The effect of industry-related air pollution on pulmonary function: A prospective,cross-sectional comparative study of industrial proximity and smoking status

Abstract

Objective

Ambient air pollution is a leading global environmental health risk, significantly contributing to the burden of chronic respiratory diseases and premature mortality worldwide. Despite being a major industrial and agricultural hub, the specific impact of long-term exposure to urban and industrial pollutants on pulmonary function in the Central Anatolian region remains insufficiently characterized. This study aimed to evaluate the subclinical respiratory effects of air pollution on healthy adults in Konya, Turkey, with a particular focus on the cumulative impact of industrial proximity.

Methods

This prospective, cross-sectional comparative study involving 455 participants collected data on sociodemographic characteristics, smoking history, and respiratory symptoms. Lung function was measured via portable spirometry. Individualized exposure scores were calculated from 1-year and 4-year air quality data. Industrial proximity was categorized using a 10-km threshold for both residence and workplace.

Results

Pulmonary function test values were significantly lower in smoking individuals and in those with respiratory diseases (p < 0.05). Nonsmoking healthy individuals living and working within 10 km of industrial zones showed significantly lower forced expiratory volume in 1 second/forced vital capacity (79.17% vs. 82.71%, p < 0.001) and small airway parameters ( forced expiratory flow between 25% and 75% of vital capacity, maximal expiratory flow at 25% of vital capacity, maximal expiratory flow at 50% of vital capacity, and maximal expiratory flow at 75% of vital capacity; p < 0.005) than in those living farther away. Notably, nonsmoking individuals living near industrial zones had lower small airway flow rates than smoking individuals living far from industrial areas (p < 0.05). The Karatay district, demonstrating the highest levels of particulate matter ≤10 µm in diameter, showed the lowest maximal expiratory flow at 25% of vital capacity values (p = 0.049).

Conclusions

Industrial proximity and smoking are independent risk factors for airway obstruction. Continuous industrial exposure may cause more pronounced small airway damage than tobacco consumption, thereby highlighting the need for air quality management in urban planning.

Keywords

Ambient air pollution cigarette smoking small airway dysfunction particulate matter pulmonary function tests maximal expiratory flow at 25% of vital capacity industrial exposure

Introduction

Air pollution as a global public health concern

Air pollution is defined as the presence of foreign substances in the atmosphere in concentrations and durations that can harm human health and disrupt ecological balance.¹ As a major global public health concern, air pollution acts as both a primary cause and an exacerbating factor for respiratory diseases such as chronic obstructive pulmonary disease (COPD), asthma, and lung cancer. It is associated with declined pulmonary function, increased infection rates, and higher respiratory mortality.² According to the Global Burden of Disease study, ambient and household air pollution rank as the fourth leading risk factor for mortality worldwide, following hypertension, smoking, and dietary factors.³ In lung diseases caused by air pollution, spirometry measurements can assess the effects on respiratory function values.⁴

Industrial emissions and respiratory health

Rapid urbanization and industrial revolution in recent decades have increased environmental pollution to hazardous levels.⁵ The World Health Organization (WHO) has categorized air pollution as a “silent killer,” estimating approximately 7 million deaths annually.⁶ Industrial emissions contribute significantly to this burden. Paolocci et al.⁷ demonstrated that residents living in close proximity to industrial zones in Central Italy exhibited a higher prevalence of respiratory symptoms and impaired health status, thereby reinforcing the need for targeted clinical assessments in industrial hubs.

Key indicators of air quality include particulate matter (PM), ozone (O₃), nitrogen dioxide (NO₂), and sulfur dioxide (SO₂). Although PM ≤10 µm in diameter (PM₁₀) can penetrate the conducting airways, PM ≤2.5 µm in diameter (PM_2.5) can accumulate in the gas-exchange regions of the lungs, resulting in significant alterations in pulmonary function test (PFT) parameters.^4,8 Studies in healthy individuals are limited; however, evidence suggests that both acute and long-term exposure to ambient air pollution is associated with reduced forced expiratory volume in 1 second (FEV₁) and FEV₁/forced vital capacity (FVC) values.^4,9

Impact of air pollution on lung function and small airway dysfunction (SAD)

The global impact of ambient air pollution on adult respiratory health has been reaffirmed by a recent large-scale systematic review and meta-analysis. Gross et al.¹⁰ synthesized data from high-quality studies up to 2024 and provided high-certainty evidence that long-term exposure to NO₂ and PM is significantly associated with lower FEV₁ and FVC, underscoring air pollution as a primary modifiable risk factor for premature decline in lung function.

Recent studies have also highlighted the combined effect of pollutants (NO₂, O₃, PM_2.5, SO₂, and carbon monoxide (CO)) on SAD, typically defined as forced expiratory flow (FEF) between 25% and 75% of vital capacity (FEF_25–75) <65%. Notably, this association remains significant independent of smoking status.¹¹

Air pollution as a predominant modifiable risk factor

According to recent global estimates, PM air pollution is associated with approximately 4.7 million premature deaths per year, ranking as a higher modifiable risk factor than smoking and high fasting plasma glucose in terms of disability-adjusted life-years.¹² This health risk assessment was further elaborated by Madani et al.,¹³ who highlighted the critical impact of PM_2.5, NO₂, and black carbon exposure on adults, demonstrating that environmental pollutants often outweigh traditional social risk factors in predicting adverse health outcomes in densely populated urban centers.

Geographic and industrial significance of the study site

Konya was particularly selected as the study site due to its industrial and geographic significance. As one of Turkey’s primary industrial hubs, it hosts 12 organized industrial zones with a high concentration of metal casting, automotive supply, and machinery manufacturing. Furthermore, its location within a closed basin on the Central Anatolian plateau (altitude: approximately 1016 m) often results in temperature inversions that prevent the dispersion of industrial and traffic-related emissions, particularly PM₁₀ and nitrogen oxides (NOx). This unique combination of high-density industrial activity and atmospheric stagnation creates a substantial environmental burden, thereby justifying the city’s selection for assessing long-term pollution exposure.

Knowledge gaps and study aims

Ambient air pollution is a leading global environmental health risk, contributing significantly to the burden of chronic respiratory diseases and premature mortality worldwide. Despite these well-documented health impacts, several critical knowledge gaps remain. First, although cigarette smoking is universally recognized as a primary modifiable risk factor for lung function decline, the comparative impact of continuous “passive” industrial exposure versus intermittent active tobacco use has not been sufficiently elucidated in clinical research. Most studies evaluate these factors in isolation, leaving a gap in understanding their relative contributions to airway obstruction. Second, there is a significant lack of objective respiratory data from unique geographic basins such as Central Anatolia. Although Konya is a major industrial hub with specific atmospheric conditions, including thermal inversions that facilitate pollutant stagnation, its impact on public health burden has not been extensively characterized using sensitive, subclinical markers of SAD. Existing literature often overlooks the “silent zone” of the lungs, focusing instead on primary parameters such as FEV₁ and FVC, which may remain within normal limits even when significant distal airway damage (maximal expiratory flow (MEF) at 25% (MEF₂₅) and (FEF_25–75) is occurring.

This study aimed to address these gaps by evaluating the impact of short- and long-term (1-year and 4-year) air quality data on PFT values across three central districts in Konya. Importantly, it provides a novel comparative analysis of industrial proximity and cigarette smoking to determine whether unavoidable environmental exposure can induce respiratory impairment comparable with, or exceeding, the effects of active tobacco use. By focusing on high-risk geographic zones and standardized pulmonary assessments, this study sought to provide a benchmark for urban planning and public health strategies in similar industrial regions.

Patients and methods

Study population and sampling strategy

This cross-sectional study was conducted between 1 January 2025 and 31 December 2025 in the three central districts of Konya, Turkey (Selçuklu, Karatay, and Meram). Participants aged 18–80 years were randomly selected from these districts, with sample sizes determined proportionally to population density based on 2023 data from Turkish Statistical Institute (TUIK). At the end of 2023, the population of Selçuklu was 693,681 (219 participants), Karatay was 358,558 (112 participants), and Meram was 382,726 (124 participants). The primary contributors to ambient PM in the study area included high-emission sectors concentrated in the organized industrial zones of Selçuklu and Karatay. These industries predominantly comprise automotive supply manufacturing, metal casting (foundries), agricultural machinery production, and chemical processing plants, all of which are associated with significant dust generation and combustion-related pollutant emissions.

The minimum sample size was calculated using Cochran’s formula for large populations: n = [Z² · p · (1 − p)]/e². With a 95% confidence level (Z = 1.96), an estimated proportion (p) of 0.5, and a 5% margin of error (e = 0.05), the required sample size was determined to be 384. To account for potential data loss and spirometry maneuver failures, 500 individuals were targeted initially. After excluding 45 participants who did not meet the American Thoracic Society (ATS) and European Respiratory Society (ERS) quality standards, the final cohort of 455 participants remained. This sample size was well above the statistically required threshold, thereby ensuring adequate power for the comparative analyses.

