Ambient Air Pollution and Smell Function: Evidence from the National Geographic Smell Survey

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Mainland , View ORCID Profile Danielle R. Reed doi: https://doi.org/10.1101/2025.06.10.25329361 Vicente Ramirez 1 Monell Chemical Senses Center , Philadelphia, PA, 19104 Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Vicente Ramirez Sandie Ha 2 University of California Merced, Health Science Research Institute , Merced, CA, 95348 Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Sandie Ha Valentina Parma 1 Monell Chemical Senses Center , Philadelphia, PA, 19104 Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Valentina Parma Pamela Dalton 1 Monell Chemical Senses Center , Philadelphia, PA, 19104 Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Pamela Dalton Joel D. Mainland 1 Monell Chemical Senses Center , Philadelphia, PA, 19104 3 Department of Neuroscience, University of Pennsylvania , Philadelphia, PA 19104 Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Joel D. Mainland Danielle R. Reed 1 Monell Chemical Senses Center , Philadelphia, PA, 19104 Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Danielle R. Reed For correspondence: reed{at}monell.org Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Abstract Background Olfactory sensory neurons are located outside the brain, allowing them to detect environmental chemicals. However, this comes at the cost of exposure to potential toxins, which may decrease olfactory function. Methods We sought to assess the association between olfactory function and air pollution, measured by the National Geographic Smell Survey and data from the Environmental Protection Agency. We examined the effects of air pollution exposure on perceived odor intensity and identification ability across 97,087 survey respondents assigned air pollution exposures at the zip code level. Results The results show that NO 2 , SO 2 , and O 3 were associated with slight alterations in olfactory acuity, and these relationships differed by odor and the type of pollutant. Specifically, increases in annual mean concentration by a standard deviation of NO 2 (OR=0.97; CI=0.96-0.97) and SO 2 (OR=0.99; CI=0.98-1.00) were associated with reduced odor identification ability. Conversely, increases in O 3 concentration were associated with a slight increase in olfactory ability (OR=1.01; CI=1.00-1.02). Conclusions Air quality is associated with olfactory health, underscoring the need to investigate the mechanisms driving pollution-induced impairment. Introduction Environmental pollution has become one of the most pressing issues in global health, as growing evidence links ambient environmental exposures to multiple health outcomes, including cardiovascular, respiratory, mental health, and neurodegenerative disorders 1 – 3 . The olfactory epithelium is positioned at the interface of the internal and external milieu, and its unique, direct connection between olfactory sensory neurons and the brain renders it especially susceptible to harmful environmental exposures. As demonstrated in animal models, air pollutants can reach the brain through this olfactory route, disrupting neural function and promoting neurodegeneration 4 , 5 . In humans, olfactory dysfunction is an early marker for Alzheimer’s and Parkinson’s diseases, cognitive decline, longevity, and overall well-being—all of which have demonstrated associations with air pollution 6 – 11 . It is plausible that toxic environmental exposures may compound declines in olfactory function, indicating early health deterioration. Early studies on the relationship between ambient air pollution and olfactory function primarily compared populations from high- and low-pollution areas. Two reports compared olfactory function in adults exposed to high pollution levels in Mexico City to adults in the less polluted Mexican State of Tlaxcala 12 , 13 . Using ecological [commercial beverage 13 ] and standardized stimuli 12 , these studies found lower odor thresholds, higher odor discrimination scores, and mild microsmia in 12% of residents, compared to 35.5% of Mexico City residents with mild to severe microsmia 12 , 13 . Calderón-Garcidueñas et al. performed autopsies on 35 Mexico City residents and nine controls (including children), reporting structural abnormalities in olfactory bulbs, the presence of ultrafine particulate matter in olfactory bulbs, and of amyloid-β 42 and α-synuclein in the olfactory nerves and bulbs of the cadavers from Mexico City but not