Impact of Air Pollution on the Long-Term Decline of Non-Idiopathic Pulmonary Fibrosis Interstitial Lung Disease

Abstract

Objective: This study examines the association between major urban pollutants and the long-term decline of non-idiopathic pulmonary fibrosis interstitial lung disease [non-IPF ILD]. Materials and methods: A total of 41 patients with non-IPF ILD were analyzed from 2011 to 2020, correlating disease long-term decline with concentrations of key pollutants [SO₂, CO, NO₂, O₃, PM_2.5, and PM₁₀] in Madrid. The likelihood of meeting severity criteria was assessed using a generalized linear model, considering the average pollutant levels during severe episodes. Results: At diagnosis, the average age of patients was 62.95 ± 13.13 years, with 47.6% women. The study found no significant association between pollution levels and the probability of meeting severity criteria for non-IPF ILD. The odds ratios were as follows: OR SO₂ = 0.92 [0.82–1.03], p = 0.16; OR CO = 0.99 [0.97–1.05], p = 0.70; OR NO₂ = 0.97 [0.92–1.03], p = 0.38; OR PM_2.5 = 0.79 [0.54–1.17], p = 0.24; OR PM₁₀ = 1.1 [0.94–1.28], p = 0.21; OR O₃ = 0.97 [0.92–1.01], p = 0.20. Conclusions: Our study suggests that, within the cohort of 41 patients with non-IPF ILD enrolled in this study, urban air pollutants in Madrid are not significantly linked to increased long-term decline of non-IPF ILD. This is one of the first studies to explore the impact of various urban pollutants on a diverse cohort of non-IPF ILD patients, including rare conditions like LAM and histiocytosis X. Further research with larger sample sizes and comprehensive exposure assessments is recommended.

1. Introduction

Interstitial lung diseases [ILDs] constitute a diverse group of pathologies sharing common clinical, radiological, and functional characteristics, primarily involving the alveolar-interstitial structures and often also affecting the pulmonary vasculature [1,2,3,4,5,6,7,8,9].

Pollution is a significant global health risk and is one of the leading causes of premature mortality, contributing to the development of numerous health conditions. The increase in pollutants caused by vehicular traffic, rapid urbanization, and energy consumption has led to respiratory system deterioration [10,11]. Sulfur dioxide [SO₂], carbon monoxide [CO], nitrogen dioxide [NO₂], ozone [O₃], and particulate matter [PM_2.5 and PM₁₀] are the most important pollutants to assess in terms of their impact on the respiratory system [12,13,14,15,16,17,18,19]. In fact, when polluted air interacts with and harms the lining of the bronchial tree, it can cause epigenetic alterations that result in higher levels of collagen buildup and abnormal healing of cells that have undergone damage [12,13].

Prior studies investigating the association between air pollution and the progression of interstitial lung diseases [ILDs] have varied significantly in terms of the pollutants measured and the lag-time considered [14,15,16,17,18,19,20]. For instance, Johannson et al. [17] focused on short-term exposures [1–7 days lag] to PM_2.5, NO₂, and O₃, assessing their impact on acute exacerbations of idiopathic pulmonary fibrosis [IPF]. Similarly, Winterbottom et al. [19] explored the relationship between PM₁₀ exposure and lung function decline in patients with IPF, with a focus on exposures up to 14 days before hospital visits. On the other hand, Mariscal-Aguilar et al. [15,21] found that an increase in the average values of CO, NO₂, O₃, and NO_x were related to an increased probability of the development of chronic respiratory failure, hospitalizations, lung function decline, radiological deterioration, clinical worsening and mortality of patients with IPF in different periods of exposure [3, 6, 12, and 36 months of exposure]. Other studies have investigated pollution’s impact on select ILDs. Roeser et al. [22] suggested an association between high O₃ exposure and systemic sclerosis-associated interstitial lung disease (SSc-ILD) severity. Conversely, Pirozzi et al. [23] found no significant links between PM_2.5, O₃, and lung function decline in patients with fibrotic sarcoidosis. These studies underscore the complexity and variability of pollutant–ILD associations, emphasizing the need for more comprehensive investigations considering diverse ILDs and pollutant exposures. However, little evidence exists on air pollution and other ILDs [22].

