Skip to main content
Download PDF

Long-term effects of air pollutants on respiratory and cardiovascular mortality in a port city along the Adriatic sea

BMC Pulmonary Medicine volume 23, Article number: 395 (2023) Cite this article

Abstract

Background

Shipping and port-related air pollution has a significant health impact on a global scale. The present study aimed to assess the mortality burden attributable to long-term exposure to ambient particulate matter (PM2.5, PM10) and nitrogen dioxide (NO2) in the city of Ancona (Italy), with one of the leading national commercial harbours.

Methods

Exposure to air pollutants was derived by dispersion models. The relationship between the long-term exposure of air pollution exposure and cause-specific mortality was evaluated by Poisson regression models, after adjustment for gender, age and socioeconomic status. Results are expressed as percent change of risk (and relative 95% confidence intervals) per 5 unit increases in the exposures. The health impact on the annual number of premature cause-specific deaths was also assessed.

Results

PM2.5 and NO2 annual concentrations were higher in the area close to the harbour than in the rest of the city. Positive associations between each pollutant and most of the mortality outcomes were observed, with estimates of up to 7.6% (95%CI 0.1, 15.6%) for 10 µg/m3 increase in NO2 and cardiovascular mortality and 15.3% (95%CI-1.1, 37.2%) for 10 µg/m3 increase PM2.5 and lung cancer. In the subpopulation living close to the harbour, there were excess risks of up to 13.5%, 24.1% and 37.9% for natural, cardiovascular and respiratory mortality. The number of annual premature deaths due to the excess of PM2.5 and NO2 exposure (having as a reference the 2021 World Health Organization Air Quality Guidelines) was 82 and 25, respectively.

Conclusions

Our study confirms the long-term health effects of PM and NO2 on mortality and reveals a higher mortality burden in areas close to shipping and port-related emissions. Estimating the source-specific health burdens is key to achieve a deeper understanding of the role of different emission sources, as well as to support effective and targeted mitigation strategies.

Peer Review reports

Introduction

Human health effects of exposure to ambient air pollution, including particulate matter (PM) and gaseous contaminants, are a primary public concern, as they are responsible for approximately 3 million premature deaths per year globally [1].

Particulate matter (PM) is made of a complex mixture of chemicals with different compositions, physical features, sources and emission profiles [2]. The health impact of mass concentrations of PM with aerodynamic diameter ≤ 2.5 μm (PM2.5, mainly originated from combustion sources) and with ≤ 10 μm (PM10, composed also of crystal material, sea salt and biological material) has been largely investigated over the last decades [3]. The overall evidence suggests a causal relationship between PM2.5 and morbidity and mortality, especially from cardiovascular and respiratory conditions [4]. An additional air pollutant with established toxicity for human health is nitrogen dioxide (NO2), considered a key precursor of secondary pollutants, as newly generated organic, nitrate, and sulfate particles contribute to the PM10/PM2.5 mass.

Due to the increasing body of evidence suggesting that air pollution affects human health at lower concentrations than previously thought, the World Health Organization (WHO) has recently updated the air quality guidelines (AQGs). In detail, PM2.5, PM10, and NO2 annual guidelines have been lowered respectively to 5, 15 and 10 µg/m3 [2].

Air pollutant concentrations are influenced by interaction among different factors, including weather conditions, diurnal/seasonal cycles in solar radiation, local sources and emission rates [5]. Urban areas are significant contributors and the more appropriate unit of analysis, especially where intense industrial and portal activities are present [6, 7]. With reference to shipping and port-related emissions due to maritime transport, the International Maritime Organisation (IMO), by means of the International Convention for the Prevention of Pollution from Ships (MARPOL) Annex VI, provided the regulations for reducing air pollution and established emission control areas (ECA) for sulphur oxides (SOx) and nitrogen oxides (NOx) [8], which were revised over time, further lowering fuel content [9]. However, despite the IMO emission control measures, there are still vast amounts of emissions from shipping, ships at berth and port activities, and the relative health burden was estimated to have further increased, with ~ 265,000 global premature deaths projected for 2020 (i.e. 0.5% of global mortality), due to rapid increases in maritime transport volumes [10]. This literature review, focused on the health impact attribultable to shipping and port-related emissions, included 32 articles on this topic, but, among these, no investigations have specifically assessed differences in health outcomes according to distance from the port [10].