in controls 12 . These findings suggest that olfactory deficits and neurodegenerative biomarkers emerge early during development and may be influenced by environmental pollutant exposure. Even though an increasing body of research links exposure to pollutants (e.g., airborne particulates, gaseous phase pollutants, heavy metals) with olfactory dysfunction 6 , 11 , 14 – 17 , the relationship between air pollution, olfactory and general health remains poorly understood, as it is often conducted in studies with small sample size. The landmark National Geographic Smell Survey (NGSS), distributed in the September 1986 issue of the National Geographic Magazine, offers a psychophysical, multifunction assessment of olfactory ability, measuring odor detection, intensity, and identification, measures that reflect peripheral (nose) and central (brain) olfactory function 18 , 19 . With data from over 1.2 million individuals in the United States (U.S.), the NGSS is one of the largest databases with psychophysical measures of human olfactory function 20 .It includes geographic indicators (zip codes) so we can examine the relationship between ambient air pollution and olfactory health when combined with U.S. Environmental Protection Agency (EPA) Air Quality Surveillance data 21 . The late 1980s were a time of heightened air pollution, just before the implementation of key regulatory measures such as the 1987 Montreal Protocol and the 1990 Clean Air Act Amendments 22 , 23 . Our study aims to explore the impact of common specific air pollutants in the U.S. on odor intensity and identification, contributing to the growing body of literature on environmental influences on olfactory function. We hypothesize that higher average annual pollution exposure is associated with decreased olfactory performance. Methods Sampling Strategy The NGSS was mailed to every National Geographic magazine subscriber. Figure S1 shows the exclusion criteria applied to this convenience sampling, which resulted in a final analytic sample of 97,087 U.S. individuals with complete olfactory and air pollutant exposure data. Download figure Open in new tab Figure S1. Flow diagram depicting the progression of participants through a study. Note: reproduced based on STROBE guidelines for reporting observational studies (von Elm et al., 2007). Olfactory Assessment The NGSS, a self-administered "scratch-and-sniff" booklet, employed a panel of 6 odors ( Table S1 ) 20 to obtain multiple dimensions of olfactory function, including odor intensity and identification. Intensity was rated on an ordinal scale from 1 (weak) to 5 (strong). Odor identification was assessed using a 12-choice panel of 10 odor descriptors, a “no odor” option, and an “other” option. Identification accuracy was coded as a binary variable, indicating whether the participant correctly matched each odor. A non-response was considered incorrect if a participant did not respond to an odor, but responded to other odors. Otherwise, individuals who did not respond to all odors were removed from the study sample. A total number of correct identifications was scored on a 0-6 scale, summing correct responses across six trials. See Figure S2 for the psychophysical odor portion of the NGSS. Download figure Open in new tab Figure S2. The Psychophysical Assessment in the National Geographic Smell Survey. Odor intensity and identification are assessed in portions C (odor intensity) and G (odor identification) of the NGSS. View this table: View inline View popup Download powerpoint Table S1. Odor stimuli in the NGSS and National Ambient Air Quality Standards for the Pollutants Measured Air Pollution Measures The EPA has standardized air quality data collections since 1980. To determine exposure to five criteria pollutants (PM 10 , Pb, SO 2 , NO 2 , and O 3 ), we used the 1986 annual concentration data from U.S. air monitoring sites 24 Linking Pollutant Exposure to Olfactory Performance The NGSS provides participant residence based on zip code, while the EPA monitors air quality at specific locations with latitude and longitude coordinates. To link these datasets, we used shapefiles for Zip Code Tabulation Areas (ZCTAs) from the 2000 U.S. Census (which are the earliest ZCTAs available) to extract geographic coordinates for zip code boundaries and centroids across the U.S 25 . We projected these geographical data using the R packages rgdal , rgeo , and sf, and a 30 km spatial buffer was created around zip code centroids 26 – 28 . Monitoring sites within 30km of a centroid