Exposure Intensity Index (EII): A New Tool to Assess the Pollution Exposure Level of the Skin

Abstract

Air pollution is known to affect skin health, but tools to objectively measure individual exposure based on skin responses are limited. This study introduces the Exposure Intensity Index (EII), a novel tool that correlates lifestyle-related pollution exposure with skin parameters. A panel of 250 women residing in Lombardy completed a detailed questionnaire on socio-demographic features and daily habits, from which an exposure score was derived. Non-invasive bioengineering techniques were used to assess skin parameters, focusing on inflammation-related signs. A positive correlation emerged between exposure scores and variations in specific skin parameters, suggesting a link between daily pollution exposure and skin alterations. The EII emerges as a preliminary exploratory approach to estimate environmental impact on the skin through its correlation with biophysical parameters. It may offer future value for subject selection in in vivo testing of antipollution cosmetic claims.

1. Introduction

Air pollution is a serious global issue that continues to be a widespread concern [1,2]. Exposure represents a major health risk [3] contributing to increasing morbidity and mortality [4,5]. While harmful effects on internal organs are well documented, the impact on the skin, the body’s largest organ [6], is still limited. Although the skin is recognized as a target for environmental pollutants, being the outermost barrier of the body [7], there are few in vivo trials investigating skin changes after exposure. Air pollution is a complex mixture of chemical, physical, or biological agents [5] coming from different sources. Among the most reactive skin pollutants are ozone (O₃), polycyclic aromatic hydrocarbons (PAHs), volatile organic compounds (VOCs), oxides (NOx-SOx-COx), urban particulate matter (PM10-PM2.5) [8,9], and cigarette smoke [10], which contains a variety of PAHs [11]. Cigarette smoke is among the most toxic airborne pollutants and has been linked to premature skin aging [12,13]. Cumulative exposure to air pollution, over premature skin aging [14,15], may impair the skin barrier, exacerbate skin conditions [16], especially when combined with other pollutants or UVR, amplifying their prooxidant smog activity [8]. Accumulated evidence from in vitro studies regarding the action of the single factors on the increased production of reactive oxygen species (ROS), induction of oxidative stress, activation of aryl hydrocarbon receptor (AhR), and inflammatory cytokines [16] shall be accompanied by trials on the outcome of the general interaction in vivo between pollutants and skin. However, ethical constraints limit direct in vivo exposure trials, making it essential to assess chronic environmental exposure through indirect but quantifiable parameters. Therefore, to be predictive of the result of daily exposure to pollution, it is necessary to recruit subjects for in vivo trials whose chronic degree of exposure is well established by clear criteria involving their lifestyle. The factors that mostly influence the exposure to air pollution are industrial sources and traffic [17]. Other evidence states that urban or rural areas differ in terms of smog quantity and pollutants derived from combustion processes, industry, or roads [13,14]. We then designed a comprehensive lifestyle-based questionnaire to estimate individual exposure.

The aim of this study was to identify the correlation between lifestyle-driven exposure to air pollutants and skin features on a panel of Caucasian women living in Northern Italy, one of the most heavily industrialized and heavily polluted regions of the country [18]. Specifically, the Lombardy region has many industrial facilities and enterprises, leading to considerable road transport. Given that non-industrial combustion plants and road transport represent more than 60% of particulate matter emission sources in the region [19], Lombardy is characterized by high air pollution levels, heterogeneous among subregional areas. Moreover, the stagnation of air pollutants over its lowland areas is facilitated by weather, climate, and orographic characteristics [20]; thus, the specific area located in the basin of the Po River is subjected to very low air mass exchange. Here, wind speed is among the lowest in Europe, leading to frequent thermal inversion and trapping of smog and pollution close to the ground [19].

2. Experimental Section

The prospective observational study consisted of two phases: design and submission of a questionnaire to assess pollution exposure based on lifestyle habits (phase 1) and the in vivo instrumental evaluation of skin parameters in eligible subjects based on phase 1 (phase 2). The single-center study was performed on women living in Pavia County, Lombardy, located in the basin of the Po River. The area features a heterogeneous landscape subdivided into three air quality zones, as defined by regulatory authorities:

Zone A–Highly urbanized lowland;

Zone B–Lowland area;

Zone C–Pre-Alps and Apennines [21].