The study was performed in accordance with the ethical principles for medical research involving human participants outlined in the Declaration of Helsinki of 1975, as revised in 2024. Ethical approval was obtained from the Necmettin Erbakan University Non-Pharmaceutical and Non-Medical Device Research Ethics Committee (location: Konya, Turkey; Decision No: 2022-3921; Date: 22 July 2022). All participant data were fully deidentified prior to analysis to ensure strict confidentiality and to prevent potential identification of individuals, in accordance with ethical standards and institutional policies. All participants provided written informed consent before participation. A face-to-face questionnaire covering sociodemographic characteristics, medical history, respiratory symptoms, smoking status, and daily time spent at home and work was administered to volunteers in industrial zones, shopping centers, and public events. All researchers (resident physicians) underwent standardized training prior to the study to ensure high data consistency and measurement quality during spirometric assessments.

The reporting of this study conforms to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines for cross-sectional studies.¹⁴

Inclusion and exclusion criteria

Inclusion criteria

The inclusion criteria were as follows: (a) adults aged 18–80 years; (b) individuals living or working in the central districts of Konya (Selçuklu, Karatay, or Meram) for at least 1 year; and (c) individuals volunteering to participate and providing written informed consent.

Exclusion criteria

The exclusion criteria were as follows: (a) inability to cooperate with spirometry maneuvers; (b) failure to meet the ATS and ERS quality standards for spirometric measurements; and (c). individuals with physical or cognitive impairments that prevent completion of the face-to-face questionnaire.

Questionnaire design, administration, and quality control

The study questionnaire was adapted from the standardized American Thoracic Society–Division of Lung Diseases 1978 (ATS-DLD-78) respiratory disease questionnaire. To ensure linguistic and cultural validity for the Turkish population, a translation and back-translation process was performed by independent experts. A pilot study involving 30 participants was conducted to refine the questions for clarity and relevance. The internal consistency of the final questionnaire was evaluated using Cronbach’s alpha, which was found to be 0.84.

To ensure data integrity and minimize missing responses, the questionnaires were administered face-to-face at each study site by a dedicated team of two physicians. Following the interview, each questionnaire was immediately cross-checked by a second independent researcher for completeness and accuracy. Incomplete or inconsistent responses were addressed in real-time while the participant was still on site. This rigorous verification process ensured an approximately 100% completion rate for primary variables among the final cohort. Artificial intelligence (AI)–assisted language tools were used solely for linguistic refinement during the manuscript writing phase, with no involvement in the research methodology or data analysis.

Air quality data collection

Air quality parameters, including SO₂, PM₁₀, nitrogen monoxide (NO), NO₂, nitrogen oxides (NOx), and CO, were obtained from the National Air Quality Monitoring Network (UHKİA). Data were collected from six fixed monitoring stations across the three central districts (two per district). These stations employ internationally recognized automated measurement techniques: beta-attenuation monitoring (BAM) for PM₁₀, ultraviolet (UV) fluorescence for SO₂, chemiluminescence for NOx, and nondispersive infrared (NDIR) spectroscopy for CO. Air quality was monitored continuously with a sampling frequency of 1 h. To ensure the robustness of the environmental exposure analysis, only validated hourly records from stations with a data recovery rate exceeding 90% during the study period were included.

Mean values for the preceding 1 year (2025) and 4 years (2022–2025) were calculated from these validated records (air quality data access address: (https://sim.csb.gov.tr/STN/STN_Report/StationDataDownloadNew)).¹⁵

Individualized exposure score (IES) calculation

To determine personal exposure levels based on home and workplace locations, IES was calculated using a time-weighted average formula for each pollutant:

IES = ((Hours home \times Conc home) + (Hours work \times Conc work)) / (Hours home + Hours work)

where, Conc refers to mean pollutant concentration in the respective district, and hours indicate the time spent daily at home or work.

Participants were also categorized by their proximity to industrial zones using a 10-km threshold. The high exposure group comprised individuals living and working within 10 km of an industrial zone, whereas the low exposure group included individuals living and working more than 10 km away.

PFT

PFTs were performed using a personal computer (PC)–based digital turbine spirometer (MicroQuark, COSMED; Rome, Italy). The device uses a bidirectional digital turbine, which is factory-calibrated and designed for high stability. To ensure measurement accuracy and compliance with ATS/ERS 2019 standards, a volume verification check was performed at the beginning of each testing day using a 3-L calibration syringe. The device was considered accurate when the measured volume was within ±3% of the target. To ensure standardized and high-quality data collection, all PFTs were conducted by a dedicated team of two trained research assistant physicians at each study site. Following each session, the spirometric maneuvers were reviewed post-collection by a secondary senior researcher for adherence to strict ATS/ERS 2019 acceptability and repeatability criteria. Participants who could not achieve at least three acceptable maneuvers (n = 45) were excluded from the study to maintain the integrity of the clinical data.

Measured parameters included FVC, FEV₁, FEV₁/FVC ratio, FEF_25–50, FEF_50–75, FEF_25–75, PEF, and MEF at 25%, 50%, and 75% of FVC.

Early airway changes occur most commonly in the peripheral regions of the lungs, particularly at the level of the terminal bronchioles. Along the expiratory curve, flow measurements can be performed at different intervals reflecting airflow in various airways. These include MEF when 75% of FVC remains (MEF₇₅), when 50% remains (MEF₅₀), when 25% remains (MEF₂₅), and the maximal mid-expiratory flow (MMEF, the value between 75% and 25% of FVC).¹⁶

Key measurements for obstructive airway disorders include FEV₁, FVC, and the FEV₁/FVC ratio, alongside MEF values at 75%, 50%, and 25% of exhalation. The MEF value obtained from the flow–volume curve is expressed in liters per second. An isolated decrease in MEF₂₅ specifically indicates small airway obstruction, as the final 25% of vital capacity originates from the most distal airways (bronchioles). This characteristic makes MEF₂₅ one of the most useful parameters for diagnosing lung conditions associated with SAD.¹⁷

Assessment of air pollutant effects and comparison groups

The distribution of spirometric results, sociodemographic characteristics, medical history, and respiratory symptoms (dyspnea, cough, and phlegm) was determined for participants across the three central districts. PFT values were compared between participants with and without respiratory diseases, as well as between smoking and nonsmoking individuals. Correlations between the amount of smoking and PFT parameters were analyzed.

To evaluate the impact of industrial air pollution, PFT values were compared between individuals living or working within 10 km of an industrial zone and those living or working farther away. These proximity-based comparisons were further analyzed within subgroups of healthy, smoking, and nonsmoking individuals. Correlations between the IES of air pollutants and PFT parameters were also examined. Notably, a specific comparative analysis was performed between the nonsmoking individuals living near industrial zones and the smoking individuals living far from industrial areas to assess the relative impact of environmental exposure versus tobacco use.

Statistical analysis

Data were analyzed using Statistical Package for the Social Sciences (SPSS) version 22.0 (IBM Corp.; Armonk, NY, USA). Descriptive statistics were reported as counts and percentages for categorical variables, whereas continuous variables were presented as mean ± SD and 95% confidence intervals (CI) to provide a more robust estimation of population parameters. The normality of data distribution was evaluated using the Shapiro–Wilk test and visual inspection of Q–Q plots. For parametric tests, homogeneity of variances was assessed using Levene’s test.

Comparisons between independent groups were performed using the unpaired Student’s t-test when normality and variance assumptions were met; alternatively, the Mann–Whitney U test was applied. For comparisons of three or more groups, one-way analysis of variance (ANOVA) was applied, followed by post hoc Tukey honestly significant difference (HSD) tests for pairwise comparisons after confirming variance homogeneity. Relationships between variables were analyzed using Pearson correlation for normally distributed data and Spearman’s rho for nonparametric correlations. The robustness of the linear regression models was confirmed by assessing the linearity of residuals, checking for homoscedasticity, and evaluating multicollinearity using the variance inflation factor (VIF < 10). A p-value <0.05 was considered statistically significant.

To minimize the impact of potential confounding factors, several strategies were employed. For primary parameters such as FEV₁ and FVC, both absolute measured values (L) and “percent predicted” values (based on global lung function equations) were analyzed to provide a comprehensive assessment of pulmonary capacity and obstruction. For flow-rate parameters, including MEF₂₅ and FEF_25–75, absolute measured values (L/s) were used to capture precise subclinical alterations in the small airways. The inherent influence of biological determinants such as age, sex, and height on these raw measurements was controlled by performing subgroup analyses specifically in healthy never-smoking individuals, thereby effectively isolating the independent impact of industrial proximity. Furthermore, socioeconomic status and education were relatively homogeneous, with 54.9% of participants being university graduates and 95.8% using natural gas for heating, which significantly reduced the confounding influence of differing indoor biomass exposure.