were assigned to that zip code. Zip codes with concentrations for all five pollutants of interest were utilized in our analysis. Crosswalk files were used to fix changes in zip code assignment and boundaries between 1990 and 2000, and merged with the NGSS zip code data. This updated NGSS was then merged with the ZCTA geocoded monitoring sites data, and annual concentrations of air pollutants for each participant were assigned based on their reported zip code. Participants missing data in any of the variables of interest were removed ( Figure S1 ). The final sample included 97,087 individuals, each assigned annual-averaged concentrations for PM 10 , Pb, SO 2 , NO 2 , and O 3 . Table 1 details descriptive statistics on demographic variables and average assigned air pollution exposures. View this table: View inline View popup Table 1. Description of demographic features of the NGSS sample analyzed Statistical Analysis Multiple single-pollutant regression models were utilized to assess the association between each pollutant, the perceived intensity of each odor, and the correct identification of odors. All models included mean annual pollutant exposure as an independent variable and covariates [i.e., sex, age, and smoking status, known modulators of olfactory function 20 , 29 , 30 ]. Mean annual pollutant exposures were z-scored, and age was centered around its mean. We utilized partial proportional odds logistic regression models to assess the association between intensity ratings and air pollution exposure. Models were fit in STATA 16 using the GOLOGIT2 library and the -autofit option, which optimizes constraints on explanatory variables to meet the proportional odds assumption 31 , 32 . Multilevel logistic regression models examined the relationship between participants’ exposure to individual air pollutants and identification scores, with random-intercepts grouped to each individual across the 6-odor identification trials. Similarly, logistic regression models were used to assess the identification of individual odors. We applied the Benjamini-Hochberg correction to account for the False Discovery Rate on multiple comparisons 33 . All analyses were run in STATA 16, R 4.3, and RStudio 2024.12.1. Figures were created using R and the ggplot2 package. Results Weak and Inconsistent Associations Between Air Pollution and Odor Intensity The association between air pollution and perceived odor intensity is modest and variable in direction and magnitude. Figure 1 shows single-pollutant associations with odor intensity by outcome threshold (how intense the odor was rated from 1 to 5, see Supplementary Files for model, covariate-adjusted estimates). A standard deviation (SD) increase in PM 10 increased the odds of intensity ratings >1 and >4 androstenone. Download figure Open in new tab Figure 1. Generalized Ordinal Logistic Regression (Partial Proportional Odds Model) of Intensity Ratings. Perceived intensity rating cut points are listed on the X-axis, and odds ratios on the Y-axis. The odds ratio for each intensity cutpoint is the odds of rating greater than that cutpoint (e.g., Rating >1 vs 1, Rating >2 vs 1 or 2). Black bars represent the 95% confidence interval. The red line represents the null assumption, and represents the baseline odds ratio (e.g., OR=1) Conversely, the effect of NO 2 was not significant for rating androstenone intensity above 1, 2, or 3 but showed a modest effect on higher intensity rating thresholds with decreased odds of rating >4. A one SD increase in exposure to PM 10 was associated with higher intensity ratings of galaxolide across all rating thresholds. Similarly, an association between O 3 exposure and galaxolide ratings was found, but this effect was only significant for rating thresholds >3 and >4. A one SD increase in SO 2 concentration decreased the odds of higher-intensity ratings for galaxolide by ∼3-8% across rating thresholds between 1 and 3. The relationship diminished after adjusting for multiple testing at ratings threshold >4. The strongest associations were found between SO 2 and NO 2 with eugenol intensity, though the effects varied across thresholds. An SD increase in SO 2 concentration led to a 17.5% decrease in the odds of rating the intensity >1 and a 3% decrease in rating the intensity >4. An SD increase in NO 2 reduced the odds of higher intensity eugenol ratings by ∼3% across all rating thresholds. The relationship between O 3 and amyl acetate was mixed: we found a negative relationship was for ratings >2 and positive for