The Italian city of Ancona is located in the center-east part of the country along the sea and has a leading national commercial harbour, placed very close to the center of the city. Thus, the present study aimed at assessing the mortality burden attributable to long-term exposure to ambient PM2.5, PM10 and NO2 (above the updated AQG 2021 levels) during 2013–2017 in Ancona with a particular focus on potential differences in health impact according to the distance from the port.

Materials and methods

Study area

Ancona's city is extended over a 123 km2 area and had over 100,696 inhabitants in 2017. The city is subdivided into 722 census tracts by the National Institute of Statistics (ISTAT), i.e. the spatial unit considered in this study (approximatively 139 inhabitants each census tract).

Located in the middle of the Italian Adriatic coast, the port areas are 1.4 km2 articulated in passenger and ferry terminals, container and general cargo facilities. The port is crucial in the Adriatic-Ionian Macro-Region as the terminal of the international ferry routes to Greece, Croatia and Albania.

As indicated, the harbour is located in the central part of the city and, to better characterize the harbour's health impact, we stratified the city into harbour areas/yes–no using an 800 m radius buffer from the centroid of the port, in order to select at least the 10% of total population, as shown in Fig. 1. The 800 m cut-off was decided a priori before the analysis was done and it was considered a good compromise to select an adequate number of people. The geographic spatial calculations were carried out with ArcGis software [11].

Fig. 1
figure 1

City map with 800 × 800 m buffer centered in the harbour area

Full size image

Environmental data

Exposure to PM10, PM2.5, and NO2 was derived by FARM (Flexible Air Quality Regional Model, www.farm-model.org) models on a 30 × 30 km spatial domain with a 0.5 × 0.5 km resolution provided by ARIANET, which are well described elsewhere [12, 13]. Briefly, FARM models are multi-grid Eulerian models, which account for the dispersion, transformation, and deposition of airborne pollutants in gas and aerosol phases by using meteorological and orographic information; subsequently, they are validated by observed data collected through fixed monitors. We assigned to each census tract specific exposure based on the 0.5 × 0.5 km grid map provided by ARIANET. For each spatial unit, we averaged the values of all cells overlapped and then weighted for the intersection area, as shown in Fig. 2 A-B. In our study, we used data based on 2019 as proxy of the total average exposure.

Fig. 2
figure 2

A-B. AverageNO2 (A) and PM2.5 (B) annual concentrations in µg/m3 over the grid map of Ancona

Full size image

Health data

We collected information for cause-specific mortality in each census tract of the city of Ancona. In detail, we obtained data for all-cause (International classification of diseases 10th version ICD-10th – A00 – R99), cardiovascular (I00-I99), respiratory (J00-J99), malignancies (C00-C99) and lung cancer (C34) mortality. For each event, we gathered information regarding age (in five classes: 0–4, 5–34, 35–64, 65–74, and 75 + years) and gender. Data were provided by the regional health information system of the Marche Region for the 2013–2017 period. Finally, data were aggregated for each census tract in the whole period. The socioeconomic indicator at the census tract level was derived from the Italian deprivation index based on the 2011 national census data about income, educational level and other socioeconomic covariates [14].

Statistical analysis

Continuous variables were reported as mean values and standard deviations, while the categorical ones were reported as total numbers and percentages for each category. The relationship between the long-term effects of air pollution exposure and cause-specific mortality was evaluated using the population at the census tract level as the denominator of the events in Poisson regression models. Specifically, the exposure was represented by the average pollutants concentration for each census tract, while the outcome was the total count of events for each census tract. We applied three different models by the level of confounding adjustment: 1) crude association with no adjustment; 2) adjustment for age and sex; 3) model 2 + socioeconomic position indicator, which is an area index (census tract specific) synthesizing many social and economic variables. Moreover, we conducted an additional analysis to evaluate the association of living close to the city's harbour area with mortality.

Results are expressed as percent change of risk and relative 95% confidence intervals per 5 unit increase in air pollutants exposure.