Results

Air quality analysis of study districts

Analysis of the mean air quality data for the central districts of Konya over 1-year and 4-year periods revealed that only the Karatay district fell within the “moderate” category according to National Air Quality Index thresholds, whereas all other parameters remained within “good” limits.¹⁸ A comparison with WHO guidelines indicated that NO₂ levels exceeded the final target (10 μg/m³) in all districts. Although NO₂ levels slightly exceeded the Interim Target-1 (40 μg/m³) in Meram, they remained lower in Selçuklu and Karatay. Similarly, PM₁₀ levels across all districts were above the final target of 15 μg/m³, with only Karatay exceeding the Interim Target-1 of 70 μg/m³ (Table 1).¹⁹

Table 1.

Average air pollutant concentrations in the central districts of Konya (1-year vs. 4-year means).

District	Period	SO₂	NO₂	NO	NO_x	CO	PM₁₀
Selçuklu	4-year mean	8.00	34.71	17.68	52.43	437.57	39.70
	1-year mean	7.45	32.44	12.41	44.85	503.46	40.68
Karatay	4-year mean	13.25	38.56	25.35	64.31	801.92	71.07*
	1-year mean	11.65	39.06	28.96	67.57	796.46	75.15*
Meram	4-year mean	12.36	42.05	23.57	65.59	683.37	40.11
	1-year mean	9.86	44.72	28.73	73.48	567.69	42.80

Values are expressed as mean concentrations in μg/m³. *Significant elevations above the WHO Interim Target-1 (70 μg/m³ for PM₁₀) are indicated in bold. Air quality levels were categorized according to the National Air Quality Index and WHO 2021 guidelines.

CO: carbon monoxide; NO: nitrogen monoxide; NO₂: nitrogen dioxide; NO_x: nitrogen oxides; PM₁₀: particulate matter; SO₂: sulfur dioxide; WHO: World Health Organization.

Participant characteristics, environmental factors, and respiratory symptoms

The mean age of the participants was 38.9 ± 10.76 years. The study population consisted of 260 women (57.1%) and 195 men (42.9%), with 70.5% (n = 321) being married. Regarding educational background, more than half of the participants (54.9%, n = 250) were university graduates, followed by primary school graduates (19.1%, n = 87). Analysis of lifestyle and medical history revealed that 25.7% (n = 117) were actively smoking individuals, whereas 74.3% (n = 338) were nonsmoking individuals. In terms of physical activity, 58.0% (n = 264) exercised less than once a week, and only 11.6% (n = 53) exercised more than three times weekly. Most participants (91%, n = 414) had no chronic respiratory disease, whereas 7.7% (n = 35) were diagnosed with asthma and 1.3% (n = 6) with COPD. In terms of environmental factors, 95.8% (n = 436) used natural gas for heating, and 10.3% (n = 47) owned pets. The most prevalent respiratory symptom was dyspnea (23.3%, n = 106), followed by cough (18.9%, n = 86) and phlegm production (17.4%, n = 79). Regarding industrial proximity, 55.4% (n = 252) lived and 58.5% (n = 266) worked within 10 km of an industrial zone (Table 2).

Table 2.

Sociodemographic characteristics, environmental factors, and respiratory symptoms of the study population (n = 455).

Variable	n (%) or mean ± SD (95% CI)
Age (years)	38.9 ± 10.76 (95% CI: 37.91–39.89)
Sex
Female	260 (57.1%)
Male	195 (42.9%)
Smoking status
Yes	117 (25.7%)
No	338 (74.3%)
Smoking amount (pack-years)	22.99 ± 16.75 (95% CI: 19.96–26.02)
Marital status
Married	321 (70.5%)
Single	134 (29.5%)
Educational status
Illiterate	3 (0.7%)
Primary school	87 (19.1%)
Middle school	30 (6.6%)
High school	82 (18.0%)
University	250 (54.9%)
Postgraduate	3 (0.7%)
Physical activity
<1 day per week	264 (58.0%)
1–3 days per week	138 (30.3%)
>3 days per week	53 (11.6%)
Chronic respiratory disease
None	414 (91.0%)
Asthma	35 (7.7%)
COPD	6 (1.3%)
Heating method
Natural gas	436 (95.8%)
Stove (coal/wood)	18 (4.0%)
Electric	1 (0.2%)
Pet ownership (yes)	47 (10.3%)
Respiratory symptoms
Dyspnea	106 (23.3%)
Cough	86 (18.9%)
Phlegm	79 (17.4%)
District (home/work)
Selçuklu	219 (48.1%)/220 (48.4%)
Karatay	112 (24.6%)/113 (24.8%)
Meram	124 (27.3%)/122 (26.8%)
Daily time allocation (hours)
At home	15.37 ± 3.42 (95% CI: 15.06–15.68)
At workplace	8.63 ± 2.85 (95% CI: 8.37–8.89)
Industrial proximity (<10 km)
Home location	252 (55.4%)
Workplace location	266 (58.5%)

Values are presented as n (%) for categorical variables and mean ± SD (95% CI) for continuous variables. Pack-years were calculated as the number of cigarette packs smoked per day multiplied by the total years of smoking. Chronic respiratory disease status (asthma/COPD) and respiratory symptoms (dyspnea, cough, and phlegm) were based on participant self-report of a prior physician diagnosis and a standardized questionnaire, respectively. District reflects both residential (home) and workplace locations.

COPD: chronic obstructive pulmonary disease; CI: confidence interval.

Impact of regional residence on respiratory functions

Participants whose residential and workplace addresses were located within the same district were analyzed across three groups: Selçuklu (n = 155), Karatay (n = 72), and Meram (n = 69). No statistically significant differences were observed between the groups for primary PFT parameters, including FVC, FEV₁, FEV₁/FVC ratio, PEF, and FEF_25–75 (p > 0.05).

However, a statistically significant difference was observed in MEF₂₅ (L/s), a parameter reflecting flow in the distal airways (p = 0.049). Post hoc pairwise comparisons revealed that the mean MEF₂₅ value in the Karatay group (1.41 ± 0.65 L/s) was significantly lower than that in the Meram group (1.75 ± 0.88 L/s). No significant differences were found between the Selçuklu group and the other two districts with respect to MEF₂₅ levels (Table 3).

Table 3.

Comparison of PFT parameters among participants living and working in the same central district (n = 296).

PFT parameters	Selçuklu (n = 155)	Karatay (n = 72)	Meram (n = 69)	p-value
FVC (L)	4.01 ± 1.09	4.08 ± 1.16	4.20 ± 1.10	0.500
FVC %	90.04 ± 12.64	92.01 ± 13.31	93.22 ± 12.94	0.200
FEV₁ (L)	3.22 ± 0.92	3.23 ± 0.90	3.37 ± 0.93	0.529
FEV₁ %	89.32 ± 13.64	91.28 ± 14.04	91.42 ± 12.89	0.440
FEV₁/FVC %	80.25 ± 7.27	79.61 ± 7.26	79.98 ± 7.46	0.823
FEF_25–50 (L/s)	4.98 ± 1.84	4.92 ± 1.75	4.88 ± 1.72	0.923
FEF_50–75 (L/s)	2.53 ± 1.16	2.38 ± 0.93	2.70 ± 1.11	0.226
FEF_25–75 (L/s)	3.30 ± 1.37	3.13 ± 1.11	3.41 ± 1.25	0.424
PEF (L/s)	6.70 ± 2.16	6.96 ± 2.27	6.63 ± 2.46	0.664
MEF₂₅ (L/s)	1.56 ± 0.84	1.41 ± 0.65*	1.75 ± 0.88*	0.049*
MEF₅₀ (L/s)	3.96 ± 1.56	3.90 ± 1.31	3.96 ± 1.38	0.960
MEF₇₅ (L/s)	5.96 ± 2.17	5.66 ± 2.05	5.76 ± 2.22	0.580

Values are presented as mean ± SD. *Bold indicates a statistically significant difference (p < 0.05). Absolute values are expressed in liters (L) or liters/second (L/s); percentage values (%) reflect percent predicted.

FEV₁: forced expiratory volume in 1 second; FVC: forced vital capacity; PEF: peak expiratory flow; PFT: pulmonary function test; FEF_25–50: forced expiratory flow between 25% and 50% of vital capacity; FEF_50–75: forced expiratory flow between 50% and 75% of vital capacity; FEF_25–75: forced expiratory flow between 25% and 75% of vital capacity; MEF₂₅: maximal expiratory flow at 25% of vital capacity; MEF₅₀: maximal expiratory flow at 50% of vital capacity; MEF₇₅: maximal expiratory flow at 75% of vital capacity.

Association between respiratory diseases and PFT parameters

Significant differences were observed across multiple PFT parameters when participants with and without chronic respiratory diseases were compared.

FVC values

The mean FVC (L) in participants with respiratory diseases (3.79 ± 1.16) was significantly lower than in healthy participants (4.24 ± 1.06; p = 0.010). Similarly, FVC% predicted was significantly reduced in patients with respiratory diseases compared with the healthy cohort (88.09 ± 13.23 vs. 92.90 ± 12.12, respectively; p = 0.017).