ratings >4, however, the significance of the relationship at rating threshold >2 diminished after adjusting p-values for multiple testing. An SD increase in SO 2 concentration was associated with a 4.7% increase in the odds of rating amyl acetate intensity higher across all thresholds. All pollutants, except Pb, were associated with higher intensity ratings of mercaptans per SD increase in concentration. No pollutant had significant associations with intensity ratings of rose. Strong Association between Pollutant Exposure and Overall Odor Identification Ability The results for odor identification are modest and show a mixed relationship between air pollution exposure and odor identification ability ( Figure 2 ). Download figure Open in new tab Figure 2. Binomial Logistic Regression for Correct Identification of Odors. The odds ratios for correctly identifying an odor over the six odor trials are plotted for each pollutant. Black bars represent the 95% confidence interval. The red line represents the null assumption and the baseline odds ratio. Decreased odor identification performance was associated with an SD increase in NO 2 (OR=0.97; CI=0.96-0.97) and SO 2 (OR=0.99; CI=0.98-1.00) concentrations. However, an SD increase in O 3 concentration was associated with a slight increase in olfactory ability (OR=1.01; CI=1.00-1.02). No associations were found between PM 10 and Pb with overall odor identification ability. After removing galaxolide and androstenone, for which specific anosmias have been documented 34 the associations between olfactory identification ability and pollutant exposures remained for NO 2 and SO 2 , but not for O 3 . Additionally, a small positive association was found between olfactory ability and Pb (OR=1.01; CI=1.00-1.02). A binary logistic regression for identifying individual odors reveals heterogeneity in the relationship between pollution exposures and olfactory identification of individual odors. Among the strongest relationships, a moderate association between NO2 and SO2 and eugenol identification revealed a 12.1% and 10.3% decrease in the odds of correctly identifying eugenol per SD increase of NO 2 and SO 2, respectively. Statistically significant associations were observed in both negative and positive directions across all odors and pollutants, as documented in Figure 3 . Download figure Open in new tab Figure 3. Logistic Regression for Correct Identification By Odor. Note. Odds ratios for correctly identifying individual odors vs the pollutant. Black bars represent the 95% confidence interval. The red line represents the null assumption, and represents the baseline odds ratio (e.g., OR=1) Discussion Main Findings We assessed the association between air pollution exposure and olfactory function in a large, population-based sample of 97,087 U.S participants. We utilized a psychophysical smell test to evaluate multiple dimensions of olfactory function. Our findings were mixed: exposure to certain pollutants, such as NO 2 and SO 2 , was associated with a significant decline in olfactory performance, whereas other pollutants, such as Pb, demonstrated no measurable impact at the observed exposure levels. While the evidence supports a relationship between air pollution and olfactory function, the observed effects were modest overall. The strongest pollutant-specific associations were limited to single odors or specific intensity thresholds. Overall, NO 2 showed the most consistent and strongest estimates for its negative effect on olfactory function, followed by SO 2 . These results align with prior literature linking NO 2 to adverse effects on physiological processes and negative health outcomes 6 , 35 , 36 . However, some findings diverged from our hypotheses, suggesting potential protective effects of certain pollutants or interactions between pollutants. For example, O 3 exposure was associated with increased intensity ratings for multiple odorants but concurrently linked to reduced identification of specific odorants (e.g., eugenol and amyl acetate). Accepting these findings as valid suggests that the effects of air pollution on olfactory function are heterogeneous, likely influencing distinct olfactory pathways via diverse mechanisms. However, the current data do not allow us to elucidate these mechanisms fully, but suggest that reduced odor identification may provide indirect evidence that central olfactory pathways are vulnerable to gaseous pollutants. Air pollutants can adversely affect the olfactory system through multiple direct