Zone A and zone B are characterized by favorable conditions for the accumulation of pollutants typical of the Po Valley with varying intensities, while zone C is characterized by weather conditions that are favorable to pollutant dispersion.

Subjects were enrolled according to the following criteria:

Female subjects (>18 y.o; <70 y.o.);

Good general health;

Absence of cutaneous diseases;

Absence of cutaneous lesions or other lesions in test areas;

No history of hypersensitivity;

Not pregnant or breastfeeding;

Subjects who signed the informed consent form.

General exclusion criteria were as follows:

Subjects that did not meet the inclusion criteria above;

Subjects undergoing pharmacological therapy;

Subjects with known allergies.

2.1. Pollution Exposure-Based Questionnaire

An in-depth survey was developed to estimate the chronic exposure to pollutants due to normal lifestyle and habits. The questionnaire, consisting of 18 questions, was divided into 3 sections, as shown in

Table 1. Questionnaire divided into 3 sections (A, B, C).

Section	Meaning	Questions	Answers
		Preliminary Information
A	Understanding the background characteristics of the subjects, such as gender, age, and general lifestyle factors	- Name/Surname - Gender - Age - Birthplace - Physical activity - Beauty Routine - Outdoor activities - UV lamps (frequency) - UV protection (frequency and type) - Change in address (during last 2 ys)	Free
Exposure Information
B	Investigating the exposure to air pollution related to the identified main sources, such as traffic and industrial processes	- Living area	- Rural area: 1 - Country: 2 - City: 3 [22]
		- Proximity to major/minor roads	- Parks/green areas: 1 - Lowdensity traffic Roads: 2 - Highdensity traffic roads or major roads (e.g., ring roads, motorway junctions, etc.): 3
		- Proximity to industrial facilities	- None: 0 - General industrial facility: 2 - Waste disposal facility/Chemical industry/Metallurgical industry: 3
		- Means of transport	- Public transport (bus/tram): 1 - Car: 1 - Bike/Motorcycle: 2 [23] - Public transport (train/tube): 3
		- Average time spent in traffic per day, regardless of the means of transport (H = Hours)	- <1 h: 1 - >1 h t <2 h: 2 - >2 h: 3
C	Investigating the current or past smoking habits	- Smoking habits	- No smoker: 0 - Former smoker: 2 - Smoker: 3
		- Cigarettes per day (n°) (in case of actual smoker)	- <5: 1 - >5 n° <10: 2 - >10: 3
		- Smoking Years	- <5 y: 1 - >5 y <10 y: 2 - >10 y: 3

:

-

Preliminary information:

A: Socio-demographic and general data;

-

Exposure information:

B: Living area, proximity to congested area/polluting facilities, means of transport;

C: Cigarette smoke.

Section A was crucial in determining the eligibility to phase 2 through textual or numerical open-ended questions. This ensured that the enrolled population remained sufficiently heterogeneous while avoiding confounding extremes.

Sections B and C gathered information on exposure to the most impactful pollution sources: road traffic, industrial proximity, time spent in transit, and smoking habits.

All answers were indexed: a numerical score was assigned to each question with a response intensity scale, where a higher number corresponds to greater exposure. The resulting identified scale had scores from 4 (minimum) to 18 (maximum). A score lower than 4 was not categorized, reflecting the unavoidable baseline level of pollution in the region. In this initial methodological phase, the scoring system was kept non-weighted. The primary aim was to test whether a cumulative score correlates with biological outcomes, thereby supporting its construct validity. This system enabled data processing and the derivation of the exposure intensity index (EII) score.

2.2. In Vivo Instrumental Evaluation

To investigate the correlation between air pollution, identified by the resulting exposure intensity index (EII) score, and skin features, eligible subjects underwent an in vivo instrumental evaluation of skin condition. All the participants for the trial were recruited in accordance with the Declaration of Helsinki (Ethical Principles for Medical Research Involving Human Subjects) [24]. Additionally, the study protocol was structured in line with the SPIRIT recommendations (Standard Protocol Items: Recommendations for Interventional Trials), also consistent with the methodological updates later introduced in SPIRIT 2025 [25].

