A New Method Proposed for the Estimation of Exposure to Atmospheric Pollution through the Analysis of Black Pigments on the Lung Surface

Abstract

Megacities can be considered excellent laboratories for studying the effects of the urban environment on human health. Typically, exposure to pollution is estimated according to daily or annual averages of pollutant concentrations, collected at monitoring stations, using satellite data for remote sensing of pollutant levels, considering proximity to major roads, or through personal exposure monitoring with portable sensors. However, these approaches fall short in identifying individual exposure values over a lifetime. It is well established that individuals living in large urban areas inhale atmospheric particles containing carbonaceous components, resulting in the deposition of black pigments in lung tissue, known as black carbon or anthracosis. This study aims to detail the procedures for assessing the deposition of such pigments, which serve as an estimate of an individual’s exposure to atmospheric pollution particles. Data collection involves administering detailed questionnaires and capturing lung images in the autopsy room. The analysis is based on macroscopic quantification of black pigments, supplemented by an evaluation of personal habits and the clinical histories of the individuals. This method of estimating lifetime exposure to inhaled particles provides a valuable tool for understanding the correlation between urban living and its potential health effects.

1. Introduction

Air pollution, a major environmental health concern, significantly impacts respiratory health by introducing particulate matter (PM) into the lungs. Historical evidence shows that the lungs of exhumed Egyptian mummies were blackened by anthracotic pigments, resulting from domestic biomass combustion, which was a primary source of hazardous anthropogenic air pollution at that time [1,2]. Nowadays, with significant urban growth, key determinants of air pollution include emissions from vehicles, industrial activities, and construction. The inhalation of PM emitted by these sources results in black carbon deposits (also referred to by pathologists as anthracosis), which can serve as a primary constituent in the black patches observed in lung samples and may act as a proxy for the complex mixture of airborne contaminants present in urban atmospheres [3].

PM is categorized into three types based on size: PM10 (particles with a diameter of 10 μm or less), PM2.5 (particles with a diameter of 2.5 μm or less), and ultrafine particles (UFPs, with a diameter of 0.1 μm or less). PM10 can penetrate the upper respiratory tract, PM2.5 can reach the lower respiratory tract, and UFPs can cross the alveoli–capillary barriers, enter the bloodstream, and affect various organs. Thus, the hazardous effects of airborne particulates extend to all physiological systems [4,5].

Monitoring and assessing air quality and its impact on human health typically involve complex and costly instrumentation. While external proxy measurements, such as those based on proximity to major roads [6], satellite observations [7], and stationary monitoring stations [8] are useful, they fall short of accurately capturing an individual’s exposure to inhaled PM. Personal factors and activity patterns play a significant role in this variability. Innovative approaches now leverage biological markers, providing complementary and relatively cost-effective means to monitor air quality and estimate exposure levels [9,10]. Carbon loading within macrophages, for instance, serves as a valuable biomarker for assessing black carbon-related PM deposition in the respiratory airways. This biomarker reflects external exposure and may be suited for evaluating cumulative personal exposure [11,12,13].

However, understanding the long-term accumulation of the inhaled particles in human lung tissue is crucial for assessing the cumulative effects of chronic exposure to polluted air. The autopsy scenario represents a valuable approach for obtaining samples [14,15,16] to address this question [17,18]. While the primary purpose of autopsies is to determine the cause of death, they also offer a unique opportunity to study disease processes. In megacities, where environmental pollutants abound, autopsies serve as large laboratories, allowing researchers to explore and understand the impact of these environmental pollutants on health.

Building upon this understanding, this study presents a noteworthy methodology for estimating lifelong exposure to urban air pollution. By utilizing a step-by-step procedure, this method aims to provide a replicable assessment of the accumulation of airborne particles in the lungs, taking into account personal habits and sociodemographic factors. The macroscopic quantification of black carbon pigment deposition on the lung surface is a valuable tool for estimating individuals’ exposure to environmental particles throughout their lifetime in a megacity. Furthermore, it contributes to studies that investigate the intricate relationship between air pollution and health outcomes.

2. Design and Method

2.1. Setting

Before initiating the research, all protocols were approved by the relevant institutional review board. The current protocol has been approved by the Research Ethics Committee of the University of Sao Paulo and complies with federal requirements for research involving human subjects.

The data collection step took place in an autopsy service environment. Our research setting was the Death Verification Service of São Paulo (SVOC). SVOC is essential for providing death reports for a large number of deaths in Sao Paulo, performing approximately 15,000 autopsies per year, corresponding to 15–20% of all natural deaths in the city [19]. Additionally, SVOC serves as a conducive environment for academic activities, including research led by University of Sao Paulo researchers. The facility is well-equipped and supports a wide range of scientific investigations.

The initial step of the protocol was quite sensitive. Creating a compassionate environment for obtaining consent from the next of kin (NOK) and conducting interviews with them was crucial, especially given the context of pain and mourning. Interviewers were trained in advance to conduct appropriate conversations with the NOK. To minimize potential harm or undue distress, consent processes were centered on the participant/NOK and were highly individualized. Informed consent was obtained by providing detailed information about the study’s purpose, procedures, and potential risks and benefits, ensuring that the NOK fully understood and voluntarily agreed to participate. Beyond the challenging moment the NOK experiences, perceptions of benefit and harm related to post-mortem tissue donation or research approaches also influence rates of study participation and adherence [20,21].