This study aimed to investigate the association between the prevalent urban air pollutants SO₂, CO, NO₂, O₃, PM_2.5, and PM₁₀ and the long-term decline of non-IPF ILDs. The severity concept is based on symptoms, lung function, and radiological and echocardiographic criteria which significantly impact long-term decline. Ours could be the first study focused on non-IPF ILD that includes rare interstitial lung diseases, such as lymphangioleiomyomatosis [LAM] and histiocytosis X [Langerhans cell histiocytosis], which are infrequent in the general population.

2. Materials and Methods

2.1. Study Design

This was a retrospective exploratory study conducted at the Pulmonary Fibrosis Specialty Clinic of the La Paz University Hospital Pneumology Department. The study focused on a cohort of 41 patients with various diffuse ILDs who were followed up from 2011 to 2020 [Figure 1]. During the follow-up period, the patients were evaluated every 3–4 months according to the IPF follow-up protocol outlined in the clinical practice guidelines [1,2]. Clinical data were collected, including the modified Medical Research Council [mMRC] questionnaire to assess dyspnea level, diagnostic tests including lung function high-resolution computed tomography [HRCT], echocardiogram results, and treatments received. We employed a retrospective exploratory study design to investigate the association between long-term urban pollutant exposure and long-term decline in non-idiopathic pulmonary fibrosis interstitial lung diseases (non-IPF ILDs) within a cohort of patients over time, utilizing previously available clinical records.

2.2. Participants

Patients who met the diagnostic criteria established by the ATS/ERS/Japanese Respiratory Society/Latin American Thoracic Association [1,2,3,4,5,6,7,8,9] were included (

Table 1. Inclusion and exclusion criteria.

Inclusion Criteria	Exclusion Criteria
18–90 years	Lack of residence address data
Patients who met American Thoracic Society/European Respiratory Society/Japanese Respiratory Society/Latin American Thoracic Association diagnosis criteria	Residence more than 15 km from the surveillance station
	Basic data [address, age, sex, complementary tests, and treatments] were not available in the medical records

). Patients who were missing basic information, such as address, age, sex, complementary tests, and treatments in their medical records were excluded from the analysis. Additionally, those who changed their home address during the study period or had incomplete data during the follow-up period were also withdrawn from the study. Lastly, those who lived further than 15 km from this surveillance station were excluded as defined in other studies [14,17,19].

2.3. Air Pollution Monitoring

Data on air pollutant levels were acquired from one surveillance station of the Integral Air Quality System of the Madrid town hall, situated in the center of Madrid city [Escuelas Aguirre air pollution station [coordinates: 40.421686, −3.682307], which measures all major pollutants affecting the respiratory system] [24]. It reports the hourly concentration of SO₂, CO, NO₂, O₃, PM_2.5, and PM₁₀ in units of μg/m³, excluding CO, which is documented in mg/m³. These stations are equipped with sophisticated diagnostic tools and instruments to measure various pollutants with high precision and reliability. Sulfur dioxide [SO₂] concentrations are measured using UV fluorescence analyzers, which detect fluorescence emitted by SO₂ molecules when exposed to ultraviolet light. Carbon monoxide [CO] levels are monitored using Non-Dispersive Infrared [NDIR] sensors that measure the absorption of infrared light by CO molecules. Nitrogen dioxide [NO₂] is detected using chemiluminescence analyzers, which measure the light emitted from the reaction of NO₂ with a reagent, typically ozone. Ozone [O₃] concentrations are determined using UV photometric analyzers that measure the absorption of UV light by ozone molecules. Particulate matter [PM_2.5 and PM₁₀] levels are measured using beta attenuation monitors and optical particle counters; beta attenuation monitors calculate particle concentration by measuring the reduction in beta radiation passing through a particle-laden filter, while optical particle counters estimate particle concentration by detecting the light scattered by particles in the air. These methods are standardized and validated to ensure data accuracy and reliability. The monitoring stations undergo regular calibration and maintenance according to international standards, providing robust data for our study [24].