Finally, we have computed the health impact assessment of long-term air pollution exposure on an annual number of premature cause-specific deaths. The method which we have used has been explained in detail elsewhere [15]. Briefly, we have applied the following formula:

$$\mathrm{ADi }=\mathrm{ TDi}*\mathrm{AFi}$$

where:

ADi is the number of attributable deaths; TDi is the number of total deaths; AFi is the attributable fraction, which is obtained by:

$$\mathrm{AF}=\left(\mathrm{RR}-1\right)=\left[\exp\{\mathrm\beta\ast\left(\mathrm{exposure}-\mathrm{AQG}\right\}-1\right]/\exp\left\{\mathrm\beta\ast\left(\mathrm{exposure}-\mathrm{AQG}\right)\right\}$$

The beta coefficient was extrapolated by evidence derived from systematic reviews and meta-analyses of several studies [3]. Specifically, we considered the following relative risks per per 10 µg/m3 PM2.5: 1.08 (1.06, 1.09), 1.11 (1.09, 1.14), 1.10 (1.03, 1.18) and1.12 (1.07, 1.16) for non-accidental, cardiovascular, respiratory and lung cancer mortality, respectively. In this way, we obtained the number of premature deaths that would have been prevented if the WHO AQG had been adopted in the study setting.

Analyses were conducted using the statistical software R [16].

Results

Table 1 reports the population characteristics in the city of Ancona during the study period 2013–2017, subdivided according to the area of the city. Almost 10% of the subjects live in the city's harbour area, and 75% are less than 65 years old. Population demographics and socioeconomic status did not differ between the harbour and non-harbour areas. Higher PM2.5 and NO2 concentrations were estimated in the area close to the port than in the rest of the city: 21 vs 18.1 and 25.5 vs 18.4 µg/m3, respectively. In the lustrum, we observed 5,584 deaths, among which 2,051 were due to cardiovascular causes and 1,594 due to malignancies. No substantial differences emerged in the distribution of individual characteristics or deaths between the two areas.

Table 1 Population characteristics and the number of cause-specific deaths by area of the city during the study period 2013–2017
Full size table

In Table 2, we describe the results of the association between long-term exposure to air pollution and cause-specific mortality in the city of Ancona during 2013–2017. We observed a positive association between each pollutant and all considered outcomes, except for respiratory and cancer mortality. In the model 3 analyses, the estimates were up to 7.6% (95%CI: 0.1, 15.6%) for NO2 and cardiovascular mortality and 15.3% (95%CI: -1.1, 37.2%) for the association between PM2.5 and lung cancer. We observed lower positive estimates for natural mortality but with large confidence intervals.

Table 2 Association between long-term exposure to PM10, PM2.5 and NO2 and cause-specific mortality in the city of Ancona during 2013–2017 period using different levels of adjustment
Full size table

In Table 3, we show the results related to subjects who lived in the city's central area close to the harbour, in comparison to subjects residing elsewhere. We observed higher risks of mortality for natural, cardiovascular and respiratory causes for those living in the central part of Ancona. In detail, there was an excess risk of up to 13.5% (95% CI, 4.2–23.7) for natural mortality, up to 24.1% for cardiovascular mortality (95% CI, 8.3–42.3), and up to 37.9% (95% CI, 7.5–76.95) for respiratory mortality.

Table 3 Association (%change) between residence in central area of the city close to the harbour and cause-specific mortality (2013–2017)
Full size table

Finally, Fig. 3 reports the number of annual premature deaths which would be preventable if the yearly exposure to PM2.5 and NO2 were compliant with the AQG: 82 and 25 total annual deaths due to the excess of PM2.5 and NO2 exposure, respectively.

Fig. 3
figure 3

Annual preventable premature deaths due to long-term exposure to PM2.5 and NO2 in the city of Ancona

Full size image

Discussion

Our study was performed through a record linkage between healthcare databases and environmental data. The use of an accurate dispersion model provides evidence of positive associations between long-term exposure to major air pollutants and cause-specific mortality in the city of Ancona during the 2013–2017 period. The strongest association was observed for NO2 and cardiovascular-related mortality (7.6% increase per 5 µg/m3). All the associations are in keeping with the findings from the systematic reviews conducted to derive the WHO AQGs. A further relevant and innovative finding of our study is the significantly increased natural and cause-specific mortality risks observed in subjects living in the city's central area close to the harbour, where PM2.5 and NO2 annual mean exposure levels were largely above the latest AQG thresholds. Although studies on shipping emissions impact on human health have been increasing in recent years, using different developed methods [10], this is the first investigation specifically addressing the health burden in the urban area closest to the port-related emissions, showing an excess mortality risk. Overall, although our estimates have wide confidence interval, yet 82 premature deaths from PM2.5 exposure and 25 premature deaths from NO2 exposure per year would have been avoided if WHO AQGs had been respected.

