
==== Front
Heliyon
Heliyon
Heliyon
2405-8440
Elsevier

S2405-8440(24)12657-6
10.1016/j.heliyon.2024.e36626
e36626
Review Article
The influence of N95 and surgical masks on carbon dioxide levels: A comprehensive systematic review and meta-analysis
Nassri Mahdi a1
Barari Parviz a1
Khanizadeh Mohammad a
Faridi Sasan bc
Shamsipour Mansour b
Naddafi Kazem ab
Niazi Sadegh d
Hassanvand Mohammad Sadegh hassanvand@tums.ac.ir
ab⁎
a Department of Environmental Health Engineering, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
b Center for Air Pollution Research (CAPR), Institute for Environmental Research (IER), Tehran University of Medical Sciences, Tehran, Iran
c Department of Research Methodology and Data Analysis, Institute for Environmental Research, Tehran University of Medical Sciences, Tehran, Iran
d Western Sydney University, School of Science, Australia
⁎ Corresponding author. Department of Environmental Health Engineering, School of Public Health, Tehran University of Medical Sciences, Center for Air Pollution Research (CAPR), Institute for Environmental Research (IER), Tehran University of Medical Sciences, Iran. hassanvand@tums.ac.ir
1 Co-first authors.

22 8 2024
30 8 2024
22 8 2024
10 16 e3662618 4 2024
23 7 2024
20 8 2024
© 2024 The Author(s)
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Objectives

This study aimed to assess the impact of wearing N95 and surgical masks on carbon dioxide (CO2) concentrations during various activity levels, to understand the implications for mask use in different settings, especially in light of the COVID-19 pandemic.

Study design

Systematic Review and Meta-Analysis.

Methods

A systematic review was conducted, retrieving 6798 articles from PubMed, Web of Science, and Scopus databases. Twenty-nine articles met the inclusion criteria. Mask types were categorized into N95 and surgical masks, while activities were classified as low, medium, and high.

Results

The meta-analysis revealed CO2 concentrations (mmHg) for different scenarios: No mask (37.91, 95 % CI: 36.46, 39.35), N95-low (36.83, 95 % CI: 33.57, 40.10), N95-moderate (37.85, 95 % CI: 36.51, 39.20), N95-high (39.51, 95 % CI: 38.00, 41.02), N95 with exhalation valve (35.82, 95 % CI: 32.89, 38.75), N95 without exhalation valve (38.45, 95 % CI: 37.10, 39.81), surgical mask-low (38.31, 95 % CI: 34.48, 42.14), surgical mask-moderate (35.05, 95 % CI: 31.12, 38.97), surgical mask-high (36.07, 95 % CI: 34.18, 37.96).

Conclusions

Our findings indicate that N95 masks lead to higher CO2 accumulation during various activities compared to surgical masks. Moreover, surgical masks exhibit higher CO2 concentrations during low activity compared to moderate and high activities. Notably, CO2 concentrations are higher in N95 masks without an exhalation valve compared to those with a valve. No significant difference was observed between not wearing a mask and wearing either N95 or surgical masks in terms of CO2 accumulation. These results provide important insights for mask selection and usage recommendations in different scenarios.

Graphical abstract

Image 1

Highlights

• The effect of two different types of masks on changes in CO2 concentration.

• CO2 levels between wearing and not wearing masks were difference.

• Wearing masks in high physical activities significantly increased CO2 levels.

• N95 masks increased CO2 concentrations by 3 mmHg, from 35 to 38 mmHg.

Keywords

Face mask
Carbon dioxide
Meta-analysis
Systematic review
Pandemic
SARS-CoV-2
==== Body
pmc1 Introduction