Autopsies are conducted in four daily shifts, operating 24 h a day. It was necessary that the collection of research samples did not disrupt SVOC’s routine. Therefore, research collections for our study occurred at a frequency of 2–3 days per week, ensuring minimal interference with regular operations.

Therefore, given the complexity of professional work in case selection and the structured nature of the autopsy service, large-scale sampling within a single day was naturally constrained. However, if a research team were available daily and the autopsy service permitted research activities during all shifts, a significant number of samples could be collected efficiently, thereby enhancing the overall research output.

After highlighting these important considerations about the research environment, the necessary facilities for this type of research included two areas within the autopsy service environment and a third area that might be external to that place:

• (1). Private interview room: Face-to-face interviews with the NOK occurred in this private space, providing a comfortable and confidential setting.

• (2). Autopsy room: This room was used for capturing images of the lungs (and for tissue collection when appropriate for the study design). The collection team collaborated with technicians and pathologists to ensure accurate data.

• (3). Office: An area designated for data analysis, archiving research documents, and coordinating various aspects of the study, located outside the autopsy service. In our research, this area was situated within the university.

2.2. Materials

The materials necessary for all the procedures are listed below.

For steps such as questionnaire application, evaluation of eligible cases, and organization and analysis of data obtained from the questionnaire and death certificates, the following were necessary:

• computer;

• printer;

• copy paper;

• spreadsheet editor (i.e., Excel);

• statistical program (i.e., SPSS);

• ImageJ program (NIH Image).

For the acquisition of lung images used for the measurement of black carbon fraction:

• high-resolution camera (with charged battery);

• glass Petri dishes (90 mm or 100 mm);

• gauze;

• personal protective equipment (PPE): long-sleeved fluid-repellent gown, gloves, safety googles, disposable bouffant caps, waterproof knee-high safety boots

Note: If the optional step of quantifying black particles in histological sections of the lung near the pleural surface is followed, additional materials are necessary for sample collection, such as histology cassettes, pencil, tweezers, scalpel with blade, biopsy vials or collector jars, and a 10% formalin solution. Histological tissue processing requires equipment and reagents typical of a common histopathological laboratory. This step was not utilized in the majority of our studies and is not detailed in this manuscript.

2.3. Procedure

A summary of the four basic procedures used in this analysis is depicted in Figure 1. The detailed steps, with an emphasis on the pathological aspects of the developed method, are described in the following sections. Important notes are included throughout the protocol steps to highlight key details.

2.3.1. Stage 1—Interview for Collecting Sociodemographic and Personal Data

1. The autopsy service front desk employees receive the NOK and, as soon as the authorization for the body to proceed to the autopsy is registered, inform the inter-viewer of the research procedure;

2. Before the autopsy procedure, the interviewer must appropriately approach the NOK. The objective of the study and a summary of the procedures must be explained to the NOK to obtain their consent for providing information and collecting images/samples from all deceased subjects intended to be included in the study;

3. The interviewer should administer a questionnaire to the NOK to collect reliable and complete information about the deceased. The questionnaire should include questions about residential addresses, duration of residence in the city, occupation, daily commuting, smoking status (including the occurrence of environmental tobacco smoking in the residence), and chronic comorbidities;

Note: Other specific information can be obtained from the NOK, depending on the objectives of the study. Furthermore, basic information such as age, gender, weight, and height can be obtained from the SVOC autopsy register and should be included in the profile of the subject of the study.

• 4.. After completing this part of the protocol, the collected data must be registered in a study database, allowing future export to a spreadsheet editor for analysis of all subjects. At this point, if the information obtained is complete, it is possible to confirm the eligibility of the case for proceeding in the study;

• 5.. The interviewer must then communicate the case identification number to the team responsible for collecting lung images (and potentially tissue samples) during the autopsy procedure. Then, proceed to the pathological step of the protocol;

Note: After the autopsy procedure, additional details about prior diseases documented in the pathologist’s report should be collected, as they are crucial for constructing the subject’s profile, especially in studies that aim to correlate exposure to airborne particles with health outcomes. This note has been included at this point of the procedure because the interviewer may be responsible for requesting the report and entering the information into the study database.

2.3.2. Stage 2—Collection of Pathological Data from Lung Images

• 6.. Put on the necessary personal protective equipment (PPE) for entering the autopsy room. Proceed to the autopsy room with the basic materials necessary for collecting images;

• 7.. Inform the autopsy technician of the case identification, for them to start the autopsy procedure and provide the lungs from the body;

• 8.. While waiting for the organs, take the first photo of the tag identifying the number of the autopsied body;

• 9.. Upon receiving the lungs, carefully remove excess blood from the lung surface with water and wipe with gauze;

• 10.. Position the lungs in an antero–lateral view on a bench under good illumination for examination;

Note: During the lung examination, certain alterations can hinder the analysis of black pigments on the lung surface. When significant changes are observed, such as those indicative of pneumonia, severe chronic obstructive pulmonary disease, pulmonary granulomatous diseases, or neoplastic diseases, the case is excluded and images should not be taken. Additionally, cases with severe acute hemorrhagic edema should be excluded due to its impact on the color of the visceral pleura.