FEV₁ values

Both FEV₁ (L) and FEV₁% predicted were significantly lower in participants with respiratory diseases than in those without (2.93 ± 0.98 vs. 3.40 ± 0.88; p = 0.001 and 84.92 ± 15.34 vs. 91.91 ± 12.93; p = 0.001, respectively).

Small airway flow rates

All parameters reflecting small airway function, including FEF_50–75 (p = 0.001), FEF_25–75 (p = 0.001), MEF₂₅ (p = 0.001), MEF₅₀ (p = 0.002), and MEF₇₅ (p = 0.025), were found to be statistically significantly lower in participants with respiratory diseases (Table 4).

Table 4.

Comparison of PFT parameters based on the presence of chronic respiratory disease (n = 455).

PFT parameters	No respiratory disease (n = 414) mean ± SD	Respiratory disease (n = 41) mean ± SD	p-value
FVC (L)	4.25 ± 1.06	3.79 ± 1.16	0.010
FVC %	92.91 ± 12.12	88.10 ± 13.23	0.017
FEV₁ (L)	3.41 ± 0.88	2.93 ± 0.98	0.001
FEV₁ %	91.91 ± 12.93	84.93 ± 15.34	0.001
FEV₁/FVC %	80.33 ± 7.61	77.72 ± 9.13	0.085
FEF_25–50 (L/s)	5.39 ± 5.90	4.28 ± 1.97	0.234
FEF_50–75 (L/s)	2.67 ± 1.09	2.06 ± 1.10	0.001
FEF_25–75 (L/s)	3.45 ± 1.26	2.74 ± 1.38	0.001
PEF (L/s)	6.94 ± 2.28	6.66 ± 2.20	0.487
MEF₂₅ (L/s)	1.66 ± 0.81	1.21 ± 0.72	0.001
MEF₅₀ (L/s)	4.12 ± 1.46	3.37 ± 1.70	0.002
MEF₇₅ (L/s)	6.07 ± 2.15	5.27 ± 2.21	0.025

Values are presented as mean ± SD. Chronic respiratory disease status (asthma/COPD) is based on self-reported physician diagnosis. A p-value <0.05 is considered statistically significant (shown in bold).

Impact of smoking on PFT parameters in healthy participants

Healthy participants without chronic respiratory disease (n = 414) were categorized by smoking status to evaluate the independent effects of tobacco use on pulmonary function.

FEV₁ and ratios

In healthy individuals who smoke, FEV₁% predicted values (89.45 ± 12.71) were significantly lower than in nonsmoking individuals (92.51 ± 12.78; p = 0.038). Notably, the FEV₁/FVC ratio showed a highly significant decrease in smoking individuals (77.83 ± 7.17) compared with nonsmoking individuals (81.02 ± 7.55; p < 0.001).

Peripheral airway functions

The adverse impact of smoking on small airways was evidenced by significant reductions in FEF_50–75 (p = 0.047) and MEF₂₅ (p = 0.018) parameters (Table 5).

Table 5.

Comparison of PFT parameters in healthy participants according to smoking status (n = 414).

PFT parameters	Smoking healthy individuals (n = 99) mean ± SD	Nonsmoking healthy individuals (n = 315) mean ± SD	p-value
FVC (L)	4.42 ± 0.97	4.28 ± 1.07	0.052
FVC %	92.41 ± 11.39	92.97 ± 12.37	0.693
FEV₁ (L)	3.44 ± 0.80	3.38 ± 0.89	0.522
FEV₁ %	89.45 ± 12.71	92.51 ± 12.78	0.038
FEV₁/FVC %	77.84 ± 7.17	81.03 ± 7.55	<0.001
FEF_25–50 (L/s)	5.08 ± 1.58	5.49 ± 6.74	0.547
FEF_50–75 (L/s)	2.48 ± 1.01	2.73 ± 1.10	0.047
FEF_25–75 (L/s)	3.29 ± 1.18	3.50 ± 1.28	0.152
PEF (L/s)	7.21 ± 2.14	6.86 ± 2.32	0.222
MEF₂₅ (L/s)	1.49 ± 0.71	1.71 ± 0.83	0.018
MEF₅₀ (L/s)	3.95 ± 1.32	4.18 ± 1.48	0.178
MEF₇₅ (L/s)	6.27 ± 2.02	6.03 ± 2.18	0.332

Values are presented as mean ± SD. Smoking status is based on participant self-report. A p-value <0.05 is considered statistically significant (shown in bold).

Correlation between smoking intensity (pack-years) and PFT parameters

The relationship between smoking intensity (pack-years) and pulmonary function parameters was analyzed in participants without chronic respiratory disease (n = 414).

FEV₁/FVC ratio and FEV₁

A significant negative correlation was observed between smoking pack-years and the FEV₁/FVC ratio (r = −0.218, p < 0.001), indicating that increased tobacco consumption is associated with a higher risk of airway obstruction. Additionally, a weak but significant negative correlation was observed between FEV₁% predicted values and smoking intensity (r = −0.121, p = 0.007).

Small airway functions

Increased smoking intensity was also significantly and negatively correlated with parameters representing the small airways, particularly MEF₂₅ (r = −0.124, p = 0.006) and FEF_50–75 (r = −0.097, p = 0.024) (Table 6).

Table 6.

Correlation between PFT parameters and smoking intensity (pack-years).

PFT parameters (n = 414)	p-value	r
FVC (L)	0.050	0.081
FVC %	0.234	−0.036
FEV₁ (L)	0.314	0.024
FEV₁ %	0.007	−0.121**
FEV₁/FVC %	0.000	−0.218**
FEF_25–50 (L/s)	0.489	−0.001
FEF_50–75 (L/s)	0.024	−0.097*
FEF_25–75 (L/s)	0.091	−0.066
PEF (L/s)	0.151	0.056
MEF₂₅ (L/s)	0.006	−0.124**
MEF₅₀ (L/s)	0.209	−0.040
MEF₇₅ (L/s)	0.154	0.050

r: Spearman correlation coefficient. *Bold correlation is significant at the 0.05 level; **Bold correlation is significant at the 0.01 level. Pack-years were calculated as (packs/day) × years.

Cumulative impact of residential and workplace proximity to industrial zones on PFT parameters

The cumulative effect of proximity to industrial zones was investigated by comparing participants with “high exposure” (both residence and workplace <10 km from industrial zones) and “low exposure” (both locations >10 km). Statistically robust differences were identified between the two groups.

Airway obstruction parameters

Participants in the high exposure group exhibited significantly lower FEV₁/FVC ratios (78.37 ± 8.53) than in those in the low exposure group (82.28 ±6.67; p < 0.001). Similarly, a significant decline in FEV₁ (L) values was observed in the high exposure group (p = 0.002).

Small airway function

Proximity to industrial zones was associated with statistically significant impairment across all small airway parameters. In the high exposure group, FEF_25–75 (p < 0.001), FEF_50–75 (p < 0.001), FEF_25–50 (p < 0.001), MEF₂₅ (p < 0.001), MEF₅₀ (p < 0.001), and MEF₇₅ (p = 0.001) values were significantly lower than those in the low exposure group (Table 7).

Table 7.

Comparison of PFT parameters based on cumulative proximity (both home and work) to industrial zones (n = 351).

PFT parameters	High exposure (<10 km) mean ± SD n = 207	Low exposure (>10 km) mean ± SD n = 144	p-value
FVC (L)	4.11 ± 1.06	4.29 ± 1.10	0.133
FVC %	92.37 ± 12.71	92.18 ± 12.04	0.887
FEV₁ (L)	3.21 ± 0.86	3.52 ± 0.93	0.002
FEV₁ %	90.23 ± 14.24	92.19 ± 12.58	0.183
FEV₁/FVC %	78.37 ± 8.53	82.28 ± 6.67	<0.001
FEF_25–50 (L/s)	4.62 ± 1.63	5.46 ± 1.89	<0.001
FEF_50–75 (L/s)	2.35 ± 1.00	2.95 ± 1.07	<0.001
FEF_25–75 (L/s)	3.06 ± 1.18	3.77 ± 1.28	<0.001
PEF (L/s)	6.66 ± 2.10	7.05 ± 2.58	0.185
MEF₂₅ (L/s)	1.42 ± 0.72	1.87 ± 0.81	<0.001
MEF₅₀ (L/s)	3.73 ± 1.41	4.41 ± 1.51	<0.001
MEF₇₅ (L/s)	5.5681 ± 2.00	6.42 ± 2.38	0.001

Values are presented as mean ± SD. High exposure: participants living and working within 10 km of an industrial zone; low exposure: participants living and working more than 10 km away. A p-value <0.05 is considered statistically significant (shown in bold).

Impact of proximity to industrial zones on PFT parameters in healthy participants

When healthy participants without chronic respiratory disease were analyzed based on their distance from industrial zones, exposure to industrial air pollution was found to significantly restrict pulmonary function.