The outbreak of the novel coronavirus disease (COVID-19) was declared a pandemic by the World Health Organization (WHO). Acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes COVID-19, had infected 505 million people and caused more than 6 million deaths worldwide as of April 2022. This virus is primarily transmitted from person to person through respiratory droplets and aerosols, especially among people in close contact (e.g., within 1 m) with an infected person [[1], [2], [3]]. Due to the high transmission rate of COVID-19 [4] and its airborne spread [5], the use of personal protective equipment (PPE), particularly face masks, has become mandatory. Face masks, in conjunction with other measures such as physical distancing and hand hygiene [6], help prevent the spread of the virus and reduce the number of infections. Recent studies have shown that mask-wearing by the general public significantly reduces community transmission [7]. Thus, domestic and international authorities, such as the Centers for Disease Control (CDC) [8] and WHO [9], widely encourage wearing masks in public. However, prolonged mask use has been associated with various physiological effects, such as changes in oxygen (O2) and carbon dioxide (CO2) levels. Long-term mask-wearing can cause skin issues like erythema, acne, pigmentation, and contact dermatitis [10]. Concerns have also been raised about poor oxygenation and increased CO2 levels during extended mask use, particularly during physical activities [11]. This can adversely affect performance and physiological parameters, including maximal oxygen uptake and respiratory comfort [12]. Additionally, prolonged mask use can lead to discomfort, affecting breathing dynamics and potentially leading to behavioral issues due to altered airflow and gas exchange [[13], [14], [15], [16], [17]]. Inhaled and exhaled O2 and CO2 concentrations can change within the mask, along with increased airflow resistance and moisture accumulation. These changes can affect airway comfort and breathability, potentially causing CO2 retention, low oxygen saturation (SpO2), and elevated heart rates (HR) [18,19]. A study involving healthcare workers using elastomeric air-purifying respirators found significant decreases in breathing rates and increases in tidal volume, with some subjects experiencing elevated transcutaneous CO2 levels after 1 h of use, impacting comfort negatively [20]. Another study examined the effects of cloth and surgical masks on physiological parameters during exercise. Results showed that while there were no significant differences in breathing frequency, HR, or SpO2 across conditions, cloth masks decreased end-tidal oxygen levels and increased end-tidal CO2 levels compared to controls [21]. Elevated CO2 levels due to rebreathing can lead to hypercapnia, which may impair cognitive function and increase the risk of organ dysfunction at high concentrations [11,22]. Given that various studies have highlighted the effects of mask use on CO2 levels, no comprehensive review has pooled these findings. Therefore, we conducted a systematic review and meta-analysis to investigate the effects of wearing different masks (N95 masks and surgical masks) at varying activity levels (low, moderate, and high) on CO2 concentration.

2 Methodology

2.1 Search strategy

Articles were systematically searched using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) criteria (Fig. 1). The literature search was conducted across multiple databases from April 25, 1995, to February 21, 2023, to identify relevant studies. We queried three English-language databases: PubMed, Web of Science Core Collection, and Scopus, using the following search terms: Face mask ("Face Mask*", "Surgical Mask*", N95, N99, FFP1 (filtering face piece mask), FFP2, FFP3, Mask, "Filtering face piece mask*", Respirator), CO2 (CO2, "Carbon dioxide*", "Exhaled breath*"). Boolean operators “OR” and “AND” were used to combine these search terms. The PICOS framework (Participants, Intervention, Comparisons, Outcomes, and Study design) is detailed in Table S1 (Supplemental file). The full search strategies for PubMed, Scopus, and Web of Science are presented in Table S2.Fig. 1 PRISMA flow diagram describing literature research and selection process.

Fig. 1

2.2 Study inclusion and exclusion criteria

2.2.1 Inclusion criteria

1. Different activity levels: Low, Moderate, and High. 2. CO2 concentration and the tools used to measure CO2 (Fig. S2). 3. Interventions: All types of masks (N95 masks, surgical masks) with images of PFRs worn by participants (Fig. S1). 4. Subjects: No restrictions on age, gender, or medical history. 5. Fully peer-reviewed research. 6. Language: English.

2.2.2 Exclusion criteria

Our exclusion criteria: The exclusion criteria for our study involved the exclusion of studies that did not fall within the scope of our investigation, specifically focusing on N95 masks and surgical masks. Additionally, articles in the format of short reports or letters to the editor were excluded. Furthermore, studies lacking substantial information regarding mask usage both pre- and post-activity were also excluded from our analysis.

2.3 Article selection

Two authors (M.N and P.B) independently screened all articles in duplicate. Eligible articles were selected based on title and abstract, adhering to the inclusion and exclusion criteria. If the title and abstract did not provide sufficient detail, the full text was reviewed. A rigorous second round of screening was conducted by M.N and P.B, with discrepancies resolved through oral discussion and consensus with M.S.H and M.SH. Endnote reference manager software was used to collect and organize search outcomes and remove duplicates.

2.4 Data extraction

Detailed information on study characteristics such as study ID, study country and city, study design, number of participants, and their characteristics (age, sex, body mass index (BMI ± SD)) was extracted by M.N and P.B. All relevant data are summarized in Table 1, with detailed extraction sheets prepared for each study location and measurement point.Table 1 Summary of characteristics of studies included in this systematic review and meta-analysis.