and indirect mechanisms: i) exposure damaging the olfactory epithelium, causing structural changes and impairing function 4 , 15 , 37 ; ii) pollutant-triggered neuroinflammation and oxidative stressor 8 , 15 ; iii) translocation from nasal mucosa to central nervous system, bypassing the blood-brain barrier 4 , 9 ; iv) pollutant-triggered mitochondrial dysfunction, which compromises homeostasis 38 . Future research is needed to clarify how gaseous pollutants affect smell function. To incorporate the findings that exposure to some pollutants, like O 3 , may be a protective factor for olfactory ability, we note that air pollution effects are contextual and interdependent. For example, the inverse concentration relationship between O 3 and NO 2 can confound observed health effects, as these relationships are highly dependent on local environmental conditions and exposure contexts and can influence the spatial variability of these pollutants 39 – 41 . For example, the NGSS survey was disseminated in September 1986, which aligns with the US cold season, when NO 2 levels are typically high and O 3 levels are low. Notably, O 3 at low concentrations has been associated with protective effects on health 42 . While most of these pollutants’ effects are modest, the patterns indicate that alterations in olfactory function may be partially attributable to environmental exposures, warranting further investigation into pollutant-specific and context-dependent effects. What is known Recent studies have suggested that environmental exposures alter olfactory function. Evidence has accumulated linking olfactory dysfunction to exposure to various pollutants such as airborne particulates, gaseous phase pollutants, and heavy metals 6 , 14 , 15 . Our analysis found that gaseous pollutants produced the largest effects, with NO 2 revealing the most consistent findings. This result agrees with previous studies that have associated urban air pollutants such as NO 2 with olfactory dysfunction, particularly in elderly individuals 6 , 43 . Zhang et al. found an association between anosmia and long-term exposure to PM 2.5 by examining the ICD9/10 diagnostic codes 44 . Scussiatto et al. demonstrated that ambient exposure to PM 10 was associated with decreased smell identification scores 17 We did not find such associations; instead, we found that the PM 10 was often protective or irrelevant. While unexpected, this is consistent with recent findings from Oleszkiewicz et al., which suggested that airborne particulates were associated with increased sensitivity 16 . Our findings align with previous work, which associated NO 2 exposure with poor olfactory function in older urban-dwelling adults 6 . With this in mind, we conclude that increased exposure to NO 2 and SO 2 , such as simply living near a busy roadway, may lead to increased risks of a decline in olfactory function 8 , 15 , 45 . What this study adds To date, this dataset represents the largest multi-dimensional olfactory database matched with environmental data, providing a unique opportunity and, critically, a model for future exploration of inter-individual variability in olfactory function and pollutant exposures. Our findings reveal that air pollution is more consistently associated with alterations in olfactory identification, expected to be mediated by central mechanisms, rather than olfactory intensity ratings, a proxy for olfactory sensitivity, linked to function in the peripheral olfactory system. This study generates new hypotheses regarding the effects of specific air pollutants on olfactory function. It underscores the need for high-resolution population-based assessments of multiple olfactory dimensions to be conducted longitudinally. Limitations A few limitations warrant caution in interpreting the results. First, the observed differences in effects between odor identification and intensity may be attributed to methodological factors. The 1-5 scale used for intensity ratings reduces granularity (i.e., each step representing a 20% change), likely obscuring subtler effects and introducing variability due to inter-rater subjectivity. Additionally, the scratch-and-sniff method for odor delivery lacks standardization: the pressure applied during scratching and the distance during sniffing might contribute to variability in intensity ratings. However, because odor concentrations exceeded typical detection thresholds, these factors likely have a smaller, though non-negligible, impact on odor identification. Second, although this sample