• 11.. Position the glass Petri dish (applying light pressure to flatten the lung surface) on the superior lobe of the right lung and take the second picture;

• 12.. Position the glass Petri dish on the inferior lobe of the right lung to take the third picture;

• 13.. Repeat steps 11 and 12 on the left lung to obtain the fourth and fifth pictures, respectively;

Note: Maintain a consistent distance between the Petri dish and the camera lens to ensure uniformity in the images obtained.

• 14.. Check the quality of the photos immediately after taking them. If necessary, repeat the sequence of photos;

Note: It is important to take the same sequence of photos for all cases in the study, ensuring the identification of the specific region of the lung (pulmonary lobes). This consistency allows, for example, the analysis of each lobe separately, which may be relevant to certain study objectives.

• 15.. If tissue collection is required for studies associating exposure to environmental particles with respiratory or other organ/system outcomes, perform this step now and proceed with specific sample processing protocols;

• 16.. Return the lungs carefully to the technician for the standard autopsy procedures;

• 17.. Exit the autopsy room, remove PPE, and proceed to the data analysis office;

2.3.3. Stage 3—Measurements and Analysis of Observed Black Particle Deposition

• 18.. Transfer the photos to the computer and save them in folders labeled with the case identification.

Note: Perform the particle quantification in a blinded manner, ensuring that the examiner has no prior access to information about the deceased individual.

• 19.. Open the ImageJ software(ImageJ https://imagej.net/ij/index.html) (the original ImageJ, NIH Image), which is used to generate the point test system for the images of the four lobes;

• 20.. Open the four images of the lung surfaces collected for the case. Analyze them with the following steps, one at a time;

• 21.. Apply the point system to each image using the following steps: Plugins > Analyze > Grid;

• 22.. Open the “Grid” tab for configuration. Select the following settings: Type: “Crosses”; Area per point: 30,000; select “Bold and Apply.” The result of these steps is demonstrated in Figure 2;

• 23.. Start the point count by going to: Plugins > Analysis > Cell counter.

• 24.. Open the point tools tab. Start counting with counter “0” to quantify the black particles’ points, and switch to counter “1” to count points on tissue without black particles, or vice versa.

• 25.. Considering only the points that fall on the lung surface image covered by the glass Petri dish, count the points over areas with black particles as well as the points over the rest of the lung tissue (Figure 3). The total number quantified by counter “0” and counter “1” is available in the point tool tab.

Note: Some photos may include small areas with reflections from the autopsy room lights. It is important not to consider these areas during the quantification of points on the lung surface. In our reported study, the picture-taking procedure was very quick, and sometimes these reflections were not noticed while checking the image quality.

• 26.. Calculate the proportion of black patches by dividing the number of black patches by the total number of black patches plus the number of clean pleura, to obtain the fraction of black pigments for each lobe. An example is shown in Figure 3;

• 27.. Calculate the mean of the values obtained from the four lobes. Use this final value to estimate the complex mixture of lifelong urban ambient particles retained in the lung;

2.3.4. Stage 4—Data Correlation

• 28.. Transfer the values to the spreadsheet editor for analysis, associating the fraction of black pigments with the personal information obtained from each participant’s questionnaire;

• 29.. Conduct specific statistical analyses in accordance with the study’s objectives.

Note: To evaluate the association between the fraction of black pigments and qualitative (categorical) variables, the Chi-square test of association can be used. The Mann–Whitney test is suitable for comparing the fraction of black pigments between two independent groups, such as men vs. women or smokers vs. non-smokers. Spearman’s correlation coefficient can be calculated to measure the correlation between the fraction of black pigments and quantitative variables. Additionally, the use of multiple regression models to assess the amount of black deposits in the lungs while adjusting for different variables of interest is recommended. These suggested analyses were employed by our research group, and other complementary analyses can be carried out as well. The participation of a statistical researcher is fundamental and very beneficial for conducting coherent and appropriate analyses.

3. Discussion

This study presents a novel methodology to estimate lifelong retention of airborne particles in lung tissue by quantifying black pigments as key indicators of urban air pollution. Conducted at an autopsy service linked to a research institution, this approach combines pathological and epidemiological methods. Data collection involved obtaining next-of-kin consent, administering detailed face-to-face questionnaires, and capturing lung images in the autopsy room. Using ImageJ software, a point test system was applied to those images to quantify black particles on the lung surface. The findings, combined with sociodemographic details and personal habits, provide an estimate of lifelong accumulation of urban air pollution particles.