Table 1Study ID	Study design	Country/City	Measurement method CO2	Types of masks	BMI (kg/m2)
± SD	Level of activities	age	Subject group	NO subjects	male	female	
[23]	Randomized crossover study	USA/Baltimore	Transcutaneous	Surgical mask	NR	Low	NR	Healthy healthcare worker	12	1	11	
[24]	Randomized crossover study	Germany/Tübingen	Transcutaneous	Surgical mask
N95,	23.7 ± 2.4	Moderate
High	38.2	Healthy subjects	39	20	19	
[25]	Randomized crossover study	China/Chongqing	Partial pressure end-tidal carbon dioxide	Surgical mask,
N95	21 ± 3	Moderate
Low
,High	34	Healthcare workers	12	6	6	
[26]	Prospective Cohort Study	Turkey/Izmir	Partial pressure end-tidal carbon dioxide	Surgical mask,
N95	NR	Moderate	24	Emergency healthcare workers	153	NR	NR	
[27]	Prospective Cohort Study	Turkey/Izmir	Partial pressure end-tidal carbon dioxide	Valve N95 masks
and	NR	Low moderate	25	Emergency healthcare workers	54	28	26	
[28]	Randomized crossover study	China/Guangzhou	Partial pressure end-tidal carbon dioxide	Surgical mask	21.46 ± 2.75	Low	28	Healthy group	71	35	36	
[29]	Randomized Controlled Trial	Taiwan/Kaohsiung	Partial pressure end-tidal carbon dioxide	Surgical mask,
N95	NR	Moderate
High	41	Healthcare workers	34	27	7	
[30]	Prospective Cohort Study	Poland/Gdansk	Partial pressure end-tidal carbon dioxide	N95	NR	High	28	Healthcare workers	110	36	74	
[31]	Randomized crossover study	Italy/Milan	Partial pressure end-tidal carbon dioxide	Surgical mask,
N95	NR	Low
Moderate
High	41	Healthy subjects	12	6	6	
[32]	Prospective Cohort Study	Italy/Rome	Partial pressure end-tidal carbon dioxide	Surgical mask	18.85 ± 1.25	Moderate
High	9	Healthy children	22–25	NR	NR	
[33]	Prospective Cohort Study	Italy/Rome	Partial pressure end-tidal carbon dioxide	N95 without (WT) and with exhalation (WE) valve	15.46 ± 2.19	Moderate
High	8	Healthy children	11	5	6	
[34]	Randomized crossover study	Australia/Vien	Partial pressure end-tidal carbon dioxide	N95 with and without exhalation valve	NA	High	28	Emergency medical service providers	48	44	4	
[35]	Experimental study	Australia/Innsbruck	Partial pressure end-tidal carbon dioxide	N95 with an exhalation valve	NR	Low
High	30	Health care worker	1	.	1	
[36]	Experimental study	Turkey/Antalya	Partial pressure end-tidal carbon dioxide	Surgical mask	26.55 ± 3.24	Moderate	41	Healthy volunteers	100	42	58	
[37]	Cross-sectional study	Saudi Arabia	Partial pressure end-tidal carbon dioxide	N95	NR	High	35	Healthcare Providers	43	6	37	
[22]	Prospective Cohort Study	Korea/Incheon	Partial pressure end-tidal carbon dioxide	N95	NR	Low	68	Patients	90	85	5	
[1]	Randomized crossover study	Israel/Haifa	Partial pressure end-tidal carbon dioxide	Surgical mask, N95	28.72 ± 3.78	Low
Moderate
High	34	Healthy volunteers	16	16	.	
[38]	Randomized crossover study	USA/Florida	Partial Pressure end-tidal carbon dioxide	surgical mask	NR	High	24	Instructor pilots	32	26	6	
[39]	Cross-sectional study	Singapore/Singapore	Partial pressure end-tidal carbon dioxide	N95,	NR	Low	30	Healthcare-workers	154	51	103	
[40]	Randomized clinical trial	Singapore/Singapore	Partial pressure end-tidal carbon dioxide	N95 without and with exhalation valve	19.1 ± 5.4	Low
Moderate	NR	Healthy children	106	59	47	
[41]	Experimental study	USA/Pittsburgh	Transcutaneous	N95,	24.1 ± 2.8	Moderate
High	23	Healthy subjects	6	6	.	
[42]	NR	USA/Pennsylvania	Transcutaneous	N95,	24.9 ± 2.3	Moderate	24	Healthy subjects	12	NR	NR	
[43]	NR	USA/Pennsylvania	Partial pressure end-tidal carbon dioxide	N95	25.4 ± 3.2	Moderate
High	26–28	Nonpregnant and pregnant subjects	22	.	22	
[44]	NR	USA/Merced	Transcutaneous	N95	23.4 ± 2.9	Low
Moderate
High	24	Healthy subjects	10	7	3	
[45]	NR	USA/Pittsburgh	Transcutaneous	N95	25.4 ± 4.2	Low
Moderate
High	23	Healthy subjects	20	13	7	
[46]	NR	USA/Pittsburgh	Transcutaneous	Surgical mask	25 ± 4.1	Low	23	Healthy subjects	20	13	7	
[20]	NR	USA/Pittsburgh	Partial pressure end-tidal carbon dioxide	N95	26.4	Moderate
High	25	Healthcare-workers	10	3	7	
[18]	NR	USA/Pittsburgh	Partial pressure end-tidal carbon dioxide	N95, surgical mask	26.4	Moderate
High	25	Healthcare-workers	10	3	7	
[2]	Randomized crossover study	Germany/Leipzig	Partial pressure end-tidal carbon dioxide	N95	24.5 ± 2	Moderate
High	38	Medical staff	12	12	.	

