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Hagedorn R, Tullis B, Nguyen C, Stockard R, Mace JC, Ramakrishnan VR, Beswick DM, Soler ZM, Smith TL, Alt JA, et al. 2023. Does air pollutant exposure impact disease severity or outcomes in chronic rhinosinusitis? Int Forum Allergy Rhinol. 14(4):755–764. doi: 10.1002/alr.23250. [DOI] [PubMed] [Google Scholar]
  11. Hall K, Vogel A, Croyle R. 2019. Strategies for team science success handbook of evidence-based principles for cross-disciplinary science and practical lessons learned from health researchers. Handb Evidence-Based Princ Cross-Discip Sci Pract Lessons Learned Health Res. 3–17. https://link.springer.com/book/10.1007/978-3-030-20992-6?page=1#toc. [Google Scholar]
  12. Han M, Choi SJ, Jeong Y, Lee K, Lee TH, Lee SH, Kim TH. 2022. Association between concentration of air pollutants and prevalence of inflammatory sinonasal diseases: a nationwide cross-sectional study. Am J Rhinol Allergy. 36 (5):649–660. doi: 10.1177/19458924221099373. [DOI] [PubMed] [Google Scholar]
  13. Heyder J, Gebhart J, Rudolf G, Schiller CF, Stahlhofen W. 1986. Deposition of particles in the human respiratory tract in the size range 0.005–15 μm. J Aerosol Sci. 17(5):811–825. doi: 10.1016/0021-8502(86)90035-2. [DOI] [Google Scholar]
  14. Hill W, Lim EL, Weeden CE, Lee C, Augustine M, Chen K, Kuan F-C, Marongiu F, Evans EJ, Moore DA, et al. 2023. Lung adenocarcinoma promotion by air pollutants. Nature. 616(7955):159–167. doi: 10.1038/s41586-023-05874-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Jiang F, Yan A. 2022. Correlation of pollen concentration and meteorological factors with medical condition of allergic rhinitis in Shenyang Area. Comput Math Methods Med. 2022:1–10. doi: 10.1155/2022/4619693. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Kim J, Waugh DW, Zaitchik BF, Luong A, Bergmark R, Lam K, Roland L, Levy J, Lee JT, Cho D-Y, et al. 2023. Climate change, the environment, and rhinologic disease. Int Forum Allergy Rhinol. 13(5):865–876. doi: 10.1002/alr.23128. [DOI] [PubMed] [Google Scholar]
  17. Kim JY, Park Y, Kim SH, Kim SP, Park SW, Yoon HJ. 2023. Effect of ambient air pollutants on the medical costs of allergic rhinitis in Seoul, Korea. Laryngoscope. 133(8):1828–1833. doi: 10.1002/lary.30464. [DOI] [PubMed] [Google Scholar]
  18. Kwon MY, Lee JS, Park S. 2019. The effect of outdoor air pollutants and greenness on allergic rhinitis incidence rates: a cross-sectional study in Seoul, Korea. Int J Sustain Devel World Ecol. 26(3):258–267. doi: 10.1080/13504509.2019.1570982. [DOI] [Google Scholar]
  19. Leland EM, Vohra V, Seal SM, Zhang Z, Ramanathan M Jr. 2022. Environmental air pollution and chronic rhinosinusitis: a systematic review. Laryngoscope Investig Oto. 7(2):349–360. doi: 10.1002/lio2.774. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Li S, Wang G, Wang B, Cao S, Zhang K, Duan X, Wu W. 2022. Has the risk of outpatient visits for allergic rhinitis, related to short-term exposure to air pollution, changed over the past Years in Beijing, China? Int J Environ Res Public Health. 19(19):12529. doi: 10.3390/ijerph191912529. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Li S, Wu W, Wang G, Zhang X, Guo Q, Wang B, Cao S, Yan M, Pan X, Xue T, et al. 2022. Association between exposure to air pollution and risk of allergic rhinitis: a systematic review and meta-analysis. Environ Res. 205:112472. doi: 10.1016/j.envres.2021.112472. [DOI] [PubMed] [Google Scholar]
  22. Lu M, Ding S, Wang J, Liu Y, An Z, Li J, Jiang J, Wu W, Song J. 2020. Acute effect of ambient air pollution on hospital outpatient cases of chronic sinusitis in Xinxiang, China. Ecotoxicol Environ Saf. 202:110923. doi: 10.1016/j.ecoenv.2020.110923. [DOI] [PubMed] [Google Scholar]