Our previous study explored lung anthracosis as an index of individual exposure to traffic-related air pollution, considering factors such as personal habits, mobility patterns, and occupational activities [3]. Interestingly, this study demonstrated differences in black patches across different lobes, which were categorized by daily commuting habits and smoking. We also found that pleural anthracosis increased with age, indicating a time-dependent pattern of carbon deposition in the lungs. Increased time spent commuting and exposure to traffic-related air pollution were significantly associated with higher levels of pleural anthracosis, confirming a link between urban air pollution and the accumulation of carbon deposits in the lungs. Additionally, black carbon levels were higher among individuals with lower socioeconomic status, indicating higher exposure to air pollution among underprivileged individuals, probably due to longer commuting hours [3,22]. These findings suggest that reducing the health consequences of air pollution requires alternative fuels, more efficient mobility strategies, and a rethinking of urban planning to address socioeconomic disparities.

Although our previous studies were relevant and validated the approach, they provided only an overview and did not detail the methodological steps. It is important to disseminate this method initiated in studies with Sao Paulo residents, because it can be easily replicated in other megacities or even smaller cities with autopsy services. After validating the estimation of exposure to atmospheric pollution through the macroscopic analysis of black pigments on the lung surface, it is possible to verify the association between such exposure and health outcomes.

We are confident that this pathological approach not only complements but also enriches the epidemiological findings that link respiratory diseases such as asthma, chronic obstructive pulmonary disease, rhinitis, and lung cancer [23,24], as well as cardiovascular diseases like atherosclerosis and hypertension [25,26], to air pollution exposure. In this context, we recently demonstrated a correlation between this index and cardiac fibrosis [27]. Our findings indicate that higher levels of black pigments in the lungs are associated with greater cardiac deposition of interstitial fibrosis, particularly among hypertensive individuals and smokers. Further studies are currently being conducted to explore the association with other cardiovascular implications and the effects on various systems. The past decade has seen substantial interest in the potential effects of air pollution on the central nervous system, particularly regarding air pollution-induced neurotoxicity and its relevance to neurodevelopmental and neurodegenerative diseases [28]. As can be noted, there are several pathways to be investigated that could benefit from the application of the detailed pathological method presented, underscoring the importance of addressing black pigment accumulation in health research.

The attempt to estimate exposure to air particles using tissue samples was conducted by Zeidberg and Prindle (1963), who analyzed histopathological sections of autopsied lungs from Nashville residents. Their study described a positive association between anthracosis and the duration of residence in the city [29]. The example of applied microscopic analysis is relevant and allows the quantification of particles deposited in different parts of the lung parenchyma. This method also offers detailed visualization and cellular-level analysis, providing insights into particle distribution and interactions [30]. However, the area usually quantified is much smaller compared with the total lung tissue, making it less representative than macroscopic approaches. Additionally, it requires the preparation of histological slides, which is time-consuming, requires technical expertise, and can be costly. Elemental chemical analysis is also valuable for determining the specific composition, sources, and estimated amount of deposited black pigments in the lung, but it similarly requires significant investment in terms of time, expertise, and cost [17,31]. The macroscopic method described in the present study is comparable to the microscopic approach, with correlating values obtained by complementary methods [3]. We believe that the method we have proposed for measuring macroscopic lung surface-deposited particles can accurately represent the percentage of particles deposited in an individual’s lung. It also has the advantage of not requiring tissue processing, thereby speeding up the acquisition of values for particles deposited in the lungs.

While we have not yet tested it, the application of artificial intelligence (AI) to optimize the analysis of black pigments on the lung surface should be seriously considered, as AI-based tools have been assisting pathologists [32]. Specifically, for example, the macrophage carbon load method for quantifying indirect lung deposition of black carbon can significantly benefit from applying machine learning counting approaches, making the assay feasible for large-scale epidemiological studies [33]. In fact, with the improvement of machine learning methods, it has become possible to integrate ground monitoring measurements of PM2.5, chemical transport model simulations of all-source PM2.5 concentrations, meteorological conditions, and geographical features [34]. These findings indicate that integrating measurable biological estimates from airways and lung tissues (including macroscopic, microscopic, and chemical analysis) with traditional data collection methods should be implemented in the near future. This integration provides novel opportunities for more accurate large-scale assessments of long-term personal exposure.

In fact, innovative approaches that combine multi-source data, including monitoring ambient air quality with fixed-site or mobile sensors and dynamic urban features, provide a more nuanced understanding of how pollutant exposure varies across space and time [35,36]. One disadvantage of all the methods used to evaluate and estimate exposure to air pollution is the cost involved. It is important to highlight that our proposed method requires a smaller team of researchers, minimal technical support, and non-sophisticated equipment. There is no need for reagents and various laboratory materials, as is usually the case in biological research settings. Additionally, all measurements and analyses can be performed using free software and applications. These characteristics make the method an attractive proposal for research institutes with limited financial resources for implementing sophisticated research facilities.