The distance between the surveillance station and the domicile of each patient was established by employing the Google Maps Distance Calculator after the geocoding of the patient’s address [14,17,19]. The pollution data utilized in this study were exclusively sourced from the most centric outdoor air quality monitoring station located in Madrid. The operational integrity and calibration of air monitoring stations were overseen by the Integral Air Quality System of the Madrid town hall, which conducts regular maintenance and calibration checks in accordance with European Union standards for air quality monitoring [EU Directive 2008/50/EC] [25]. These checks ensure the accuracy and reliability of the data collected. Additionally, the data from these stations are publicly accessible and regularly audited for compliance with these standards, providing further assurance regarding their operational status [24].

Each patient was evaluated multiple times, corresponding to their scheduled clinical visits throughout the study period. Also, to assess the long-term decline from air pollution’s impact, the average concentration of each pollutant was calculated during the period between successive medical visits [3–4 months]. This approach allowed us to examine the association between long-term pollutant exposure and disease decline over the same intervals in which clinical changes were assessed. For the initial visit, the pollutant levels were considered over the preceding 90 days. In instances of mortality, the pollutant concentration average 90 days before the occurrence of this event was examined. The exposure window was considered for each participant from the starting date of data collection [from January 2013 to December 2019] until the occurrence of death, pulmonary transplantation, or 31 December 2019. Patients were considered as having experienced the outcome of interest if they met the predefined severity criteria for non-IPF ILDs at any point during their final follow-up period, which was defined as the last 90 days before their death. The choice to examine air pollutant levels 90 days before death was based on the variable progression and acute exacerbation patterns of non-IPF ILDs. This timeframe captures the crucial period when the cumulative effects of air pollution could notably influence disease severity and outcomes.

2.4. Outcome Variables

Severity criteria were defined by Mariscal-Aguilar et al. [22], based on the requirements in lung transplantation guidelines for the referral of patients with ILD for lung transplantation [14,26,27,28]. Of 6 criteria, 2 were required to define a severe episode of non-IPF ILDs, and 1 of these had to be linked to a decline in pulmonary function [spirometry, diffusing capacity of the lungs for carbon monoxide [DLCO] or 6 min walk test [6MWT]]]. The criteria were the following:

-

Elevated level of breathlessness compared with the prior visit [mMRC dyspnea scale] [28,29,30].

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Absolute reduction in forced volume capacity [FVC] by ≥10% of the predicted value compared with the previous clinical consultation [14,28,29,30,31,32].

-

Absolute decline in DLCO by ≥15% of the predicted value compared with the preceding visit [14,28,29,30,31,32].

-

A reduction of >50 m in the 6MWT relative to the previous medical evaluation [28,29,32].

-

Emergence of new ground glass opacities or increased signs of fibrosis [honeycombing, loss of volume, or traction bronchiectasis] in HRCT [28,29,30,32].

-

Appearance of pulmonary hypertension signs on the echocardiogram [28,29,32].

Lung function assessments conducted during each medical visit [spirometry, plethysmography, DLCO, and 6MWT] were executed following the standardized ATS/ERS criteria [33,34,35]. For this purpose, an integrated module within the MasterLab-body 6.0 version equipment [Viasys, Würzburg, Germany] was employed.

Regarding the radiological images, HRCT was performed and interpreted by radiologists specialized in ILD using a computed tomography scanner equipped with 16 detectors [SOMATOM Emotion; Siemens Medical Solutions, Erlangen, Germany]. All echocardiograms were conducted by the staff of the Department of Cardiology, employing Philips IE33 and Philips EPIQ [Philips, Andover, MA, USA] ultrasound machines.