The assessment was performed on the cheekbone area in an air-conditioned room with controlled temperature and humidity (T 22 °C, R.H. = 50% ± 5%) after acclimatization for at least 15 min. Each participant did not apply any cosmetics and/or topical products to the area on the evaluation day.

The instruments used in the evaluation of the skin properties, such as water content and the erythema index, involved contact between the skin and a series of probes that did not cause discomfort, pain, or damage to the skin.

The Stratum Corneum Water Content (SC water content), reflecting the skin hydration, was evaluated by Corneometer CM 825 (Cutometer MPA580, Courage & Khazaka, Cologne, Germany). Corneometry is a technology used to assess the hydration of the outer layer of the epidermis: the stratum corneum [26]. Since the skin is a dielectric medium, all variations in SC water content correspond to changes in the skin capacity [27].

The device used in our trial was equipped with a 49 mm² surface probe that allows precise measurements in 1 s within a 10–20 µm depth range in the stratum corneum. The parameters were expressed as an arbitrary score scale (0–100 A.U.).

The erythema index (EI) was assessed using a reflectance spectrophotometer (Mexameter MX 18, Courage & Khazaka, Cologne, Germany). Using reflectance spectrophotometry, it measures one of the components responsible for skin color: hemoglobin. A 5 mm diameter probe emits three selected wavelengths (568 nm, 660 nm, and 870 nm) to determine the amount of biochromophore (hemoglobin), providing an arbitrary erythema Index (IE) in the range of 0–999.

2.3. Statistical Analysis

A statistical analysis of the data collected from the in vivo study was performed. The comparison of the data collected was performed using a t-test. A significance of 95% was chosen, thus changes were considered significantly different when the p value was <0.05.

Spearman’s rank correlation test was used to evaluate the correlation between two variables [28]. It has a value between +1 and −1, where 1 is a total positive correlation and −1 is a total negative correlation. The correlation reflects the noisiness and direction of a relationship, but not the slope of that relationship, nor many aspects of nonlinear relationships. There is no consensus on the classification of the relationship for different coefficients. In this work, the criteria reported in

Table 2. Criteria applied for the strength interpretation of Spearman’s rank correlation coefficient.

Coefficient Value		Strength Interpretation
+1	−1	Perfect positive or negative correlation
+0.9–0.7	−0.9–0.7	Very strong correlation
+0.6–0.4	−0.6–0.4	Strong correlation
+0.3	−0.3	Moderate correlation
+0.2	−0.2	Weak correlation
+0.1	−0.1	Negligible correlation
0	0	No correlation

were applied.

3. Results

An analysis of pollution in the designated geographical area is essential to comprehend the operative framework of the study. Arpa Lombardia (regional agency for environmental protection) [21] provides data on air emissions in Pavia County, distinguishing among the types of pollutants. Data provided by ARPA Lombardia highlight that in the province of Pavia, the main emission sources remain constant over time. The values, observed consistently in 2020, 2022, and 2023, indicate a remarkable temporal stability of pollutant patterns. The study was conducted in 2022, while emission data from 2020 and 2023 were also considered as comparison years; 2021 was excluded as it represented an anomalous year due to the COVID-19 pandemic, introducing a potential bias. This temporal stability, combined with the intra-provincial heterogeneity between urban, industrial, and rural zones, makes the area particularly adequate for evaluations on the correlation between chronic exposure to pollution and skin parameters. Data shows that the average emissions of various pollutants remain relatively constant with only slight shifts between specific components and sources, which do not significantly impact the total emissions. This overview of the environmental conditions supports the need for an “in vivo” investigation.

Furthermore, pairing the data related to the industrial sector and the traffic-related sector, which are of particular interest for the study and closely related to emissions that are most likely to interact with the skin, such as oxides, VOC, and PM [21], it was clear that pollution generated by industrial facilities and transport is generally comparable, confirming that the contribution depends on both the source and the type of pollutant, as shown in Figure 1. This observation supports the decision to adopt a non-weighted scoring system in this exploratory phase, while leaving open the possibility of refining the scores based on region-specific exposure patterns.

According to these observations, the selection of the region, based on the type of pollution and continuity of exposure, proved to be suitable for the development of the study.