Although the described method is an excellent approach for estimating individual lifetime exposure to pollution, it has limitations. It is understood that the number of particles inhaled over a lifetime is certainly much higher than what is found in lung tissue exposed during an autopsy. In fact, about 40% of inhaled particles are deposited in different regions of the respiratory tract [37]. Among these, particles with a size of 0.01 µm exhibit the highest deposition efficiency in the alveolar region [38]. As summarized by Morawska and Buonanno (2021), we also recognize that numerous factors influence particle deposition in the respiratory tract, including: (i) the physicochemistry of aerosols, such as particle size distribution, density, shape, surface area, and whether they are hygroscopic or hydrophobic; (ii) the anatomy of the respiratory tract, including its diameter and length and the angles of airway segments; (iii) the physiology of the respiratory tract, such as airflow patterns and breathing patterns [39]. The clearance mechanism is also a crucial aspect of how the respiratory system deals with inhaled particles. Key points include mucociliary clearance, where cilia move mucus and trapped particles towards the throat for expulsion; alveolar macrophages, which are immune cells that engulf and digest particles in the lungs; the cough reflex, which helps expel mucus and particles; and solubility and absorption, where some particles dissolve in mucus and enter the bloodstream for elimination [40]. The efficiency of these mechanisms can be influenced by factors such as age, smoking, respiratory diseases, and other health conditions, with older adults potentially experiencing reduced mucociliary clearance due to changes in the respiratory tract structure [37].

We also acknowledge that this approach is uncommon due to the requirement for +an autopsy system that facilitates research. The research team members and the autopsy staff need to work synchronously to acquire a satisfactory amount of data. Additionally, identifying the specific components present in the particles deposited in the lungs could provide a relevant and complementary analysis. Although this aspect was not the focus of the current study, it has been addressed in previous research [16,17,41]. Due to significant differences in geographical environments, source distributions, pollution characteristics, economic conditions, and living habits, individual air pollution exposure varies widely across different regions of the world. Therefore, applying the method detailed in this study is feasible and could greatly enhance the approach if implemented in other cities with varying sources and levels of air pollutants.

It is also important to consider the potential biases that may arise from self-reported data obtained through NOK interviews. In fact, bias in epidemiologic and medical research remains a significant issue [42]. To address this concern, the interviewer must be trained to conduct the conversation very effectively with the family member and to detect any uncertainties in their responses. Another valuable document is the pathologist’s report, which typically contains information on previous clinical conditions and causes of death and may serve as a supportive document for confirming some of the information provided by the relatives. Once again, we believe that future studies should explore the use of AI to support the current approach, enhance data acquisition, and accurately establish the relationship between health outcomes and exposure to environmental pollutant particulates.

4. Conclusions

In conclusion, this study details a robust, low-cost, and innovative methodology for estimating lifelong exposure to urban air pollution through the quantification of black pigments in lung tissue obtained during autopsies. By considering sociodemographic data and information about personal habits collected via interviews with immediate family members in combination with the macroscopic examination of lung surfaces, this method offers a practical and cost-effective alternative to traditional air quality monitoring techniques. Meticulously documenting and standardizing each step enhances the reliability and reproducibility of the data, ensuring consistent application across different research settings.

The described methodology is easily replicable in other urban centers, providing a unique opportunity to study the effects of air pollution across different populations and environments with varying pollution levels. Future research should focus on applying this method in different geographic regions and demographic settings to validate its generalizability and understand its limitations. Additionally, there is a need to refine the method by incorporating the analysis of specific pollutants and complementary measurements for more accurate estimation of exposure to airborne particles.

To further enhance the applicability of this approach, integrating AI could significantly optimize the analysis process. AI tools, such as machine learning algorithms, could be developed to automate the quantification of black pigments and analyze large datasets more efficiently. This would not only make the process more scalable for large-scale epidemiological studies but also enhance the accuracy and consistency of the findings.

Ultimately, the continued development of this approach, including its expansion to include specific pollutant analysis and AI integration, can contribute to a deeper understanding of the relationship between urban air pollution and human health. These advancements pave the way for informed public health interventions and policies aimed at reducing exposure and mitigating risks, particularly in underprivileged communities that may be disproportionately affected by air pollution.

Author Contributions

Conceptualization, M.M.V., P.H.N.S. and A.P.C.T.; methodology, M.M.V. and A.P.C.T.; investigation, D.W.; resources, M.M.V., P.H.N.S. and A.P.C.T.; writing—original draft preparation, D.W. and A.P.C.T.; writing—review and editing, M.M.V. and P.H.N.S. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board (or Ethics Committee) of the Faculdade de Medicina da Universidade de São Paulo (protocol code 98929118.9.0000.0065; date of approval: 09/18/2018) for studies involving humans.

Informed Consent Statement

Informed consent was obtained from all next of kin of deceased subjects involved in the study.

Data Availability Statement

Data available on request due to privacy.

Conflicts of Interest

The authors declare no conflicts of interest.

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Figures

Figure 1. Basic stages for estimating lifelong exposure to urban ambient particles, combining pathological and epidemiological approaches. Stages 1 and 2 are conducted in the autopsy service. Stages 3 and 4 are conducted in a regular office.

Figure 2. Representative photo of the surface of a lung lobe with the point test system overlaid on the image.