2.5. Statistical Analysis

To evaluate the severity of air pollution’s impact, pollutant concentrations were averaged over the intervals between consecutive medical appointments, typically spanning 3–4 months. For the first visit, data from the previous 90 days were used. In cases of death, the average pollutant levels were analyzed for the 90 days preceding the event. Each participant’s exposure was tracked from the start of data collection in January 2013 until either their death, lung transplant, or the end of December 2019. Variables were examined including age at the time of inclusion, sex, smoking, and scheduled treatments. Pollution data was compared from the station with patients’ health conditions and according to the severity criteria indicated above.

To assess the impact of air pollution on the long-term decline of non-IPF ILDs, we employed a mixed-effects logistic regression model. This model considered the binary outcome of meeting severity criteria and included fixed effects for pollutant levels and covariates, as well as random intercepts for each patient to account for repeated measurements and within-subject correlations. The analysis considered the mean pollutant levels measured in the period between medical visits or in the 90 days preceding either the initial visit or the date of death documented for the participants. This approach ensured that our exposure assessment aligned with the timing of clinical evaluations, allowing us to examine potential cumulative effects of pollutant exposure on long-term decline of the disease. In calculating the mean pollutant concentrations, the model was enhanced by incorporating a random intercept and an unstructured covariance matrix into the generalized linear mixed model, which assumed a normal distribution and utilized an identity link function. In calculating the mean pollutant concentrations, the model was enhanced by incorporating a random intercept [with 41 levels corresponding to each patient] and an unstructured covariance matrix into the generalized linear mixed model, which assumed a normal distribution and utilized an identity link function. While the sample size was limited, the mixed effect model was suitable for our longitudinal data, accounting for within-subject correlations and providing robust estimates. The effectiveness of this approach was measured by the computed odds ratio [OR] and a 95% confidence interval, which served to estimate the risk associated with a severe health episode corresponding to every increment of 5 units in the levels of SO₂, CO, NO₂, O₃, PM_2.5, and PM₁₀, and for every increase of 0.1 unit in CO concentration. Adjustments were made in the model to account for variables such as age, sex, smoking habits, and the administration of corticosteroids and antifibrotic treatments [22].

All statistical analyses were bidirectional, and p-values below 0.05 were considered noteworthy. The data were analyzed with SAS 9.3 [SAS Institute, Cary, NC, USA].

2.6. Ethics

This study was conducted in accordance with the standards of good clinical practice and the ethical principles of the Declaration of Helsinki. It was approved by the La Paz University Hospital Clinical Research Ethics Committee [code PI-3742, 20 June 2019]. Given the retrospective nature of the study, informed consent was not required.

3. Results

Of the 44 patients in our initial cohort, 3 were excluded because they moved during the study period. The patients’ visits and characteristics are included in

Table 2. Analysis of patient visit frequencies.

Disease	Observation Unit [Number of Visits]	Observation Unit [Number of Visits]/Patient	Observation Unit [Average Visits/Year/Patient]
Sarcoidosis	408	22.6	3.78
NSIP	26	6.5	1.08
NH	30	10	1.17
OP	10	10	0.83
LAM	151	18.85	2.8
HX	7	1	1.17

and

Table 3. Patients’ characteristics.

Parameter	Mean ± SD
Age, years	66 ± 10
Men, n [%]	20 [48.8]
Former smoker	13 [31.7]
Smoker, n [%]	19 [46.3]
Nonsmoker	9 [22]
FVC post-Bd, % pred.	78.9 ± 10.5
FEV1 post-Bd, % pred.	84.05 ± 12.5
FEV1/FVC post-Bd	81 ± 6.1
Oxygen [number of patients]	4 [10]
Steroids [number of patients]	14 [34.1]
Immunosuppressive treatment [number of patients]	23 [56.1]
Antifibrotic [number of patients]	1 [2.4]
Mortality [number of patients]	2 [4.8]

Bd: bronchodilation; FEV₁: forced expiratory volume in the first second; FVC: forced vital capacity.

. The patients’ mean age was 66 ± 10 years, and 20 were men. Twenty-two patients developed at least 1 severe episode during the follow-up period. No notable distinctions were observed among patients who experienced severe episodes, those who did not have severe episodes, and those who died, regarding age at the point of inclusion, sex, tobacco use, underlying health conditions, or planned medical interventions.