2.5 Statistical analysis

To understand the impact of different mask types (no mask, N95 masks, and surgical masks) on CO2 concentrations, a meta-analysis was performed using Stata 17 software (https://www.stata.com). Due to the diverse variables in mask and activity types, data were categorized accordingly, and a random-effects meta-analysis model was employed as a conservative approach. Funnel plots were used to evaluate publication bias. Mean differences (MDs) with 95 % confidence intervals (CIs) were calculated to analyze mask performance on CO2 concentration. Sensitivity analyses were conducted by removing one study at a time to ensure robustness of findings. A p-value <0.05 was considered statistically significant. Statistical heterogeneity was represented by I2 values, classified as low (0–25 %), moderate (26–50 %), substantial (50–75 %), and high (>75 %) [47].

2.6 Risk of bias assessment

The risk of bias (RoB) was assessed independently by M.N and P.B across five domains: 1) bias from the randomization process, 2) bias due to deviations from intended intervention, 3) bias due to missing outcome data, 4) bias in measurement of the outcome, and 5) bias in selection of the reported result [47,48]. The RoB was conducted independently by two authors (M.N and P.B), and discrepancies were resolved through discussion and consensus with M.S.H and M.SH. The RoB for included studies was assessed using the Cochrane Collaboration's RoB2 online tool (RoB2 tool, https://mcguinlu.shinyapps.io/robvis/). The Cochrane Collaboration's tool for assessing risk of bias is a comprehensive and systematic approach used in evaluating the quality of studies included in systematic reviews and meta-analyses. This tool, often referred to as the RoB tool, helps reviewers identify potential biases in the design, conduct, and reporting of randomized trials.

3 Results

3.1 Study selection and characteristics of the included studies

A systematic search yielded 6798 studies, with 1935 duplicates removed. We then excluded 4863 studies based on a simple screening of the title, abstract, or full text of the article. Based on the inclusion and exclusion criteria (Fig. 1), the remaining 129 articles underwent a detailed full-text review. Sixty articles were excluded due to unsuitable data for analysis; specifically, some did not specify a unit for CO2, while others measured CO2 without detailing the activity, making categorization impossible. Fifteen articles were excluded because they did not provide the required type of mask; for instance, some mentioned N95 or surgical masks in the title, but the method of checking CO2 or the mask shape differed from the standard. Additionally, 9 of the extracted articles were review articles, 5 were commentaries, 8 were only abstracts, and 3 used different units. Thus, 29 articles were excluded in total.

The studies included were conducted on various masks such as N95 (FFP1, FFP2, Moldex 2200, Moldex 2300, 3M 9210, 3M 9211) and surgical masks. Detailed information on the masks worn by the participants was extracted. Most participants were healthcare workers [2,20,23,25,29,30,35,37,39], followed by healthy volunteers [1,24,28,36,41,42,[44], [45], [46], [49]], emergency health care workers [26,27,34], healthy children [32,33,40], instructor pilots [38], pregnant [43] and patients [22].

Most of the studies were conducted in the United States [18,20,23,38,[41], [42], [43], [44], [45], [46]], and the other studies were conducted in Australia [34,35], Germany [2,24], China [24,28], Occupied Palestine [1], Italy [[31], [32], [33]], Korea [22], Saudi Arabia [37], Singapore [39,40], Taiwan [29], Turkey [26,27,36], Romania (Fig. 2). Approximately 81 % of the included studies were published between 2016 and 2023 [1,2,22,[23], [24], [25], [26], [27], [28], [29], [30], [31], [32], [33], [34], [35], [36], [37], [38], [39], [40], [41], [42]] (Fig. 3). The distribution of studies across various countries is acknowledged in this research. However, the primary criterion for selecting studies was the availability and relevance of data, rather than a deliberate focus on specific regions. This approach was adopted to ensure the inclusion of the most pertinent and high-quality data, regardless of geographic origin. Nonetheless, it is important to recognize that this methodology introduces certain limitations, particularly concerning the generalizability of the findings. Given that the selected studies may not represent a globally balanced sample, caution must be exercised when extrapolating the results to populations in different regions or countries. Cultural, socioeconomic, environmental, and healthcare system differences can significantly influence study outcomes. Therefore, while the findings provide valuable insights, they may not be universally applicable across all demographic groups or geographic locations. Notably, according to the graph shown, mask-wearing trended upward from 2019 to 2022, suggesting that the prevalence of Covid-19 disease peaked during this period [50]. The gender profile of the participants comprised approximately 47 % (541) female and 53 % (606) male. About 78 % of the studies measured CO2 in "exhaled breath" and 'transcutaneous'. Additionally, some studies measured the amount of carbon dioxide in the blood of people wearing masks during various activities (Fig. 4).Fig. 2 The number of publications conducted in different countries.