Figure 3. Representative photo of the surface of a lung lobe with the quantification of black patches (yellow dots, total = 47) and clear pleura tissue (purple dots, total = 103). The fraction of black patches on this lobe is 47/(47 + 103) = 0.31, which can be interpreted as 31% of the surface being covered by black patches. After performing the same quantification for the other lobes, the mean value can be calculated from the four lobes to estimate the quantity of lifelong urban ambient particles retained in the lung.

References

1. Walker, R.; Parsche, F.; Bierbrier, M.; McKerrow, J.H. Tissue identification and histologic study of six lung specimens from Egyptian mummies. Am. J. Phys. Anthropol.; 1987; 72, pp. 43-48. [DOI: https://dx.doi.org/10.1002/ajpa.1330720106] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/3548413]

2. Isidro, A.; Malgosa, A.; Prats-Munoz, G. Anthracosis in a Coptic mummy. Arch. Bronconeumol.; 2014; 50, pp. 368-369. [DOI: https://dx.doi.org/10.1016/j.arbr.2014.06.006] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/24684766]

3. Takano, A.P.C.; Justo, L.T.; Dos Santos, N.V.; Marquezini, M.V.; de André, P.A.; da Rocha, F.M.M.; Pasqualucci, C.A.; Barrozo, L.V.; Singer, J.M.; De André, C.D.S. et al. Pleural anthracosis as an indicator of lifetime exposure to urban air pollution: An autopsy-based study in Sao Paulo. Environ. Res.; 2019; 173, pp. 23-32. [DOI: https://dx.doi.org/10.1016/j.envres.2019.03.006] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/30884435]

4. Pryor, J.T.; Cowley, L.O.; Simonds, S.E. The physiological effects of air pollution: Particulate matter, physiology and disease. Front. Public Health; 2022; 10, 882569. [DOI: https://dx.doi.org/10.3389/fpubh.2022.882569]

5. Konduracka, E.; Rostoff, P. Links between chronic exposure to outdoor air pollution and cardiovascular diseases: A review. Environ. Chem. Lett.; 2022; 20, pp. 2971-2988. [DOI: https://dx.doi.org/10.1007/s10311-022-01450-9] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/35496466]

6. Brauer, M.; Hoek, G.; van Vliet, P.; Meliefste, K.; Fischer, P.; Gehring, U.; Heinrich, J.; Cyrys, J.; Bellander, T.; Lewne, M. et al. Estimating long-term average particulate air pollution concentrations: Application of traffic indicators and geographic information systems. Epidemiology; 2003; 14, pp. 228-239.

7. Van Donkelaar, A.; Martin, R.V.; Brauer, M.; Boys, B.L. Use of satellite observations for long-term exposure assessment of global concentrations of fine particulate matter. Environ. Health Perspect.; 2015; 123, pp. 135-143. [DOI: https://dx.doi.org/10.1289/ehp.1408646]

8. Gruzieva, O.; Georgelis, A.; Andersson, N.; Johansson, C.; Bellander, T.; Merritt, A.S. Comparison of personal exposure to black carbon levels with fixed-site monitoring data and with dispersion modelling and the influence of activity patterns and environment. J. Expo. Sci. Environ. Epidemiol.; 2024; 34, pp. 538-545.

9. Mirowsky, J.; Gordon, T. Noninvasive effects measurements for air pollution human studies: Methods, analysis, and implications. J. Expo. Sci. Environ. Epidemiol.; 2015; 25, pp. 354-380. [DOI: https://dx.doi.org/10.1038/jes.2014.93]

10. Takano, A.P.C.; Rybak, J.; Veras, M.M. Bioindicators and human biomarkers as alternative approaches for cost-effective assessment of air pollution exposure. Front. Environ. Eng.; 2024; 3, 1346863.

11. Bai, Y.; Brugha, R.E.; Jacobs, L.; Grigg, J.; Nawrot, T.S.; Nemery, B. Carbon loading in airway macrophages as a biomarker for individual exposure to particulate matter air pollution—A critical review. Environ. Int.; 2015; 74, pp. 32-41. [DOI: https://dx.doi.org/10.1016/j.envint.2014.09.010] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/25318022]

12. Bai, Y.; Bové, H.; Nawrot, T.S.; Nemery, B. Carbon load in airway macrophages as a biomarker of exposure to particulate air pollution; a longitudinal study of an international Panel. Part Fibre Toxicol.; 2018; 15, 14. [DOI: https://dx.doi.org/10.1186/s12989-018-0250-8] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/29540230]

13. Kulkarni, N.S.; Prudon, B.; Panditi, S.L.; Abebe, Y.; Grigg, J. Carbon loading of alveolar macrophages in adults and children exposed to biomass smoke particles. Sci. Total Environ.; 2005; 345, pp. 23-30. [DOI: https://dx.doi.org/10.1016/j.scitotenv.2004.10.016] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/15919524]

14. Pérez, F.; Nadal, M.; Navarro-Ortega, A.; Fàbrega, F.; Domingo, J.L.; Barceló, D.; Farré, M. Accumulation of perfluoroalkyl substances in human tissues. Environ. Int.; 2013; 59, pp. 354-362. [DOI: https://dx.doi.org/10.1016/j.envint.2013.06.004] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/23892228]

15. Rallis, G.N.; Boumba, V.A.; Sakkas, V.A.; Fragkouli, K.; Siozios, G.; Albanis, T.A.; Vougiouklakis, T. Residues of selected polychlorinated biphenyls (PCB) and organochlorine pesticides (OCP) in postmortem lungs from Epirus, northwestern Greece. J. Toxicol. Environ. Health A; 2014; 77, pp. 767-775.