Effects of Pollution on the Long-Term Decline of Non-Idiopathic Pulmonary Fibrosis Interstitial Lung Disease

Regarding air pollution levels, the average pollutant levels during severe episodes affecting 23 [71.77%] patients were SO₂ = 8.90 ± 3.33 μg/m³, NO₂ = 58.13 ± 9.05 μg/m³, CO = 0.41 ± 0.10 mg/m³, O₃ = 41.34 ± 14.60 μg/m³, PM_2.5 = 11.21 ± 2.27 μg/m³, and PM₁₀ = 21.24 ± 5.71 μg/m³ [

Table 4. Average levels of air pollutants during severe and non-severe episodes.

Air Pollutant	Severe Episode [Mean ± SE]	Non-Severe Episode [Mean ± SE]	OR Severe Episode	p-Value
SO2	8.90 ± 3.33	9.51 ± 4.09	0.92 [0.82–1.03]	0.16
CO	0.41 ± 0.10	0.41 ± 0.11	0.99 [0.97–1.05]	0.70
NO2	58.13 ± 9.05	56.97 ± 9.58	0.97 [0.92–1.03]	0.38
O3	41.34 ± 14.60	40.72 ± 16.03	0.97 [0.92–1.01]	0.20
PM2.5	11.21 ± 2.27	11.77 ± 2.74	0.79 [0.54–1.17]	0.24
PM10	21.24 ± 5.71	22.16 ± 6.07	1.1 [0.94–1.28]	0.21

]. In contrast, the average pollutant levels observed during periods when patients did not meet the defined severity criteria were SO₂ = 9.51 ± 4.09 μg/m³, NO₂ = 56.97 ± 9.58 μg/m³, CO = 0.41 ± 0.11 mg/m³, O₃ = 40.72 ± 16.03 μg/m³, PM_2.5 = 11.77 ± 2.74 μg/m³, and PM₁₀ = 22.16 ± 6.07 μg/m³ [Table 4].

Pharmacological treatments were rigorously factored into our statistical analyses, ensuring a meticulous adjustment for medication impact and enabling a focused exploration of the association between urban air pollutants and the long- term decline of non-IPF ILDs.

The odds ratios were [Figure 2] OR SO₂ = 0.92 [0.82–1.03], p = 0.16; OR CO = 0.99 [0.97–1.05], p = 0.70; OR NO₂ = 0.97 [0.92–1.03], p = 0.38; OR PM_2.5 = 0.79 [0.54–1.17], p = 0.24; OR PM₁₀ = 1.1 [0.94–1.28], p = 0.21; and OR O₃ = 0.97 [0.92–1.01], p = 0.20.

During the entire study period, the total average concentration of PM_2.5 was 11.69 μg/m³ ± 0.08. This value surpassed the recommended limits set by the World Health Organization [36].

Similarly, the occurrence of severe episodes did not correlate with an elevated average level of these pollutants [Figure 3].

4. Discussion

The results obtained indicate that the average values of SO₂, CO, NO₂, O₃, PM_2.5, and PM₁₀ did not influence the progression of non-IPF ILDs, as determined by the severity criteria. In this regard, our data demonstrate that air pollution is not associated with the long-term decline of non-IPF ILDs, regardless of smoking habits or treatment with corticosteroids or antifibrotics. Few studies have assessed air pollution in patients with non-IPF ILDs, and no investigations have been found that include diseases as uncommon as LAM or histiocytosis X.

In this regard, only two studies were found that analyzed the impact of pollution on the likelihood of long-term decline in non-IPF ILD in the literature. First, the investigation performed by Roeser et al. [22], who suggested that high levels of O₃ exposure were associated with more severe SSc-ILD at diagnosis and progression at 24 months. Furthermore, the study by Pirozzi et al. [23] did not find a significant association between PM_2.5 and O₃ and a decrease in lung function in patients with fibrotic sarcoidosis.