Fig. 2

Fig. 3 The number of studies conducted in different years.

Fig. 3

Fig. 4 The number of carbon dioxide measurement studies conducted at various zone.

Fig. 4

3.2 Physiological Outcome (CO2 concentration)

Given the diversity of activities studied, a decision was made to categorize them into three levels: low, medium, and high. This categorization was necessitated by several factors, including the wide range of activities chosen and the varying durations of these activities. For instance, one study measured CO2 levels in cyclists at different speeds and times, and included periods without cycling as a baseline for comparison. Another study assessed CO2 levels during activities involving three different treadmills or gym equipment, such as stationary bicycles. The high variability in the types and durations of activities presented a significant challenge in analyzing and comparing data consistently. Therefore, categorizing activities into low, medium, and high levels allowed for a more systematic approach. This classification aimed to standardize the activities based on their intensity and duration, facilitating more accurate comparisons and analyses. By categorizing the activities into these three levels, the study aimed to account for the significant variations in physical exertion and duration, allowing for a more structured and meaningful analysis of the data. This approach also enhances the clarity and comparability of the results, providing a clearer understanding of how different activity levels impact variables such as CO2 levels. This activities include low, medium and high, and each mask (No mask, surgical masks and N95 masks) according to the level the activity was analyzed and it is as follows.

3.2.1 No mask

Among the present studies, 11 studies [1,2,22,28,34,39,40,42,43,45,46] considered the state without a mask, examining CO2 concentration in the body compared to wearing a mask at different activity levels. As shown in Fig. 5, the meta-analysis results indicate that the CO2 concentration without a mask in these studies was (37.91 mmHg, 95 % CI 36.46 to 39.35; p = 0.08, I2 = 20.47 %, T2 = 1.08, H2 = 1.26)Fig. 5 Meta-analysis of the effects of no-mask on CO2 concentration. The mean difference estimates (95%CIs) are shown for no-mask.

Fig. 5

3.2.2 N95 (low, moderate, and high)

Due to the great variety of activities, all activities were categorized into three levels: low, moderate, and high. The most common parameter analyzed was N95-moderate activity, reported in 16 articles [1,2,18,20,24,27,29,31,33,[39], [40], [41], [42], [43], [44], [45]], followed by N95-high activity in 13 articles [1,18,20,25,30,[33], [34], [35],37,41,[43], [44], [45]], and N95-low activity in 10 articles [1,22,23,25,27,31,35,40,44,45] (Fig. 6) The meta-analysis showed no significant differences in CO2 concentration for N95-low activity (36.83 mmHg, 95 % CI 33.57 to 40.10; p = 0.03, I2 = 47.68 %, T2 = 12.34, H2 = 1.91)(Fig. 6A), N95-moderate activity (37.85 mmHg, 95 % CI 36.51 to 39.20; p = 0.36, I2 = 2.43 %, T2 = 0.19, H2 = 1.02) (Fig. 6B), and N95-high activity (39.51 mmHg, 95 % CI 38.00 to 41.02; p = 0.23, I2 = 0.00 %, T2 = 0.00, H2 = 1.00) (Fig. 6C) Although an increasing trend in CO2 concentration from low to high is visible, it is not significant.Fig. 6 Meta-analysis of the effects of wearing mask plus activity on CO2 concentration. The mean difference estimates (95%CIs) are shown for N95 in low (A), moderate (B), and high (C) activity.

Fig. 6

3.2.3 SM (low, moderate, and high)

The most common parameter analyzed was SM-high activity, reported in 8 articles [2,24,25,28,29,31,32,38], followed by SM-moderate activity was reported in 6 articles [1,23,25,26,32,36], and SM-low activity was reported in 6 articles [1,23,25,28,31,46].