16. Domingo, J.L.; García, F.; Nadal, M.; Schuhmacher, M. Autopsy tissues as biological monitors of human exposure to environmental pollutants. A case study: Concentrations of metals and PCDD/Fs in subjects living near a hazardous waste incinerator. Environ. Res.; 2017; 154, pp. 269-274.

17. Brauer, M.; Avila-Casado, C.; Fortoul, T.I.; Vedal, S.; Stevens, B.; Churg, A. Air pollution and retained particles in the lung. Environ. Health Perspect.; 2001; 109, pp. 1039-1043. [DOI: https://dx.doi.org/10.2307/3454959]

18. Churg, A.; Brauer, M.; del Carmen Avila-Casado, M.; Fortoul, T.I.; Wright, J.L. Chronic exposure to high levels of particulate air pollution and small airway remodeling. Environ. Health Perspect.; 2003; 111, pp. 714-718. [DOI: https://dx.doi.org/10.1289/ehp.6042]

19. de André, C.D.S.; Bierrenbach, A.L.; Barroso, L.P.; de André, P.A.; Justo, L.T.; Pereira, L.A.A.; Taniguchi, M.T.; Minto, C.M.; Takecian, P.L.; Kamaura, L.T. et al. Validation of physician certified verbal autopsy using conventional autopsy: A large study of adult non-external causes of death in a metropolitan area in Brazil. BMC Public Health; 2022; 22, 748. [DOI: https://dx.doi.org/10.1186/s12889-022-13081-4]

20. Griffin, C.P.; Bowen, J.R.; Walker, M.M.; Lynam, J.; Paul, C.L. Understanding the value of brain donation for research to donors, next-of-kin and clinicians: A systematic review. PLoS ONE; 2023; 18, e0295438. [DOI: https://dx.doi.org/10.1371/journal.pone.0295438]

21. Desmedt, C.; Carey, L.A. Global post-mortem tissue donation programmes to accelerate cancer research. Nat. Rev. Cancer; 2024; 24, pp. 289-290. [DOI: https://dx.doi.org/10.1038/s41568-024-00683-w] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/38589555]

22. da Motta Singer, J.; Saldiva de André, C.D.; Afonso de André, P.; Monteiro Rocha, F.M.; Waked, D.; Vaz, A.M.; Gois, G.F.; de Fátima Andrade, M.; Veras, M.M.; Nascimento Saldiva, P.H. et al. Assessing socioeconomic bias of exposure to urban air pollution: An autopsy-based study in São Paulo, Brazil. Lancet Reg. Health Am.; 2023; 22, 100500. [DOI: https://dx.doi.org/10.1016/j.lana.2023.100500] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/37187677]

23. Tran, H.M.; Tsai, F.J.; Lee, Y.L.; Chang, J.H.; Chang, L.T.; Chang, T.Y.; Chung, K.F.; Kuo, H.P.; Lee, K.Y.; Chuang, K.J. et al. The impact of air pollution on respiratory diseases in an era of climate change: A review of the current evidence. Sci. Total Environ.; 2023; 898, 166340. [DOI: https://dx.doi.org/10.1016/j.scitotenv.2023.166340] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/37591374]

24. Bălă, G.P.; Râjnoveanu, R.M.; Tudorache, E.; Motișan, R.; Oancea, C. Air pollution exposure-the (in)visible risk factor for respiratory diseases. Environ. Sci. Pollut. Res. Int.; 2021; 28, pp. 19615-19628. [DOI: https://dx.doi.org/10.1007/s11356-021-13208-x] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/33660184]

25. Bevan, G.H.; Al-Kindi, S.G.; Brook, R.D.; Münzel, T.; Rajagopalan, S. Ambient Air Pollution and Atherosclerosis: Insights Into Dose, Time, and Mechanisms. Arterioscler. Thromb. Vasc. Biol.; 2021; 41, pp. 628-637.