Those results were somewhat similar to ours, in that Roeser et al. [22] only showed a significant relationship between long-term decline and ozone levels, whereas an association was not demonstrated with the other pollutants. The difference in the ozone outcome could be explained in a few ways. First, our study had a cohort of various ILDs with variable prevalence, which adds complexity to the interpretation of the results, given that they might exhibit a similar progression in some cases but behave differently in others. Second, our definition of long-term decline differs from that determined by Roeser et al. [22] because an absolute decrease was used in lung function according to the guidelines, whereas they employed a relative decrease. Thus, Roeser et al. [22] might have obtained a higher number of severe episodes with relative values and therefore found a significant association with ozone. In addition, although they considered radiological worsening in terms of severity, they did not use the 6MWT or echocardiogram signs, which could provide more information on the progression of this type of lung disease. Lastly, SSc-associated ILD is a connective tissue disease, which has particularities that are different from those of our ILD cohort.

Nevertheless, as with our investigation, Pirozzi et al. [23] found no significant association between air pollution and lung function [23]. The lack of a significant association could be due to their small sample size [16 patients] or because they only considered short-term exposure [up to 14 days of contact with airborne contaminants]. In addition, the different levels of the air pollutants during their study compared with ours could have limited their statistical power.

In contrast, there are other studies reported by Rustler et al. [37] and Singh et al. [38] that did find a significant association between air pollution and the incidence of hypersensitivity pneumonitis or sarcoidosis. These results would have different clinical applications from ours, given that the present study analyzes long-term decline based on parameters that influence the progression of these diseases, whereas their objective of determining an association with incidence will be primarily applied to primary prevention.

This investigation is important because it analyzes the evolution of non-IPF ILDs, which is essential in the follow-up of these patients after their diagnosis. Along these lines, other protocols have been identified that link pollution to the progression of IPF, with results partially consistent with those obtained. Although IPF can progress differently compared with the non-IPF ILDs included in this protocol, it was found useful to review studies that considered the impact of air pollution on IPF. Mariscal-Aguilar et al. [15] demonstrated an association between major air pollutants and chronic respiratory failure and hospitalizations, and Sesé et al. [16] analyzed IPF progression based on lung function tests in a French cohort and found no significant association with concentrations of NO₂, PM_2.5, PM₁₀, or O₃. Similarly, Johannson et al. [18] did not find a relationship between the decline in lung function and an increase in these pollutants. On the other hand, Winterbottom et al. [19] did find a positive impact of PM₁₀-induced pollution on the deterioration of lung function in patients with IPF. Additionally, a Chinese group demonstrated that an increase in NO₂ did affect the progression of lung function in patients with IPF [20].

Our results from this protocol of patients with non-IPF ILDs coincides with the findings of Sesé et al. [16] and Johannson et al. [18], who, like us, did not observe a relationship between pollution and a decline in lung function. This lack of an association could be attributed to their small sample sizes, as was the case in our study.

The studies conducted by Winterbottom et al. [19] and Yoon et al. [20] have discordant results from ours for several reasons. First, these protocols had notably larger sample sizes than ours. Additionally, in both cases there was a higher density of measurement stations and more data collected, thus providing greater statistical power. Furthermore, in the Chinese study, the measured decrease in FVC is relative, whereas in our study the deterioration of lung function in absolute terms was analyzed as indicated in the guidelines [8], making it less likely to find cases with a functional decline.

When comparing our results on non-IPF ILDs with those of IPF studies, caution is warranted because IPF has the poorest prognosis and the most unpredictable behavior in some cases. However, the course of IPF can be similar to other ILDs that develop a fibrotic phenotype, as can occur in the case of sarcoidosis, NSIP, or hypersensitivity pneumonitis. On the other hand, in other studies, lung function has been considered individually as a mortality predictor, whereas in the present study, severity required at least one lung function parameter but also considered clinical, radiological, and echocardiographic variables that are also prognostic factors. Furthermore, our exposure time was the last 90 days previous to death or last visit, whereas the exposure time of the other protocols was very heterogeneous, ranging from 3 weeks to 7 years. The Chinese study, for example, uses pollution prediction models [16,18,19,20].