Airway patency and capacity

Among healthy individuals, those in the high exposure group (both residence and workplace <10 km), had significantly lower FEV₁ (L) values (3.27 ± 0.82) than in those in the low exposure group (3.53 ± 0.92; p = 0.007). Notably, the FEV₁/FVC ratio, one of the most sensitive indicators of airway obstruction, was significant reduced in the high exposure group compared with the low exposure group (78.71 ± 8.34 vs. 82.21 ± 6.57; p < 0.001).

Small airway performance

The most prominent effect of industrial air pollution on healthy lungs was observed in the small airways. In the high exposure healthy group, all parameters, including FEF_25–75 (p < 0.001), FEF_50–75 (p < 0.001), FEF_25–50 (p < 0.001), MEF₂₅ (p < 0.001), MEF₅₀ (p < 0.001), and MEF₇₅ (p = 0.003), were statistically significantly lower than those in healthy individuals living farther from industrial zones (Table 8).

Table 8.

Comparison of PFT parameters in healthy participants based on cumulative proximity to industrial zones (n = 321).

PFT parameters	Healthy—high exposure (<10 km) mean ± SD (n = 185)	Healthy—low exposure (>10 km) mean ± SD (n = 136)	p-value
FVC (L)	4.17 ± 1.02	4.31 ± 1.10	0.270
FVC %	93.18 ± 12.22	92.30 ± 12.19	0.525
FEV₁ (L)	3.27 ± 0.82	3.54 ± 0.92	0.007*
FEV₁ %	91.29 ± 13.52	92.32 ± 12.61	0.488
FEV₁/FVC %	78.71 ± 8.34	82.21 ± 6.57	<0.001*
FEF_25–50 (L/s)	4.74 ± 1.55	5.47 ± 1.90	<0.001*
FEF_50–75 (L/s)	2.42 ± 0.97	2.97 ± 1.06	<0.001*
FEF_25–75 (L/s)	3.15 ± 1.14	3.78 ± 1.27	<0.001*
PEF (L/s)	6.71 ± 2.11	7.10 ± 2.61	0.210
MEF₂₅ (L/s)	1.48 ± 0.72	1.88 ± 0.80	<0.001*
MEF₅₀ (L/s)	3.84 ± 1.34	4.41 ± 1.51	<0.001*
MEF₇₅ (L/s)	5.68 ± 1.95	6.45 ± 2.40	0.003*

Values are presented as mean ± SD. This analysis is restricted to participants without chronic respiratory disease. High exposure: <10 km; low exposure: >10 km from industrial zones. *Bold indicates statistical significance (p < 0.05).

Impact of industrial exposure on healthy never-smoking individuals

Participants without chronic respiratory disease who had never smoked (n = 248) were analyzed based on their proximity to industrial zones. This subgroup analysis aimed to isolate environmental exposure as an independent risk factor for pulmonary dysfunction.

Airway obstruction and capacity

Among nonsmoking healthy individuals, those in the high exposure group (both residence and workplace <10 km) had significantly lower FEV₁ (L) values (3.24 ± 0.79) than in those in the low exposure group (3.53 ± 0.93; p = 0.009). The FEV₁/FVC ratio, a key indicator of airway obstruction, was significant reduced in the group living near industrial zones compared with the low exposure group (79.17 ± 8.26 vs. 82.71 ± 6.35; p < 0.001).

Small airway functions

Despite the absence of smoking and chronic disease, industrial exposure was associated with marked reductions across all small airway parameters. In the high exposure group, FEF_25–75 (p < 0.001), FEF_50–75 (p < 0.001), FEF_25–50 (p < 0.001), MEF₂₅ (p < 0.001), MEF₅₀ (p = 0.002), and MEF₇₅ (p = 0.002) values were statistically significantly lower than those in the low exposure group (Table 9).

Table 9.

Comparison of PFT parameters in never-smoking healthy individuals based on cumulative proximity to industrial zones (n = 248).

PFT parameters	High exposure (<10 km) (n = 133) mean ± SD	Low exposure (>10 km) (n = 115) mean ± SD	p-value
FVC (L)	4.12 ± 1.00	4.28 ± 1.11	0.223
FVC %	93.71 ± 12.22	92.43 ± 12.47	0.413
FEV₁ (L)	3.24 ± 0.79	3.54 ± 0.93	0.009
FEV₁ %	92.04 ± 12.87	92.87 ± 12.47	0.607
FEV₁/FVC %	79.18 ± 8.26	82.72 ± 6.35	<0.001
FEF_25–50 (L/s)	4.71 ± 1.51	5.51 ± 1.93	<0.001
FEF_50–75 (L/s)	2.49 ± 0.97	3.01 ± 1.04	<0.001
FEF_25–75 (L/s)	3.21 ± 1.13	3.83 ± 1.27	<0.001
PEF (L/s)	6.51 ± 2.10	7.16 ± 2.68	0.061
MEF₂₅ (L/s)	1.54 ± 0.76	1.91 ± 0.79	<0.001
MEF₅₀ (L/s)	3.89 ± 1.33	4.46 ± 1.54	0.002
MEF₇₅ (L/s)	5.57 ± 1.91	6.47 ± 2.46	0.002

Values are presented as mean ± SD. This analysis is restricted to healthy never-smoking individuals to isolate the impact of industrial exposure. High exposure: <10 km; low exposure: >10 km. A p-value <0.05 is considered statistically significant (shown in bold).

Impact of industrial exposure on smoking healthy individuals

When smoking healthy individuals without chronic respiratory disease (n = 69) were analyzed according to their proximity to industrial zones, no statistically significant differences were observed in PFT parameters (p > 0.05). However, a downward trend in pulmonary function values was noted among smoking individuals living and working near industrial zones.

Airway ratios

The FEV₁/FVC ratio in smoking individuals near industrial zones (76.70 ± 8.00) was lower than those in the low exposure group (79.45 ± 7.22), although this difference did not reach statistical significance (p = 0.181).

Small airway functions

Parameters reflecting small airway performance, such as FEF_50–75 (p = 0.088) and MEF₂₅ (p = 0.079), were also lower in the high exposure group; however, these differences did not reach statistical significance (Table 10).

Table 10.

Comparison of PFT parameters in smoking healthy individuals based on cumulative proximity to industrial zones (n = 69).

PFT Pprameters	Smoking healthy individuals—high exposure (n = 48) mean ± SD	Smoking healthy individuals—low exposure (n = 21) mean ± SD	p-value
FVC (L)	4.32 ± 1.00	4.43 ± 1.04	0.658
FVC %	91.50 ± 12.25	91.62 ± 10.76	0.969
FEV₁ (L)	3.30 ± 0.76	3.54 ± 0.91	0.263
FEV₁ %	88.29 ± 14.01	89.33 ± 13.26	0.774
FEV₁/FVC %	76.70 ± 8.00	79.45 ± 7.22	0.181
FEF_25–50 (L/s)	4.89 ± 1.58	5.24 ± 1.70	0.417
FEF_50–75 (L/s)	2.29 ± 0.90	2.74 ± 1.15	0.088
FEF_25–75 (L/s)	3.08 ± 1.10	3.54 ± 1.29	0.139
PEF (L/s)	7.30 ± 2.12	6.66 ± 2.08	0.339
MEF₂₅ (L/s)	1.33 ± 0.57	1.71 ± 0.87	0.079
MEF₅₀ (L/s)	3.78 ± 1.30	4.15 ± 1.38	0.293
MEF₇₅ (L/s)	6.10 ± 1.93	6.31 ± 2.11	0.690

Values are presented as mean ± SD. Analysis was performed in smoking healthy individuals to evaluate the combined impact of smoking and industrial proximity. A p-value <0.05 is considered statistically significant.

Correlation between air pollution exposure and PFT parameters

Correlations between PFT parameters and IES were investigated. The IES was calculated based on the 1-year (IES-1) and 4-year (IES-4) mean concentrations of CO, NO, NO₂, NOx, PM₁₀, and SO₂ measured in the Selçuklu, Karatay, and Meram districts.

IES-4 and PFT correlations

When analyzing the 4-year cumulative exposure (IES-4) for all participants, a statistically significant, weak negative correlation was identified between PM₁₀ exposure and MEF₂₅ values (r = −0.112, p = 0.017). However, this correlation did not remain significant when the analysis was restricted to nonsmoking healthy individuals.

IES-1 and PFT correlations

Similarly, a significant weak negative correlation was observed between 1-year PM₁₀ exposure (IES-1) and MEF₂₅ for all participants (r = −0.108, p = 0.021). No significant correlation was found in the subgroup of nonsmoking healthy individuals.

No significant correlations were found between other IES parameters and PFT values across the general study population or within the subgroups of nonsmoking healthy and smoking healthy individuals.

Comparative impact of industrial exposure vs. smoking on PFT parameters

To evaluate the relative impact of environmental industrial exposure compared with tobacco use, PFT parameters were compared between nonsmoking individuals living and working near industrial zones (n = 150) and smoking individuals living and working far from industrial zones (n = 26).