As shown in Fig. 7 In the meta-analysis, considering that the amount of activity is increasing, the amount of CO2 is decreasing in those wearing SM-low activity (38.31 mmHg, 95 % CI 34.48 to 42.14; p = 0.01, I2 = 53.88 %, T2 = 10.06, H2 = 2.17) (Fig. 7A), SM-moderate activity (35.05 mmHg, 95 % CI 35.12 to 38.97; p = 0.66, I2 = 0.00 %, T2 = 0.00, H2 = 1.00) (Fig. 7B), and SM-high activity (36.07 mmHg, 95 % CI 34.18 to 37.96; p = 0.17, I2 = 8.24 %, T2 = 0.67, H2 = 1.09) (Fig. 7C).Fig. 7 Meta-analysis of the effects of wearing mask plus activity on CO2 concentration. The mean difference estimates (95%CIs) are shown for SM in low (A), moderate (B), and high (C) activity.

Fig. 7

3.2.4 N95-WE & WT

To test the effects of N95 masks with and without valves, we classified N95 masks into two groups and performed a meta-analysis. As shown by the results Figure S3 (A and B), WE-CO2 in participants (35.82 mmHg, 95 % CI 32.89 to 38.75; p < 0.05, I2 = 60.62 %, T2 = 9.26, H2 = 2.54) (Fig. S3A), and in the WT, the amount of CO2 is (38.45 mmHg, 95 % CI 37.10 to 39.81; p = 0.48, I2 = 0.00 %, T2 = 0.00, H2 = 1.00) (Fig. S3B).

The results clearly show an increase in CO2 from the valve state to the valve-less state, likely due to dead space created in the mask gap in the valve-less state.

3.3 Results of bias assessment and publication bias

Based on the Cochrane Collaboration's online tool (https://mcgui nlu.shinyapps.io/robvis/), twenty studies were assessed as having “low” or “some concerns” risk of bias, while 9 studies had a “high” risk of bias (Fig. S4). Figs. S5–S8 reveal the funnel plot tests for the no-mask, N95 masks, surgical masks, N95-WT, and N95-WE respectively. Funnel plots revealed no publication bias for N95-low activity (Fig. S7A), except for N95-moderate activity (p-value = 0.36) (Fig. S7B), and N95-high activity (p-value = 0.23) (Fig. S7C), surgical mask no sign of publication bias for SM-low activity (Fig. S6A), except for SM-moderate activity (p-value = 0.66) (Fig. S6B), and SM-high activity (p-value = 0.17) (Fig. S6C), and for N95-WE (Fig. S8A) without sign of publication bias, except for N95-WT (p-value = 0.48)(Fig. S8B). Heterogeneity was not found for N95-high activity (Fig. 6C), N95-WT (Fig. S3B), SM-moderate activity (Fig. 7B), and T2 and I2 index were 0.0 and 0 %, respectively.

3.4 Sensitivity analyses

No notable changes were observed in pooled estimates after removing studies one by one (Table S3). However, for no-mask [2,22,39,40,43] and N95-high activity [1,33,34] when the influential study was added the pooled estimates were statistically significant so that the heterogeneity indices had increased, (40.21 mmHg 95 % CL 38.14 to 42.27, p = 0.2, T2 = 2.65, I2 = 21.83 % and H2 = 1.28), (40.36 mmHg 95 % CL 38.00 to 42.73, p = 0.19, T2 = 4.40, I2 = 26.74 %, and H2 = 1.37) and (40.24 mmHg 95 % CL 38.17 to 42.30, p = 0.21, T2 = 2.60, I2 = 21.62 % and H2 = 1.27) and for no-mask when the influential study was removed the pooled estimates were statistically significant so that the heterogeneity indices had decreased (38.12 mmHg 95 % CL 36.80 to 39.44, p = 0.09, T2 = 0.67, I2 = 14.79 % and H2 = 1.17), (37.63 mmHg 95 % CL 37.58 to 39.67, p = 0.07, T2 = 0.00, I2 = 0.00 %, and H2 = 1.00), (38.66 mmHg 95 % CL 37.68 to 39.64, p = 0.51, T2 = 0.00, I2 = 0.00 % and H2 = 1.00), (38.37 mmHg 95 % CL 37.23 to 39.51, p = 0.09, T2 = 0.24, I2 = 5.52 %, and H2 = 1.06) and (38.30 mmHg 95 % CL 37.10 to 39.5, p = 0.09, T2 = 0.35, I2 = 7.98 % and H2 = 1.09) respectively. Moreover, for Surgical mask-low activity when two influential studies by Refs. [25,46] and for N95 with an exhalation valve (WE) the influential study of [35] removed the heterogeneity indicates reduced (41.59 mmHg 95 % CL 39.95 to 43.09, p = 0.64, T2 = 0.00, I2 = 0.00 % and H2 = 1.00), (35.72 mmHg 95 % CL 31.99 to 39.45, p = 0.37, T2 = 1.83, I2 = 8.73 %, and H2 = 1.10) and (36.69 mmHg 95 % CL 34.41 to 38.98, p = 0.19, T2 = 1.43, I2 = 14.60 % and H2 = 1.17), respectively.