  23. Mady LJ, Schwarzbach HL, Moore JA, Boudreau RM, Kaffenberger TM, Willson TJ, Lee SE. 2018. The association of air pollutants and allergic and nonallergic rhinitis in chronic rhinosinusitis. Int Forum Allergy Rhinol. 8 (3):369–376. doi: 10.1002/alr.22060. [DOI] [PubMed] [Google Scholar]
  24. Mady LJ, Schwarzbach HL, Moore JA, Boudreau RM, Willson TJ, Lee SE. 2018. Air pollutants may be environmental risk factors in chronic rhinosinusitis disease progression. Int Forum Allergy Rhinol. 8(3):377–384. doi: 10.1002/alr.22052. [DOI] [PubMed] [Google Scholar]
  25. Padhye LV, Kish JL, Batra PS, Miller GE, Mahdavinia M. 2021. The impact of levels of particulate matter with an aerodynamic diameter smaller than 2.5 μm on the nasal microbiota in chronic rhinosinusitis and healthy individuals. Ann Allergy Asthma Immunol. 126(2):195–197. doi: 10.1016/j.anai.2020.10.006. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Park M, Lee JS, Park MK. 2019. The effects of air pollutants on the prevalence of common ear, nose, and throat diseases in South Korea: a National population-based study. Clin Exp Otorhinolaryngol. 12(3):294–300. doi: 10.21053/ceo.2018.00612. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Patel TR, Tajudeen BA, Brown H, Gattuso P, LoSavio P, Papagiannopoulos P, Batra PS, Mahdavinia M. 2021. Association of air pollutant exposure and sinonasal histopathology findings in chronic rhinosinusitis. Am J Rhinol Allergy. 35(6):761–767. doi: 10.1177/1945892421993655. [DOI] [PubMed] [Google Scholar]
  28. Peeters S, Wang C, Bijnens EM, Bullens DMA, Fokkens WJ, Bachert C, Hellings PW, Nawrot TS, Seys SF. 2022. Association between outdoor air pollution and chronic rhinosinusitis patient reported outcomes. Environ Health. 21(1):134. doi: 10.1186/s12940-022-00948-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Pope CA 3rd, Lefler JS, Ezzati M, Higbee JD, Marshall JD, Kim S-Y, Bechle M, Gilliat KS, Vernon SE, Robinson AL, et al. 2019. Mortality risk and fine particulate air pollution in a large, representative cohort of U.S. Adults. Environ Health Perspect. 127(7):77007. doi: 10.1289/EHP4438. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Salman FM, Dasgupta R, Eldeirawi KM, Nyenhuis SM, Lee VS. 2023. Associations of community-level particulate matter with high-acuity visit presentation for sinusitis. Am J Otolaryngol. 44(2):103739. doi: 10.1016/j.amjoto.2022.103739. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Tang W, Sun L, Wang J, Li K, Liu S, Wang M, Cheng Y, Dai L. 2023. Exploring associations between short-term air pollution and daily outpatient visits for allergic rhinitis. Risk Manag Healthc Policy. 16:1455–1465. doi: 10.2147/RMHP.S416365. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Todkill D, de Jesus Colon Gonzalez F, Morbey R, Charlett A, Hajat S, Kovats S, Osborne NJ, McInnes R, Vardoulakis S, Exley K, et al. 2020. Environmental factors associated with general practitioner consultations for allergic rhinitis in London, England: a retrospective time series analysis. BMJ Open. 10(12):e036724. doi: 10.1136/bmjopen-2019-036724. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Tricco AC, Lillie E, Zarin W, O’Brien KK, Colquhoun H, Levac D, Moher D, Peters MDJ, Horsley T, Weeks L, et al. 2018. PRISMA extension for scoping reviews (PRISMA-ScR): checklist and explanation. Ann Intern Med. 169 (7):467–473. doi: 10.7326/M18-0850. [DOI] [PubMed] [Google Scholar]
  34. Velasquez N, Gardiner L, Cheng TZ, Moore JA, Boudreau RM, Presto AA, Lee SE. 2022. Relationship between socioeconomic status, exposure to airborne pollutants, and chronic rhinosinusitis disease severity. Int Forum Allergy Rhinol. 12(2):172–180. doi: 10.1002/alr.22884. [DOI] [PubMed] [Google Scholar]