Small airway functions

Notably, nonsmoking healthy individuals in the high exposure group exhibited significantly lower values in several key parameters compared with smoking individuals in the low exposure group, including FEF_25–50 (4.53 ± 1.59 vs. 5.27 ± 1.76 L/s; p = 0.033), FEF_25–75 (3.06 ± 1.18 vs. 3.58 ± 1.35 L/s; p = 0.045), and MEF₇₅ (5.41 ± 1.95 vs. 6.28 ± 2.11 L/s; p = 0.039).

Airway flow rates (FEV₁)

The mean FEV₁ (L) in the nonsmoking high exposure group (3.16 ± 0.88 L) was lower than that in the smoking low exposure group (3.53 ± 0.91 L), with the difference approaching statistical significance (p = 0.057).

Capacity and ratios

No statistically significant differences were observed between the two groups in FVC %, FEV₁ %, FEV₁/FVC %, PEF, or MEF₂₅ parameters (p > 0.05) (Table 11).

Table 11.

Comparison of PFT parameters: nonsmoking individuals with high industrial exposure versus smoking individuals with low industrial exposure.

PFT parameters	High industrial exposure/nonsmoker (n = 150) mean ± SD	Low industrial exposure/smoker (n = 26) mean ± SD	p-value
FVC (L)	4.02 ± 1.06	4.41 ± 1.01	0.088
FVC %	92.67 ± 12.92	91.50 ± 10.08	0.660
FEV₁ (L)	3.17 ± 0.88	3.53 ± 0.91	0.057
FEV₁ %	90.82 ± 14.31	89.65 ± 13.31	0.699
FEV₁/FVC %	78.88 ± 8.57	80.40 ± 7.99	0.402
FEF_25–50 (L/s)	4.53 ± 1.59	5.27 ± 1.76	0.033
FEF_50–75 (L/s)	2.37 ± 1.01	2.79 ± 1.18	0.060
FEF_25–75 (L/s)	3.06 ± 1.18	3.58 ± 1.35	0.045
PEF (L/s)	6.40 ± 2.06	6.62 ± 1.99	0.674
MEF₂₅ (L/s)	1.46 ± 0.76	1.72 ± 0.89	0.116
MEF₅₀ (L/s)	3.72 ± 1.40	4.23 ± 1.45	0.090
MEF₇₅ (L/s)	5.41 ± 1.95	6.28 ± 2.11	0.039

Values are presented as mean ± SD. This comparative analysis evaluates healthy never-smoking individuals in high exposure zones versus smoking healthy individuals in low exposure zones. A p-value <0.05 indicates a statistically significant difference between these two distinct groups (shown in bold).

Discussion

District-level differences in air pollution exposure

Exposure to air pollution is estimated to cause millions of deaths and the loss of millions of healthy life-years annually. Currently, the disease burden associated with air pollution is considered comparable to that of other major global health risks such as unhealthy diets and tobacco consumption.¹⁹ In the context of this global threat, our study was conducted with 455 participants living and working in the central districts of Konya (Selçuklu, Karatay, and Meram). Compared to similar studies in the literature, the fact that 54.9% of our sample comprised university graduates, and 91% had no known chronic respiratory disease adds significant value to our work, particularly for observing the subclinical effects of air pollution in healthy and well-educated individuals. Furthermore, the exceptionally high rate of natural gas use for heating (95.8%) allowed us to minimize confounding factors such as indoor air pollution (e.g. biomass exposure), thereby facilitating a more precise analysis of the relationship between outdoor air quality and pulmonary function. Additionally, the high proportion of participants (approximately 55%–58%) who reported living and working within 10 km of industrial zones strengthened the cumulative exposure analysis from both residential and occupational perspectives.

PM₁₀ exposure and SAD

When short-term and long-term air quality measurements in our study were examined, the mean PM₁₀ concentrations in the Karatay district over the preceding 4 years (71.07 µg/m³) and 1 year (75.145 µg/m³) were approximately twice as high as those in the Selçuklu and Meram districts. The literature indicates that exposure to PM₁₀ is associated with reductions in absolute values of spirometric parameters, including FEV₁, MEF₅₀, and PEF as well as an increased incidence of various respiratory diseases.²⁰

Furthermore, PM₁₀ exposure is known to impair small airway parameters such as MEF₂₅.²¹ In our comparison of participants who both lived and worked within the same district, MEF₂₅ values representing distal airway function were significantly lower in Karatay, where the air pollution burden was highest among the three central districts (1.41 ± 0.65 L/s, p = 0.049).

Validation of PFT results across disease status

In our study, participants with a history of respiratory diseases (asthma and COPD) exhibited statistically significantly lower PFT parameters than healthy individuals (Table 4). The significant differences observed in primary indicators of lung function, such as FVC (p = 0.010) and FEV₁ (p = 0.001), are consistent with established pathophysiological data in the literature. Particularly, highly significant reductions (p ≤ 0.002) were recorded across all parameters reflecting small airway function, including FEF_50–75, FEF_25–75, MEF₂₅, and MEF₅₀. It is well documented in the literature that chronic inflammation and narrowing of the airways primarily affect these flow rates.¹⁷ This finding demonstrates that the disease status reported by participants is directly reflected in objective PFT measurements. These results are important for the following two reasons. 1. Methodological reliability. The ability of PFT measurements to clearly distinguish individuals with known diseases from those without validates the accuracy of the measurement techniques and device calibration used in this study. 2. Sample consistency. The correlation between participants’ health self-reports and physical test results confirms the high internal validity of our dataset, supporting the reliability of findings obtained in other stages of the study. Our findings are consistent with previous studies examining the effects of respiratory diseases on PFTs. Yılmaz et al.²² emphasized that a decrease in FEF_25–75 values is a critical parameter in the diagnosis and monitoring of individuals with respiratory complaints. Similarly, Yazdi et al.,²³ in their study comparing the prevalence of respiratory symptoms and lung dysfunction between healthy individuals and swimming pool workers exposed to airborne chlorine products, reported significantly lower values for primary parameters such as FVC and FEV₁ compared with those in the control group. These data support the consistency between participant reports and PFT results in our study.

Independent effect of smoking on early airway damage

In our study, among healthy participants without prior respiratory disease, smoking individuals exhibited significantly lower values for FEV₁% (p = 0.038), FEV₁/FVC % (p < 0.001), FEF_50–75 (p = 0.047), and MEF₂₅ (p = 0.018) compared with nonsmoking individuals. The significant decline in MEF₂₅, alongside other parameters, supports its consistency as a clinical marker. These findings indicate that the adverse impact of smoking on the respiratory tract begins well before the onset of clinical symptoms or overt airway restriction (such as COPD or asthma) develop. In particular, the highly significant difference observed in the FEV₁/FVC % ratio (p < 0.001), which reflects major airway resistance and obstruction, confirms that cigarette smoke affects airway caliber even in the early stages. More importantly, the correlation analyses presented in Table 6 demonstrate that these functional declines are directly associated with smoking intensity (pack-years). As smoking intensity increased, significant negative correlations were observed not only in primary parameters such as FEV₁% (p = 0.007) and FEV₁/FVC % (p < 0.001) but also in sensitive indicators of small airway function, specifically FEF_50–75^24,25 (p = 0.024) and MEF₂₅¹⁶ (p = 0.006). The significant correlation observed for MEF₂₅ supports the concept that damage in the small airways, often referred to in the literature as the “silent zone,”¹⁶ becomes more pronounced as cumulative smoking exposure increases. This suggests that MEF₂₅ may serve as a consistent and sensitive marker for detecting early-stage tobacco-induced damage. Overall, these findings indicate that smoking leads to an insidious decline in small airway function (MEF₂₅) and general airway resistance (FEV₁/FVC), even before clinical disease manifests.

Industrial proximity and pulmonary function

One of the most striking findings of our study is that individuals whose residence and workplace were within 10 km of an industrial zone exhibited statistically significantly lower PFT values than those living farther away. The highly significant decline observed in parameters such as FEV₁/FVC % (p < 0.001) and FEF_25–75 (p < 0.001), as well as across the entire MEF and FEF series, demonstrates that cumulative and continuous exposure to industrial air pollution increases the risk of airway obstruction and SAD. The absence of a significant difference in FVC% values (p = 0.887) in our findings suggests that industrial pollution in this population does not cause restrictive damage but rather induces an obstructive effect that narrows the small airways. The finding that the FEF_25–75 value was 3.06 L/s in the “near” group compared with 3.76 L/s in the “far” group, along with similar impacts on other small airway parameters, confirms that proximity to industrial zones is an independent risk factor for silent SAD. Our findings are consistent with previous studies showing that individuals working in occupations at risk for air pollution exposure experience declines in FEV₁ and FEV₁/FVC values. Obstructive patterns have been reported to be more common in adults living in urban areas where industrial emissions are more concentrated compared with rural regions.²⁶ Furthermore, the European Study of Cohorts for Air Pollution Effects (ESCAPE), a meta-analysis of five European cohorts, demonstrated that higher exposure to ambient NO₂ and PM₁₀ as well as higher traffic density near residences are associated with lung function impairment in adults.²⁷

Impact of environmental exposure on never-smoking healthy individuals

After excluding participants with known respiratory diseases, PFT values were compared specifically among healthy individuals based on their cumulative proximity to industrial zones (Table 8). This analysis provided a “purer” dataset by entirely eliminating the confounding effect of pre-existing pathology. Our findings showed that even in healthy individuals, living and working within 10 km of industrial zones results in significant reductions in small airway flow rates (MEF/FEF series) and airway ratios (FEV₁/FVC). Notably, the reduction of the FEV₁/FVC ratio from 82.2% to 78.7% in the healthy cohort indicates that industrial proximity initiates an “obstructive” trend. These subgroup analyses within the healthy subgroup demonstrated that the detrimental impact of industrial exposure is independent of chronic disease status.