4 Discussion

Our study represents a pioneering effort, being the first systematic review and meta-analysis to comprehensively investigate the impact of wearing masks, specifically N95 and surgical masks, on carbon dioxide (CO2) concentration during diverse activities in healthy individuals. Through an exhaustive search across prominent English databases, we identified 29 articles meeting our rigorous inclusion criteria, providing a robust foundation for our analysis of physiological outcomes associated with mask use. Our findings shed light on the immediate and progressive effects of face mask usage on CO2 concentration, with N95 masks exhibiting a more pronounced impact compared to surgical masks [[51], [52], [53]]. While our meta-analysis primarily focused on the general impact of N95 and surgical masks on CO2 concentration, it is crucial to contextualize these findings within the broader landscape of the COVID-19 pandemic. The increased utilization of masks since 2019 signifies a societal shift towards prioritizing protection, contributing to sustained mask usage even beyond the pandemic [50,54] However, the significance of this trend in CO2 emissions from surgical masks and the transition to N95 masks remains elusive. This could be attributed to the potentially lower exchange rate of N95 masks with the external environment, leading to increased dead space and subsequently higher CO2 concentrations compared to surgical masks [55]. In our study, we categorized activities into low, medium, and high levels due to the diverse nature of activities chosen across studies. Activities such as cycling at different speeds [1,24], piloting at various altitudes [38], and treadmill exercises were among those studied, necessitating the categorization for analysis [20,42,44,46], Our findings revealed a lack of statistically significant disparity in CO2 concentration during activities between mask utilization and non-utilization. However, sensitivity analysis highlighted the potential influence of specific studies on observed trends, indicating a discernible decreasing trend in CO2 concentration upon the exclusion of certain studies in the absence of masks, and conversely, an elevation in CO2 concentration with the inclusion of specific studies in N95 mask deployment during high-activity scenarios. Regarding the long-term physiological effects of wearing masks, our study suggests that while there may be changes in CO2 concentration, it is uncertain whether these effects pose significant health risks. Some studies have reported changes in oxygen levels and heart rate, with implications for headaches or dizziness, but the overall safety of mask-wearing appears affirmed in healthy individuals [38,56]. In summary, our systematic review and meta-analysis offer a comprehensive exploration of the impact of mask-wearing on CO2 concentration during various activities. The observed trends underscore the importance of considering specific mask types, activities, and individual variations in interpreting physiological effects. While further intervention studies across diverse populations and environmental conditions are warranted to better understand the long-term effects of mask-wearing, our study contributes valuable insights to guide public health practices amidst ongoing global challenges such as the COVID-19 pandemic.

4.1 Limitations and recommendations for future research

In this section, we address the limitations encountered during the review process and provide recommendations for future research. We highlight two main points.

4.1.1 Categorization for meta-analysis

The diverse range of activities and types of masks introduced methodological challenges that necessitated categorization for the purposes of meta-analysis. Activities varied widely, from cycling to treadmill exercises, while the types of masks included N95 and surgical masks. This diversity introduced significant heterogeneity into the data, making direct comparisons difficult without appropriate categorization. For instance, in some studies, activities were measured over various time intervals (e.g., 4 h, 5 h, and 9 h) with and without rest periods, and involved different models of N95 masks. Activities in these studies were categorized from low to high based on the duration of the activity. In another study, cycling was tested with the intensity of cycling adjusted from 0 % to 100 %. For the purposes of our analysis, we classified 0–20 % intensity as low activity, 20–50 % as medium activity, and 50–100 % as high activity [1,27]. This systematic approach to categorization allowed us to manage the data's complexity and draw more meaningful conclusions. Future studies should consider examining specific types of activities and masks independently to provide more precise insights. Additionally, focusing on specific demographic groups, such as healthcare workers who frequently engage in various activities in medical settings, could offer valuable real-world scenarios. This targeted approach would not only enhance the accuracy of the findings but also improve their applicability to specific occupational or situational contexts, ultimately leading to better-informed guidelines and practices for mask usage during different activities.