size allows for powerful observations, using zip codes to assign air pollution exposure introduces bias as it does not account for spatial variability when assigning exposures. Annual averages in air pollution concentration could have masked shorter periods of peak exposures that may have heightened risks. Selection bias is also possible, as we only included people living in zip codes with air monitors within 30 km. Lastly, the study sample may not fully represent the general population because it was self-selected. Conclusions Our findings indicate that significant effects on human olfaction were observed even at pollutant concentrations below the U.S. National Standards, which have implications for human health 46 . Data Availability All data used in this study are publicly available. Zip code tabulation files are publicly available through the US Census Bureau FTP repository at https://www2.census.gov/geo/tiger/TIGER2010/ZCTA5/2000/. Air monitoring sites and pollution data are publicly available through the US EPA Air Quality Service at https://aqs.epa.gov/aqsweb/airdata/download_files.html The National Geographic Smell Survey data is publicly available via Kaggle. https://www.kaggle.com/datasets/jmainland/national-geographic-smell-survey https://www2.census.gov/geo/tiger/TIGER2010/ZCTA5/2000/ https://www.kaggle.com/datasets/jmainland/national-geographic-smell-survey Funding Vicente Ramirez is supported by the National Institute on Deafness and Other Communication Disorders T32 grant under award T32DC000014. Data Availability All data used in this study are publicly available. Zip code tabulation files are publicly available through the US Census Bureau FTP repository at https://www2.census.gov/geo/tiger/TIGER2010/ZCTA5/2000/ . Air monitoring sites and pollution data are publicly available through the US EPA Air Quality Service at https://aqs.epa.gov/aqsweb/airdata/download_files.html The National Geographic Smell Survey data is publicly available via Kaggle. https://www.kaggle.com/datasets/jmainland/national-geographic-smell-survey Alt-text Figure 1 : A grid of small multiple plots showing odds ratios (OR) with 95% confidence intervals across odor intensity cut point thresholds (>1 to >4) for six odors (amyl acetate, androsterone, eugenol, galaxolide, mercaptans, and rose) and five air pollutants (PM 10 , SO₂, Pb, NO₂, and O₃). Each panel represents the association between a specific odor and pollutant across increasing intensity levels. The y-axis represents the odds ratio (OR), and the x-axis represents odor intensity (1 to 4). Black dots with error bars represent the odds ratio (OR) estimates and confidence intervals. A red dashed horizontal line at OR = 1.0 indicates the null effect. Alt-text Figure 2: A plot showing the odds ratio (OR) with 95% confidence intervals across five air pollutants (PM 10 , SO₂, Pb, NO₂, and O 3 ). The y-axis represents the odds ratio (OR), and the x-axis represents odor intensity (1 to 4). Black dots with error bars represent the OR estimates and confidence intervals. A red dashed horizontal line at OR = 1.0 indicates the null effect. Alt-text Figure 3 : A panel of plots showing the odds ratio (OR) with 95% confidence intervals across five air pollutants (PM 10 , SO₂, Pb, NO₂, and O 3 ). The y-axis represents the odds ratio (OR), and the x-axis represents odor tested (amyl acetate, androsterone, eugenol, galaxolide, mercaptans, and rose). Black dots with error bars represent the odds ratio (OR) estimates and confidence intervals. A red dashed horizontal line at OR = 1.0 indicates the null effect. Footnotes Brief biographical statements: Vicente Ramirez is a postdoctoral scholar at the Monell Chemical Senses Center in Philadelphia, PA, USA. Danielle R. Reed is the Chief Science Officer and a Member of the Monell Chemical Senses Center in Philadelphia, PA, USA. Valentina Parma is the Assistant Director and Assistant Member at the Monell Chemical Senses Center in Philadelphia, PA, USA. Joel D. Mainland is a Member of the Monell Chemical Senses Center in Philadelphia, PA, USA. Pamela H. Dalton is a Member of the Monell Chemical Senses Center in Philadelphia, PA, USA. Sandie Ha is an Associate Professor in the Department of Public Health at the University of California, Merced. References 1. ↵ de Bont J , Jaganathan S , Dahlquist M , Persson Å , Stafoggia M , Ljungman P . Ambient air pollution and cardiovascular diseases: An umbrella review of systematic reviews and meta-analyses . 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