In addition, PFT measurements of never-smoking healthy individuals were compared based on proximity to industrial zones (Table 9). These findings provided critical evidence for the study. By excluding confounding factors such as pre-existing disease and smoking, this analysis clearly demonstrated the “pure” effect of industrial proximity on healthy lungs. It showed that industrial pollution (emissions, dust, and gases) contributes to narrowing of the small airways and deterioration of overall airway ratios (FEV₁/FVC) in nonsmoking healthy individuals. Our findings regarding declines in FEV₁ and FVC among participants living near industrial zones are consistent with recent epidemiological evidence. According to the meta-analysis by Gross et al.,¹⁰ long-term ambient air pollution exposure leads to consistent reductions in lung volumes and airflow across diverse populations. The high-certainty evidence reported for FVC decline in their study further supports our observation that continuous environmental exposure in industrial regions such as Konya initiates a measurable obstructive trend, even in otherwise healthy individuals. The observed impairment in distal lung function, particularly FEF_25–75 and MEF values among industrially exposed nonsmoking individuals, is strongly supported by recent mechanistic insights. Hamanaka and Mutlu¹² highlighted that fine and ultrafine particles (PM₂.₅ and PM₀.₁) are particularly hazardous because they penetrate deep into the small airways and alveoli, where they induce mitochondrial reactive oxygen species (ROS)–dependent inflammatory responses and disrupt the epithelial barrier. This pathways help explain why even individuals without a smoking history or prior disease exhibit an “obstructive” trend when living and working near industrial zones.

The finding that the FEV₁/FVC ratio fell below the 80% threshold (79.17%) even in never-smoking individuals suggests that environmental exposure may act as a primary driver initiating the obstructive process. Consistent with this finding, a recent comprehensive review by Deolmi et al.²⁸ reported that, in nonsmoking individuals, air pollutants such as NOx, SO₂, CO, volatile organic compounds (VOCs), and PM are associated with the development of respiratory diseases through multiple intermediary pathways. These include systemic and nonsystemic inflammatory processes, oxidative stress, and alterations in epigenetic regulation of the lungs.^29,30 Early-life exposure to air pollutants has also been linked to impaired lung function development, particularly in children with asthma.³¹ Furthermore, traffic-related air pollution during childhood has been associated with lower FEV₁ and an increased risk of COPD in later life.³² Furthermore, air pollution is increasingly recognized as a multifaceted environmental stressor; beyond its primary respiratory effects, recent evidence suggests that cumulative exposure to ambient pollutants alongside other environmental factors may also be associated with an increased risk of neurodevelopmental conditions, including autism spectrum disorder.³³

Masking effect of smoking on environmental damage

When smoking healthy individuals without respiratory disease (n = 69) were analyzed based on their proximity to industrial zones (Table 10), no statistically significant differences were observed in PFT parameters (p > 0.05). This finding highlights the distinct and combined effects of industrial exposure and tobacco consumption on the respiratory system. The absence of statistical significance among smoking individuals by industrial distance does not imply the lack of impact of industrial pollution. In contrast, the chronic damage caused by smoking likely “shadows” or masks the additional functional decline associated with environmental factors. In smoking healthy individuals living near industrial zones, the reduction of the FEV₁/FVC ratio from the 79% range to approximately 76% suggests that smoking severely suppresses respiratory capacity, with industrial exposure further exacerbating this condition. Although industrial exposure acts as a strong and independent risk factor in nonsmoking individuals, it combines with the harmful effects of tobacco in smoking individuals to create a cumulative burden. These findings indicate that, in regional planning for Konya, both the strategic positioning of industrial zones and efforts to reduce tobacco consumption are essential for protecting public lung health.

Dose–response relationships and individual exposure scores

Correlations between PFT parameters and IES, calculated based on 1-year and 4-year mean concentrations of CO, NO, NO₂, NOx, PM₁₀, and SO₂ in Konya’s central districts, were investigated. Among all participants, a statistically significant but weak negative correlation was identified only between PM₁₀ and MEF₂₅. However, after adjusting for confounding factors such as smoking and respiratory disease using regression analysis, this association was no longer statistically significant. This finding suggests that air pollution in Konya is not solely industry-derived but is also influenced by PM (dust), traffic, and heating fuels. In addition, factors such as indoor air quality (e.g. secondhand smoke) and limitations of station-based measurements, which may not capture the full diversity of pollutants from various industrial sectors, may have obscured direct correlations. Personal protective measures, including mask usage and indoor air filtration, may also act as masking factors. Nevertheless, group-based comparisons (t-tests) showed that individuals living and working near industrial zones had significantly lower PFT values than those in the low exposure group (p < 0.05). This suggests that industrial exposure exerts a cumulative impact on respiratory function, which may manifest through a regional exposure threshold rather than a strictly linear dose–response relationship.

Comparative effects of industrial exposure and smoking

One of the most comprehensive studies conducted in the UK titled “Air Pollution, Lung Function, and COPD,” clearly demonstrated that ambient air pollution is associated with reduced lung function and increased COPD prevalence.²⁶ Consistent with this, one of the most notable findings of our study emerged from the comparison between nonsmoking individuals with high industrial exposure (both residence and workplace <10 km) and smoking individuals with low industrial exposure (both residence and workplace >10 km) (Table 11). Our findings indicate that continuous environmental exposure to industrial zones may lead to more pronounced impairment, particularly in small and medium-sized airway function, than the damage caused by tobacco consumption alone. Supporting this observation, Wang et al.³⁴ investigated the relationship between long-term exposure to ambient air pollution and changes in emphysema and lung function. They reported that a 3 ppb increase in mean O₃ exposure over a 10-year period was significantly associated with a 0.18% increase in the percentage of emphysema. Remarkably, this increase was found to be equivalent to the increase in emphysema rates caused by 29 pack-years of smoking. Furthermore, the association between higher pollutant concentrations and the more rapid progression of emphysema percentage was also statistically significant for NOx.

Smoking pack-year data, by its very nature, represents intermittent exposure spread over time. As pack-years are the product of daily consumption and total years of smoking, they reflect the cumulative “duration” of tobacco exposure, even if it occurs at intervals. In our previous correlation analysis (Table 6), the significant negative correlations between pack-years and FEV₁/FVC (r = −0.218) and MEF₂₅ (r = −0.124) demonstrate the impact of this cumulative but intermittent exposure. In contrast, the industrial proximity analysis, categorizing those who both live and work near industrial zones (<10 km), assumes that these individuals spend approximately 24 h a day within that environment. This represents a more comprehensive “total life exposure.” The significantly lower values of FEF_25–50 (p = 0.033), FEF_25–75 (p = 0.045), and MEF₇₅ (p = 0.039) observed in high exposure nonsmoking individuals than in low exposure smoking individuals can be attributed to the continuous nature of industrial exposure. Moreover, air pollution is not a “choice.” Although smoking is an individual and intermittent preference, living and working in an industrial environment represents an unavoidable and continuous exposure. Our findings suggest that this persistent environmental burden may even exceed the obstructive effects traditionally associated with smoking, as reflected by an FEV₁/FVC value of 79.17% in the nonsmoking high exposure group.

Strengths and limitations

Strengths

This study has several notable strengths that enhance the validity and clinical relevance of its findings. First, it uniquely evaluates the synergistic and independent impacts of both environmental industrial exposure and active smoking, rather than focusing on a single risk factor. By incorporating both residential and workplace proximity to industrial zones, the study captures a more comprehensive “total life exposure” compared with residence-only assessments. Methodologically, the use of objective PFT and sensitive small airway parameters (FEF_25–75 and MEF series) allows detection of subtle subclinical changes before the onset of overt respiratory disease. The inclusion of generally healthy, well-educated adults, along with the exceptionally high rate of natural gas use (95.8%), effectively minimizes major confounders such as indoor biomass exposure and pre-existing pathology. Furthermore, by comparing exposure patterns across multiple districts with varying pollution burdens and utilizing both proximity-based metrics and pollutant-specific exposure scores (IES), this study provides a multifaceted perspective. Finally, these findings offer practical, evidence-based relevance for urban planning and public health policies related to industrial zoning.