The impact of exposure to major air pollutants on natural and selected cause-specific mortality in Ancona has already been investigated in a multi-center study on short-term exposures involving 25 Italian cities (EpiAir2) but the different methods and sources employed for analyses make it difficult to compare the results [17].

Several epidemiological studies have correlated increased air pollution to cardiovascular events, such as arrhythmias, atherosclerosis progression, myocardial infarction, and heart failure exacerbation [3, 18, 19]. Almost two-thirds of deaths attributed to pollution are due to cardiovascular diseases, outweighing mortality due to respiratory conditions, and 33% of global cardiovascular deaths are currently attributable to air pollution [1, 20]. Plausible pathogenic pathways include endothelial dysfunction and vasoconstriction, increased blood pressure, prothrombotic and coagulant changes, systemic inflammatory and oxidative stress responses, and autonomic imbalance [21,22,23].

It is also well established that increased exposures to various air pollutants contribute to exacerbations in patients with chronic respiratory diseases, such as asthma and chronic obstructive respiratory disease [4, 24]. The respiratory tract is the primary portal of entry of PM; thus, airway inflammation, remodeling, and responsiveness are likely to be promoted and amplified by both acute and chronic inhalation of these irritants [4].

The association between PM2.5 and lung cancer has also been investigated in lifelong non-smokers to remove the confounding role of active smoke. Lung cancer incidence in this subgroup has been rapidly increasing over the recent decades, accounting for approximately 25% of cases worldwide, and PM2.5 is currently deemed to play a critical role in this context [25]. Data from 17 cohort studies in nine European countries (ESCAPE) showed that a 5 μg/m3 increment in annual exposure to PM2.5 was associated with an increased hazard ratio of lung cancer incidence with a relative risk of 1.18 [26]. The relative risk per 10 μg/m3 increase in PM2.5 for development of lung cancer was 1.19 (95% CI: 1.09, 1.30) in a large study in the USA [27]. Furthermore, an increase in PM2.5 concentration was positively correlated with an increase in lung cancer mortality. Indeed, outdoor PM2.5 pollution was defined as a Group 1 human carcinogen by the International Agency for Research on Cancer (IARC) in 2013 [28, 29]. Potential pathogenic carcinogenesis mechanisms include inflammatory injury, reactive oxygen species production, and oxidative damage to DNA. Our study detected an increased risk of lung cancer, although with a large confidence interval.

With reference to source-specific emission effects, while numerous studies looked at road transport-sourced air pollution, relatively less data are available on shipping and port-related health impact. However, over the last decade, the ability to assess the health effects and the chemical and physical characteristics of ship pollutant exposure were identified as key knowledge gaps and, thus, a growing attention has been given to this challenging context.

A major strength of the present paper, as mentioned above, is the assessment of air pollutant concentration in a city characterized by the presence of a large harbor located in the central urban area, analyzing natural and cause-specific mortality risk overall and separately for subjects living in zones near to this source. However, some limitations need to be acknowledged. Data on air pollutant concentrations at spatial level were the results of a model, although validated through a local monitoring unit. Moreover, due to the lack of measurement stations in the harbor area, it was not clear what proportion of air pollution was actually shipping-sourced and to what extent emissions from non-ship activities, such as cargo-loading machinery, heavy-duty vehicles or trains have influenced the overall burden. Furthermore, we used the average exposure of the year 2019 assuming that no spatial differences emerged during the study period. Another limitation could be related to the arbitrary a priori choice of 800 m cut-off for buffer. However, a sensitivity analysis considering different buffers related to the central area was performed (600m2 and 1000m2 buffers), showing consistent results among the three buffers (data not shown), but confirming the 800 m2 one as the best compromise between an adequate size of population exposed and weight of exposure.

Considering that Ancona is a relatively small city, the limited number of cause-specific events observed may have impacted the power of the study and the generalizability of the results. In fact, the observed associations are characterized by high uncertainty, with wide confidence intervals; in addition, they are often of borderline significance. A further limitation is related to the data sources: administrative databases not primarily designed as tools for epidemiological research. As a result, information on important individual risk factors, such as smoking habits, physical activity, and diet, was unavailable. Therefore, some degrees of residual confounding might be possible in the evaluation of the associations, especially when comparing the harbour area with the rest of the city. It should be considered, however, that we adjusted our models for socioeconomic status, which is a proxy of some lifestyle behaviours including those previously described. In this way, we were able to take into account, at least partially, the role of some individual risk factors [30, 31]. We did not consider other variables, like access and quality of health care that, in theory, are guaranteed to all citizens in Italy under the National Health Service. In a small town like Ancona, we have no reasons to think that healthcare is different in different parts of the city.