In total, 250 subjects were enrolled in Pavia province, who filled out the comprehensive questionnaire designed in 2022, a year marked by normal anthropogenic activity, frequent PM₁₀ exceedances, and ozone peaks [21], within a narrow seasonal window. Participants were almost equally distributed between zone A (58%-urban areas) and zone B and C (40% countryside, 2% rural areas): this implied the involvement of panelists originating from distinct areas within the county, thus subjected to varying levels of air exposure. According to answers to section A of the questionnaire, exclusion criteria were applied to individuals whose lifestyle significantly diverged from that of the average participant. Exposure to UV lamps, no UV protection during intentional sun exposure, and relocation to a different living area within the past five years were identified as exclusion factors. Participants reporting unusually aggressive or highly specific beauty routines, including invasive cosmetic treatments, were also excluded. According to the answers provided in sections B and C, 193 subjects were recruited for the evaluation of the SC water content, and 209 subjects were recruited for the evaluation of the erythema index (Phase 2).

Biophysical Parameters Correlated to Score

The characteristics of the skin are normally influenced by age, and this relation has been demonstrated in several studies [29,30]. Different evidence shows that the SC water content is altered by age, with a big impact due to ethnicity as well, especially for Caucasians [31]. The correlation between the exposure intensity index (EII) and biophysical parameters was assessed by adopting an age-adjustment approach to ensure a more homogeneous panel.

Establishing the threshold at 50 y.o. and comparing the SC water content values on 80 volunteers between group 1 (women < 50 y.o.) and group 2 (women ≥ 50 y.o.), we observed a statistically significant difference in SC water content levels between the two age groups (p = 0.0086 **), as shown in Figure 2.

When correlating SC water content values with beauty routine data (section A6 of the questionnaire), it became evident that individuals aged over 50 generally exhibited a more pronounced influence from their skin care habits [32].

Age can affect the erythema index, as well as sex, race, and anatomical site (Figure 2b). Establishing the threshold at 50 y.o. and comparing erythema index values between group 1 (women < 50 y.o.) and group 2 (women ≥ 50 y.o.), we observed a statistically significant difference in erythema values between the two groups, p < 0.0001 ****.

Spearman’s rank correlation test proved a strong correlation between age and erythema index, as shown in

Table 3. Correlation coefficients results.

Parameters	Spearman’s Coefficient	Strength Interpretation
Erythema index vs. age	−0.40	Strong correlation
SC water content vs. age	0.25	Weak correlation

. Although subgroup sizes were relatively small, the study design minimized major sources of variability: all participants were Caucasian women, and all measurements were performed on the same anatomical site (cheekbone). Age, therefore, represented the main factor requiring stratification, and under these controlled conditions, the correlation between EII and skin parameters appeared more consistent.

Therefore, we decided to proceed by separating the two age groups due to the considerations outlined earlier.

Specifically, 39 subjects aged between 21 and 49 (group 1) underwent the instrumental evaluation, and their SC water content values were associated with the exposure intensity index score (EII) (

Table 4. Average skin values per group vs. EII.

	SC Water Content (A.U.)		Erythema Index (A.U.)
EII Score	Group 1	Group 2	Group 1	Group 2
6–8	47.29	55.99	361.50	281.89
9–10	49.33	56.47	360.18	325.00
11–12	56.54	57.00	369.85	284.86
13–14	37.40	53.09	311.08	272.44
15–16	47.27	53.21	417.03	377.22

). In total, 41 participants, aged between 50 and 70 years (group 2), underwent the same instrumental evaluations. Their SC water content values were then correlated with the exposure intensity index score (EII), as shown in Table 4.