There are several limitations associated with this study. It was a single-center study, and the sample size was relatively limited due to the low prevalence of these diseases. This sample size imposes constraints on our statistical power, which could affect the robustness of our conclusions regarding the absence of a significant relationship between air pollution levels and the long-term decline of non-IPF ILDs. The small number of participants limits our ability to detect subtle but potentially clinically significant effects of air pollution on the long-term decline of the disease. Furthermore, while the complexity and heterogeneity of non-IPF ILDs, combined with their multifactorial nature, underscore the need for larger, multi-center studies, this limitation is somewhat mitigated because these diseases share a similar natural history in terms of clinical presentation, lung function, and progression to fibrosis, which makes them comparable. On the other hand, the pollution data were exclusively from outdoor air quality monitoring stations, which may not fully represent individual exposure levels, especially for those who spend significant time indoors. Similar to other studies [21,22,23,24,25,26,27,28,29,30,31,32], our research does not include detailed information on air flow direction. However, the absence of clear associations between pollution exposure and health outcomes cannot be attributed solely to the small sample size. The discrepancy between the wide exposure assessment and the detailed disease evaluation complicates data interpretation, suggesting that both a more precise exposure assessment and a closer examination of exposure duration are needed to better understand the impacts of pollution on health. We acknowledge the sample size as a limitation and recommend that future research should involve larger cohorts to validate our findings.

Additionally, the research was susceptible to inaccuracies in categorizing exposure for several reasons. The fluctuating proximity between patients’ residences and the chosen monitoring station could have led to an erroneous estimation of the actual exposure levels. Additionally, our study did not account for potential exposure to air pollution in the workplace or inside the houses, which could hold significant relevance in this context. However, relying on only a single monitoring station might not accurately capture the spatial variability of air pollution exposure, potentially leading to exposure misclassification. Such misclassification could dilute the observed associations between air pollution and long-term decline of the disease, masking potential effects of environmental exposure on non-IPF ILD progression. Furthermore, this approach does not account for individual behaviors, such as time spent indoors versus outdoors, commuting patterns, or occupational exposures, which could significantly influence personal exposure levels. Conversely, there was a lack of data regarding temperature, relative humidity, and seasons, which are important environmental factors to consider. Indeed, although it could be considered a limitation of the study, meteorological variables were not included due to the urban regional climatic variations, such as the microclimate that exists in the city of Madrid, with areas of different altitudes and, therefore, different temperatures. Finally, our study’s retrospective design and the broad window of pollutant exposure assessment represent limitations, particularly when compared with studies assessing short-term lag effects. The variability in lag times, ranging from immediate [1–3 days] to more extended periods [up to 14 days], could underscore the dynamic nature of air pollution’s impact on health outcomes.

Our study also has its strengths. It is the first investigation to analyze the relationship between non-IPF ILDs and air pollution. In fact, some ILDs, such as LAM or histiocytosis X, have never been studied in this way because of their low prevalence. In addition, our definition of long-term decline considered all the variables that are relevant to the progression of these kinds of diseases, thereby offering important clinical applicability.

5. Conclusions

This study examined the association between urban air pollution and the long-term decline of non-idiopathic pulmonary fibrosis interstitial lung diseases [non-IPF ILDs] in a cohort of 41 patients in Madrid. Our findings indicate that there is no significant correlation between the levels of key pollutants—SO₂, CO, NO₂, O₃, PM_2.5, and PM₁₀—and the long-term decline of non-IPF ILDs. Notably, this research is one of the first to explore the impact of various urban pollutants on a diverse group of non-IPF ILD patients, including rare conditions such as lymphangioleiomyomatosis [LAM] and histiocytosis X. While the results suggest that, within the studied cohort and pollutant levels, urban air pollution does not directly influence long-term decline, they highlight the need for further studies with larger sample sizes and comprehensive exposure assessments to fully understand the complex interactions between environmental factors and non-IPF ILD progression.