  35. Velasquez N, Moore JA, Boudreau RM, Mady LJ, Lee SE. 2020. Association of air pollutants, airborne occupational exposures, and chronic rhinosinusitis disease severity. Int Forum Allergy Rhinol. 10(2):175–182. doi: 10.1002/alr.22477. [DOI] [PubMed] [Google Scholar]
  36. Wang J, Shen S, Yan B, He Y, Zhang G, Shan C, Yang Q, Qin L, Duan Z, Jiang L, et al. 2023. Individual exposure of ambient particulate matters and eosinophilic chronic rhinosinusitis with nasal polyps: dose-response, mediation effects and recurrence prediction. Environ Int. 177. doi: 10.1016/j.envint.2023.108031. [DOI] [PubMed] [Google Scholar]
  37. Wang M, Wang S, Wang X, Tian Y, Wu Y, Cao Y, Song J, Wu T, Hu Y. 2020. The association between PM(2.5) exposure and daily outpatient visits for allergic rhinitis: evidence from a seriously air-polluted environment. Int J Biometeorol. 64(1):139–144. doi: 10.1007/s00484-019-01804-z. [DOI] [PubMed] [Google Scholar]
  38. Wang X, Gao C, Xia Y, Xu X, Li L, Liu Y, Yao X, Cao N, Li Z, Fang X. 2023. Effect of air pollutants and meteorological factors on daily outpatient visits of allergic rhinitis in Hohhot, China. J Asthma Allergy. 16:1217–1228. doi: 10.2147/JAA.S430062. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Wee JH, Min C, Jung HJ, Park MW, Park B, Choi HG. 2021. Association between air pollution and chronic rhinosinusitis: a nested case-control study using meteorological data and national health screening cohort data. Rhinology. 59(5):451–459. doi: 10.4193/Rhin21.141. [DOI] [PubMed] [Google Scholar]
  40. WHO. 2023. Overview of methods to assess population exposure to ambient air pollution. [accessed 5 1]. https://www.who.int/publications/i/item/9789240073494. [Google Scholar]
  41. WHO. WHO. 2021. What are the WHO air quality guidelines? [accessed 4 23]. https://www.who.int/news-room/feature-stories/detail/what-are-the-who-air-quality-guidelines. [Google Scholar]
  42. Wolf C 2002. Urban air pollution and health: an ecological study of chronic rhinosinusitis in Cologne, Germany. Health Place. 8(2):129–139. doi: 10.1016/S1353-8292(01)00040-5. [DOI] [PubMed] [Google Scholar]
  43. Wu R, Guo Q, Fan J, Guo C, Wang G, Wu W, Xu J. 2022. Association between air pollution and outpatient visits for allergic rhinitis: effect modification by ambient temperature and relative humidity. Sci Total Environ. 821:152960. doi: 10.1016/j.scitotenv.2022.152960. [DOI] [PubMed] [Google Scholar]
  44. Xie X, Semanjski I, Gautama S, Tsiligianni E, Deligiannis N, Rajan R, Pasveer F, Philips W. 2017. A review of urban air pollution monitoring and exposure assessment methods. ISPRS Int J Geo-Inf. 6(12):389. doi: 10.3390/ijgi6120389. [DOI] [Google Scholar]
  45. Yang D, Yan Y, Pu K. 2022. The association between air pollutants and daily outpatient visits for allergic rhinitis: a Time-series analysis based on distribution lag nonlinear model in Chongqing, China. Risk Manag Healthc Policy. 15:1501–1515. doi: 10.2147/RMHP.S373085. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Yang X, Shen S, Deng Y, Wang C, Zhang L. 2022. Air pollution exposure affects severity and cellular endotype of chronic rhinosinusitis with nasal polyps. Laryngoscope. 132(11):2103–2110. doi: 10.1002/lary.29974. [DOI] [PubMed] [Google Scholar]
  47. Zhang Y, Zhang L. 2014. Prevalence of allergic rhinitis in china. Allergy Asthma Immunol Res. 6(2):105–113. doi: 10.4168/aair.2014.6.2.105. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Zhang Z, Kamil RJ, London NR, Lee SE, Sidhaye VK, Biswal S, Lane AP, Pinto JM, Ramanathan M. 2021. Long-term exposure to particulate matter air pollution and chronic rhinosinusitis in nonallergic patients. Am J Respir Crit Care Med. 204(7):859–862. doi: 10.1164/rccm.202102-0368LE. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

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

Supplementary Materials

Supp Doc
NIHMS2146505-supplement-Supp_Doc.docx (16.9KB, docx)

RESOURCES