Limitations

Although this study provides robust evidence demonstrating the associations between industrial proximity and pulmonary functions, it possesses several limitations that warrant a cautious interpretation.

Study design and causality

Given its cross-sectional design, this study primarily establishes significant associations; therefore, a definitive causal relationship between industrial proximity and PFT outcomes cannot be fully established. Longitudinal studies would be beneficial to assess long-term progressive effects.

Exposure estimation and potential misclassification

Exposure levels were categorized using a 10-km linear distance threshold. Although this threshold provides a practical and standardized comparative framework for this region, it may not fully capture all microclimatic factors (e.g. wind direction and topography) or the complete individual exposure profile. In addition, reliance on district-level monitoring data, rather than personal exposure sensors, may introduce some degree of exposure misclassification.

Time–activity patterns and other sources

The analysis did not include detailed time–activity patterns, such as precise time spent in traffic, specific indoor ventilation conditions, or commuting exposures. In addition, although the focus was on industrial emissions, other contributors, including traffic-related pollutants, specific occupational exposures, and secondhand smoke, may not have been fully quantified.

Bias and confounding factors

Information on respiratory symptoms and chronic disease status was obtained via self-report, which can introduce recall or reporting bias. Despite rigorous subgroup analyses, residual confounding may persist from factors such as body mass index (BMI), detailed occupational history, or subtle socioeconomic variation.

Generalizability and statistical power

As the study was conducted in a single industrial hub (Konya), the findings may require further validation in regions with different geographic or industrial profiles. Although the overall cohort size was robust, some specific subgroup comparisons (e.g. smoking healthy individuals in low exposure zones) had relatively small sample sizes, which may limit statistical power in those analyses.

Conclusion and recommendations

This study suggests that industrial proximity and active tobacco consumption may act as significant, independent risk factors associated with pulmonary function decline, potentially manifesting long before the onset of overt clinical respiratory symptoms. A key finding of this study is the observed association indicating that continuous environmental exposure in high-density industrial zones may be linked to more pronounced small airway impairment, particularly reflected in FEF_25–75 and MEF parameters, which may be comparable to, or in some cohorts even exceed, the functional losses traditionally attributed to active smoking. Even among never-smoking healthy individuals, living and working within a 10-km radius of industrial activity was associated with a measurable obstructive trend in FEV₁/FVC ratios, suggesting that environmental exposure may act as a primary driver of early-stage airway narrowing. These findings indicate that distal airway parameters may serve as sensitive early markers of subclinical lung damage and may warrant closer clinical monitoring in at-risk urban populations.

To address these challenges, environmental health strategies should consider the long-term respiratory impact of industrial zoning on nearby residential areas. The implementation of green buffer zones and improved urban planning strategies may help mitigate cumulative pollution exposure. In addition, in regions with high industrial density, the assessment of small airway function using detailed spirometric parameters may be a valuable component of routine health screening for early detection of subclinical changes. Ultimately, integrated public health policies that prioritize both stringent air quality management and tobacco control are essential for protecting community respiratory health. Although these findings provide a strong foundation for regional planning, further longitudinal and multicenter studies are needed to better quantify long-term clinical implications and clarify dose–response relationships between industrial exposure and pulmonary health across diverse urban settings.

Supplemental Material

sj-pdf-1-imr-10.1177_03000605261448032 - Supplemental material for The effect of industry-related air pollution on pulmonary function: A prospective, cross-sectional comparative study of industrial proximity and smoking status

Supplemental material, sj-pdf-1-imr-10.1177_03000605261448032 for The effect of industry-related air pollution on pulmonary function: A prospective, cross-sectional comparative study of industrial proximity and smoking status by Celalettin Korkmaz, Mehmet Uyar, Şeyma Şahin, Büşra Ünal, Bekir Sunay, Ömer Faruk Aydos, Basel Shaqdeh, Fatih Alptekin, Recep Şaban Üner, Ali Aydınoğlu, Zeynep Nisa Derviş and Rumeysa Zehra Urhan in Journal of International Medical Research

Supplemental Material

sj-pdf-2-imr-10.1177_03000605261448032 - Supplemental material for The effect of industry-related air pollution on pulmonary function: A prospective, cross-sectional comparative study of industrial proximity and smoking status

Supplemental material, sj-pdf-2-imr-10.1177_03000605261448032 for The effect of industry-related air pollution on pulmonary function: A prospective, cross-sectional comparative study of industrial proximity and smoking status by Celalettin Korkmaz, Mehmet Uyar, Şeyma Şahin, Büşra Ünal, Bekir Sunay, Ömer Faruk Aydos, Basel Shaqdeh, Fatih Alptekin, Recep Şaban Üner, Ali Aydınoğlu, Zeynep Nisa Derviş and Rumeysa Zehra Urhan in Journal of International Medical Research

Footnotes

Acknowledgments

The authors would like to thank all participants. Special thanks to the Necmettin Erbakan University BAP Coordinator’s Office.

Author contributions

CK: Conceptualization, Methodology, Project administration, Funding acquisition, Formal analysis, Data curation, and Writing-Original Draft Preparation. MU: Conceptualization, Methodology, Validation, and Writing-Review & Editing. ŞŞ, BÜ, and BS: Investigation, Data curation, and Validation. ÖFA, BS, FA, RŞÜ, AA, ZND, and RZU: Investigation, Resources, and Data curation. All authors have read and agreed to the published version of the manuscript.

Data availability statement

The air quality datasets analyzed are available at . Original research data supporting the results of this study are available from the corresponding author upon reasonable request.

Declaration of conflicting interests

The authors declare that there are no financial or personal conflicts of interest that could inappropriately influence the research and findings presented in this study.

Funding

This study was supported by the Necmettin Erbakan University Scientific Research Projects (BAP) Coordinator’s Office (Project No: 23GAP18011).

Role of AI tools

During the preparation of this manuscript, the authors used Gemini 1.5 Flash exclusively for English language translation and to enhance readability. This tool was not used for data collection, analysis, or interpretation. The authors assume full responsibility for the content and integrity of the final article.

ORCID iD

Celalettin Korkmaz

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Supplementary Material

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The effect of industry-related air pollution on pulmonary function: A prospective,cross-sectional comparative study of industrial proximity and smoking status

Abstract

Objective

Methods

Results

Conclusions

Keywords

Introduction

Air pollution as a global public health concern

Industrial emissions and respiratory health

Impact of air pollution on lung function and small airway dysfunction (SAD)

Air pollution as a predominant modifiable risk factor

Geographic and industrial significance of the study site

Knowledge gaps and study aims

Patients and methods

Study population and sampling strategy

Inclusion and exclusion criteria

Inclusion criteria

Exclusion criteria

Questionnaire design, administration, and quality control

Air quality data collection

Individualized exposure score (IES) calculation

PFT

Assessment of air pollutant effects and comparison groups

Statistical analysis

Results

Air quality analysis of study districts

Participant characteristics, environmental factors, and respiratory symptoms

Impact of regional residence on respiratory functions

Association between respiratory diseases and PFT parameters

FVC values

FEV1 values

Small airway flow rates

Impact of smoking on PFT parameters in healthy participants

FEV1 and ratios

Peripheral airway functions

Correlation between smoking intensity (pack-years) and PFT parameters

FEV1/FVC ratio and FEV1

Small airway functions

Cumulative impact of residential and workplace proximity to industrial zones on PFT parameters

Airway obstruction parameters

Small airway function

Impact of proximity to industrial zones on PFT parameters in healthy participants

Airway patency and capacity

Small airway performance

Impact of industrial exposure on healthy never-smoking individuals

Airway obstruction and capacity

Small airway functions

Impact of industrial exposure on smoking healthy individuals

Airway ratios

Small airway functions

Correlation between air pollution exposure and PFT parameters

IES-4 and PFT correlations

IES-1 and PFT correlations

Comparative impact of industrial exposure vs. smoking on PFT parameters

Small airway functions

Airway flow rates (FEV1)

Capacity and ratios

Discussion

District-level differences in air pollution exposure

PM10 exposure and SAD

Validation of PFT results across disease status

Independent effect of smoking on early airway damage

Industrial proximity and pulmonary function

Impact of environmental exposure on never-smoking healthy individuals

Masking effect of smoking on environmental damage

Dose–response relationships and individual exposure scores

Comparative effects of industrial exposure and smoking

Strengths and limitations

Strengths

Limitations

Study design and causality

Exposure estimation and potential misclassification

Time–activity patterns and other sources

Bias and confounding factors

Generalizability and statistical power

Conclusion and recommendations

Supplemental Material

sj-pdf-1-imr-10.1177_03000605261448032 - Supplemental material for The effect of industry-related air pollution on pulmonary function: A prospective, cross-sectional comparative study of industrial proximity and smoking status

Supplemental Material

FEV₁ values

FEV₁ and ratios

FEV₁/FVC ratio and FEV₁

Airway flow rates (FEV₁)

PM₁₀ exposure and SAD