26. Sanidas, E.; Papadopoulos, D.P.; Grassos, H.; Velliou, M.; Tsioufis, K.; Barbetseas, J.; Papademetriou, V. Air pollution and arterial hypertension. A new risk factor is in the air. J. Am. Soc. Hypertens.; 2017; 11, pp. 709-715. [DOI: https://dx.doi.org/10.1016/j.jash.2017.09.008]

27. Takano, A.P.C.; de André, C.D.S.; de Almeida, R.; Waked, D.; Veras, M.M.; Saldiva, P.H.N. Association of pulmonary black carbon accumulation with cardiac fibrosis in residents of São Paulo, Brazil. Environ. Res.; 2024; 248, 118380. [DOI: https://dx.doi.org/10.1016/j.envres.2024.118380]

28. Costa, L.G.; Cole, T.B.; Dao, K.; Chang, Y.C.; Coburn, J.; Garrick, J.M. Effects of air pollution on the nervous system and its possible role in neurodevelopmental and neurodegenerative disorders. Pharmacol. Ther.; 2020; 210, 107523. [DOI: https://dx.doi.org/10.1016/j.pharmthera.2020.107523]

29. Zeidberg, L.D.; Prindle, R.A. The Nashville air pollution study. II. Pulmonary anthracosis as an index of air pollution. Am. J. Public Health Nations Health; 1963; 53, pp. 185-199. [DOI: https://dx.doi.org/10.2105/ajph.53.2.185]

30. Geiser, M.; Kreyling, W.G. Deposition and biokinetics of inhaled nanoparticles. Part Fibre Toxicol.; 2010; 7, 2. [DOI: https://dx.doi.org/10.1186/1743-8977-7-2]

31. Wang, W.; Lin, Y.; Yang, H.; Ling, W.; Liu, L.; Zhang, W.; Lu, D.; Liu, Q.; Jiang, G. Internal Exposure and Distribution of Airborne Fine Particles in the Human Body: Methodology, Current Understandings, and Research Needs. Environ. Sci. Technol.; 2022; 56, pp. 6857-6869. [DOI: https://dx.doi.org/10.1021/acs.est.1c07051] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/35199997]

32. Viswanathan, V.S.; Toro, P.; Corredor, G.; Mukhopadhyay, S.; Madabhushi, A. The state of the art for artificial intelligence in lung digital pathology. J. Pathol.; 2022; 257, pp. 413-429. [DOI: https://dx.doi.org/10.1002/path.5966] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/35579955]

33. Jiang, M.; Hu, C.J.; Rowe, C.L.; Kang, H.; Gong, X.; Dagucon, C.P.; Wang, J.; Lin, Y.; Sood, A.; Guo, Y. et al. Application of artificial intelligence in quantifying lung deposition dose of black carbon in people with exposure to ambient combustion particles. J. Expo. Sci. Environ. Epidemiol.; 2024; 34, pp. 529-537. [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/37848612]

34. Yu, W.; Ye, T.; Zhang, Y.; Xu, R.; Lei, Y.; Chen, Z.; Yang, Z.; Zhang, Y.; Song, J.; Yue, X. et al. Global estimates of daily ambient fine particulate matter concentrations and unequal spatiotemporal distribution of population exposure: A machine learning modelling study. Lancet Planet. Health; 2023; 7, pp. e209-e218. [DOI: https://dx.doi.org/10.1016/s2542-5196(23)00008-6]

35. Song, J. Towards space-time modelling of PM2.5 inhalation volume with ST-exposure. Sci. Total Environ.; 2024; 948, 174888. [DOI: https://dx.doi.org/10.1016/j.scitotenv.2024.174888]

36. Song, J.; Stettler, M.E.J. A novel multi-pollutant space-time learning network for air pollution inference. Sci. Total Environ.; 2022; 811, 152254. [DOI: https://dx.doi.org/10.1016/j.scitotenv.2021.152254]

37. ICRP. Human respiratory tract models for radiological protection. ICRP Publ.; 1994; 66, pp. 1-3.

38. Chauhan, B.V.S.; Corada, K.; Young, C.; Smallbone, K.L.; Wyche, K.P. Review on Sampling Methods and Health Impacts of Fine (PM2.5, ≤2.5 µm) and Ultrafine (UFP, PM0.1, ≤0.1 µm) Particles. Atmosphere; 2024; 15, 572. [DOI: https://dx.doi.org/10.3390/atmos15050572]

39. Morawska, L.; Buonanno, G. The physics of particle formation and deposition during breathing. Nat. Rev. Phys.; 2021; 3, pp. 300-301. [DOI: https://dx.doi.org/10.1038/s42254-021-00307-4]

40. Mühlfeld, C.; Ochs, M. Functional Aspects of Lung Structure as Related to Interaction with Particles. Particle-Lung Interactions; 2nd ed. Gehr, P.; Mühlfeld, C.; Rothen-Rutishauser, B.; Blank, F. CRC Press: Boca Raton, FL, USA, 2010; pp. 9-12.

41. Saieg, M.A.; Cury, P.M.; Godleski, J.J.; Stearns, R.; Duarte, L.G.; D’Agostino, L.; Kahn, H.; Pinto, E.M.; Mauad, T.; Saldiva, P.H. et al. Differential elemental distribution of retained particles along the respiratory tract. Inhal. Toxicol.; 2011; 23, pp. 459-467. [DOI: https://dx.doi.org/10.3109/08958378.2011.582895]

42. Althubaiti, A. Information bias in health research: Definition, pitfalls, and adjustment methods. J. Multidiscip. Healthc.; 2016; 9, pp. 211-217. [DOI: https://dx.doi.org/10.2147/jmdh.s104807] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/27217764]