4.1.2 Database limitations

Despite exhaustive efforts, our review faced limitations due to restricted access to certain databases, preventing us from retrieving some paid articles. This exclusion may have impacted the comprehensiveness of our findings and introduced potential biases. Specifically, restricted access to high-quality, pay walled studies meant potentially valuable data were left out, possibly skewing our results. Additionally, varying formats and limited search functionalities of different databases further hindered data extraction. To address these limitations in future research, several strategies could be employed. Collaborating with research institutions or libraries can provide broader access to paid articles. Utilizing alternative search methodologies, such as systematic manual searches or citation tracking, can help identify relevant studies that automated searches might miss. Expanding the search to include additional databases and utilizing open-access repositories and preprint servers can also enhance the completeness of the review. Engaging with academic networks and directly contacting authors for access to their publications can further circumvent access issues. By implementing these strategies, future studies can achieve more comprehensive and unbiased findings, better reflecting the full scope of existing research.

We acknowledge that these limitations may have affected the robustness of our findings and underscore the importance of transparency and methodological rigor in future research endeavors. By addressing these limitations and implementing rigorous methodologies, researchers can enhance the quality and reliability of evidence in the field of mask-related research.

5 Conclusions

Our study provides a comprehensive analysis of the impact of different mask types on CO2 concentrations during various activities, contributing significant insights to public health practices. This investigation is especially relevant amidst the COVID-19 pandemic, which has heightened the need for effective and safe mask usage. The analysis of 29 rigorously selected articles revealed specific trends in CO2 concentrations based on mask type and activity level. Notably, while N95 masks showed an observable but statistically non-significant increase in CO2 levels from low to high activity, surgical masks demonstrated a decreasing trend in CO2 concentrations as activity levels escalated. These patterns suggest that the type of mask and the intensity of physical activity play critical roles in influencing CO2 accumulation. Our findings have practical implications for mask-wearing scenarios in real-world settings. For instance, the higher CO2 levels observed in N95 masks without exhalation valves compared to those with valves highlight the importance of mask design in respiratory dynamics. This suggests that masks with exhalation valves may be more suitable for high-intensity activities due to their potential to lower CO2 buildup, thereby enhancing comfort and potentially improving compliance with mask-wearing guidelines. In terms of quantitative results, the meta-analysis showed that CO2 concentration for N95-low activity was 36.83 mmHg (95 % CI 33.57 to 40.10), N95-moderate activity was 37.85 mmHg (95 % CI 36.51 to 39.20), and N95-high activity was 39.51 mmHg (95 % CI 38.00 to 41.02). For surgical masks, the CO2 concentration for low activity was 38.31 mmHg (95 % CI 34.48 to 42.14), moderate activity was 35.05 mmHg (95 % CI 35.12 to 38.97), and high activity was 36.07 mmHg (95 % CI 34.18 to 37.96). These specific values underscore the variability in CO2 levels depending on mask type and activity intensity. Although the statistical significance of these trends may not always be clear, the observed changes in CO2 concentration are crucial for understanding the physiological effects of different masks. Such insights can inform recommendations for mask selection and usage, particularly for individuals engaged in varying levels of physical activity. For example, healthcare workers and other professionals who perform strenuous tasks may benefit from using masks designed to minimize CO2 accumulation. In conclusion, while the study underscores the nuanced relationship between mask type, activity level, and CO2 concentrations, it also points to the need for further research. Future studies should aim to isolate specific activities and mask types to provide more detailed insights. Additionally, considering the diverse demographic and environmental contexts in which masks are used will enhance the generalizability and applicability of these findings. By refining our understanding of how masks influence CO2 levels during different activities, we can better inform public health guidelines and ensure the safe and effective use of masks in various settings.

Consent for publication

All authors provided written informed consent to publish this study.

Funding

Not applicable.

Additional files

Not applicable.

Other information

*Review was not registered.

*Protocol was not prepared.

Data availability statement

Has data associated with your study been deposited into a publicly available repository? NO.

This information has not been made available to anyone, according to the opinion of the author of the article.

CRediT authorship contribution statement

Mahdi Nassri: Writing – review & editing, Writing – original draft, Software, Resources, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Parviz Barari: Data curation, Conceptualization. Mohammad Khanizadeh: Validation, Data curation, Conceptualization. Sasan Faridi: Supervision, Software. Mansour Shamsipour: Writing – review & editing, Writing – original draft, Software. Kazem Naddafi: Writing – review & editing. Sadegh Niazi: Writing – review & editing. Mohammad Sadegh Hassanvand: Writing – review & editing, Writing – original draft.

Declaration of competing interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:Mohammad sadegh hassanvand reports statistical analysis was provided by Tehran University of Medical Sciences. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix A Supplementary data

The following is the Supplementary data to this article.Multimedia component 1

Multimedia component 1

Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.heliyon.2024.e36626.
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