The results revealed that, among the two age groups considered, there was a generally observed decline in SC water content values, which was statistically significant. This downward trend in SC water content values was associated with higher EII scores indicative of increased exposure to pollution. Nevertheless, this trend does not appear to hold practical relevance. In fact, within group 1, the parameter shows variability: the lack of consistency in cosmetic treatment, together with the cause–effect relationships with pollution, which are still to be investigated, does not depict a clear pattern of performance. Conversely, in group 2, there is minimal variance among SC water content values linked to the different scores, probably due to the previously mentioned impact of the beauty routine. This observation underscores that a pollution-related effect cannot be clearly defined or attributed to this specific skin characteristic. Furthermore, 59 subjects aged between 21 and 49 y.o. underwent the instrumental evaluation, and their biophysical parameters were associated with the exposure intensity index score (EII). A total of 53 participants, aged between 50 and 70 y.o (group 2), also underwent the instrumental evaluation. Their erythema index values were associated with the exposure intensity index score (EII) (Table 4). As results demonstrated, subjects eligible for the phase 2 evaluation showed an upward trend in both age groups, with higher erythema values being correlated with higher EII scores indicative of heightened exposure to pollution. Both groups demonstrated a similar pattern, suggesting that the erythema index appeared more sensitive to pollution-related exposure than SC water content values, although further studies are required to confirm this association under controlled conditions. This becomes even more evident when combining data obtained for both groups (1 and 2). Except for scores 13–14, where the dataset is less consistent compared to the other scores, the trend seems to be clearly discernible, as depicted in Figure 3.

4. Discussion

While the cutaneous effects of pollutants have been the subject of numerous investigations, most of these studies focused on isolated aspects without offering an integrated model of exposure–response dynamics. Our work proposes a new approach to characterize the relationship between self-reported exposure and skin parameters within the framework of real-life environmental conditions.

This study introduces the exposure intensity index (EII) as a preliminary framework designed to differentiate individuals based on lifestyle-related exposure. Unlike previous methods limited to environmental measurements or in vitro exposure models, the EII enables an in vivo contextualization, accounting for individual habits, geographic variability, and cumulative exposure, thus offering a global assessment of pollution impact. The non-weighted scoring system was adopted at this stage, since emission data from the Lombardy region show comparable contributions of traffic- and industry-related sources [21].

Our findings indicate that EII scores significantly differ across subgroups from distinct geographical areas, supporting its capability in capturing differential exposure patterns. More importantly, EII correlates with increased erythema index values, suggesting a link between self-reported pollutant exposure and early-stage cutaneous inflammation.

This result aligns with the evidence indicating that polycyclic aromatic hydrocarbons (PAHs) and other airborne toxicants can induce skin irritation, redness, and subclinical inflammatory responses following repeated exposure [33,34]. The fact that erythema intensity follows the EII gradient reinforces the plausibility of this association and underscores the relevance of self-reported data as a representation for actual impact.

Conversely, the variability observed in stratum corneum (SC) water content values, especially when modulated by age and other intrinsic factors, suggests a weaker or more complex relationship with pollution exposure. Although previous studies have associated pollution with skin dryness and xerosis [35,36], our results indicate that this effect may be influenced by multiple variables, making it a less robust marker in this context. In fact, it is possible that skin hydration may be more readily compensated for by regular beauty routines—such as moisturizing practices—thereby masking any subtle effects of pollution exposure. In contrast, erythema is less influenced by such habits and remains responsive to EII variations. This interpretation is consistent with evidence showing that women tend to prioritize facial skincare while often neglecting other body areas [37]. As this study represents an exploratory phase, the lack of significant hydration findings is not unexpected; increasing the sample size in future research will allow a deeper investigation of this parameter. EII offers a non-invasive and cost-effective approach to exploring exposure levels. The current non-weighted scoring system was chosen based on the comparability of industrial and traffic-related emissions in the study area. Its potential relevance is suggested by the correlation with inflammation-related skin parameters,

Nonetheless, some limitations must be recognized. The subjective nature of self-reporting introduces potential issues, while erythema may also be affected by other factors. Further validation is still necessary before considering its reliability for broader applications. While the study population was geographically restricted, the controlled heterogeneity achieved through exclusion criteria supports the internal validity of findings, although future studies should confirm them in larger and more diverse cohorts. EII holds significant potential for forthcoming research and product development: in the cosmetics sector, where antipollution claims are increasingly growing, EII may act as a preliminary tool to recruit subjects with a clear exposure background, enhancing the results of efficacy studies.

5. Conclusions

This study provides evidence that the exposure intensity index (EII), derived from lifestyle-based questionnaires, serves as an exploratory framework showing potential to differentiate pollution exposure based on skin condition, particularly erythema. Its application introduces a new methodological system for evaluating environmental stress on the skin, with implications for both basic research and the cosmetic industry.