Conclusion

Our analysis showed a significant long-term health effect of PM and NO2, indicating a not negligible higher mortality burden in areas close to a substantial source of air pollutants like the port. Considering that a large majority of the world's population currently lives in areas where air pollution exceeds AQG levels, especially those with intense industrial/portal activity, it is essential to locally estimate the concentration of air pollutants and to urgently adopt targeted policy action to reduce the mortality burden attributable to the air quality. Estimating the source-specific health burdens is key to achieve a deeper understanding of the role of different emission sources, as well as to support the design of effective and targeted mitigation strategies.

Availability of data and materials

The datasets used during the current study was available from the corresponding authors.

References

  1. GBD. Global burden of 87 risk factors in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet Risk Factors Collabotators. 2019;2020(396):1223–49.

    Google Scholar 

  2. WHO. Global air quality guidelines: particulate matter (PM2.5 and PM10), ozone, nitrogen dioxide, sulfur dioxide and carbon monoxide. 2021. Available at https://apps.who.int/iris/handle/10665/345329. Last accessed 9th Nov 2022

  3. Chen J, Hoek G. Long-term exposure to PM and all-cause and cause-specific mortality: a systematic review and meta-analysis. Environ Int. 2020;143:105974.

    Article  CAS  PubMed  Google Scholar 

  4. Thurston GD, Kipen H, Annesi-Maesano I, et al. A joint ERS/ATS policy statement: what constitutes an adverse health effect of air pollution? An analytical framework. Eur Respir J. 2017;49:1600419.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Fan Q, Zhang Y, Ma W, et al. Spatial and seasonal dynamics of ship emissions over the Yangtze river Delta and East China sea and their potential environmental influence. Environ Sci Technol. 2016;50:1322–9.

    Article  CAS  PubMed  Google Scholar 

  6. Sorte S, Rodrigues V, Borrego C, Monteiro A. Impact of harbour activities on local air quality: a review. Environ Pollut. 2020;257:113542.

    Article  CAS  PubMed  Google Scholar 

  7. Ito K, Johnson S, Kheirbek I, et al. Intraurban variation of fine particle elemental concentrations in New York City. Environ Sci Technol. 2016;50:7517–26.

    Article  CAS  PubMed  Google Scholar 

  8. IMO. Prevention of air pollution from ships. 2019a. Available at https://www.imo.org/en/OurWork/Environment/Pages/Air-Pollution.aspx

  9. IMO. Sulphur Oxides (SOx) and Particulate Matter (PM) – Regulation 14. URL. 2019b. Availableat https://www.imo.org/en/OurWork/Environment/Pages/Sulphur-oxides-(SOx)-%5f-Regulation-14.aspx

  10. Mueller N, Westerby M, Nieuwenhuijsen M. Health impact assessments of shipping and port-sourced air pollution on a global scale: a scoping literature review. Environ Res. 2023;216:114460.

    Article  CAS  PubMed  Google Scholar 

  11. ESRI. ArcGIS Desktop: Release 10. Redlands, CA: Environmental Systems Research Institute. 2011

  12. Mircea M, Grigoras G, D’isidoro M, Righini G, Adani M, Briganti G, et al. Impact of grid resolution on aerosol predictions: a case study over Italy. Aerosol Air Qual Res. 2016;16:1253–67. https://doi.org/10.4209/aaqr.2015.02.0058.

  13. Baklanov A, Bjergene N, Fay B, Finardi S, Gross A, Jantunen M, et al. Forecasting urban meteorology, air pollution and population exposure (European FUMAPEX Project). Air Pollut Model Its Appl XVII. 2007:29–40. https://doi.org/10.1007/978-0-387-68854-1_4.

  14. Rosano A, Pacelli B, Zengarini N, Costa G, Cislaghi C, Caranci N. Update and review of the 2011 Italian deprivation index calculated at the census section level. Epidemiol Prev. 2020;44:162–70.

    PubMed  Google Scholar 

  15. Renzi M, Stafoggia M, Faustini A, Cesaroni G, Agabiti N, Forastiere F. Health effects of air pollution in Rome in December 2015. Epidemiol Prev. 2016;40:29–32.

    PubMed  Google Scholar 

  16. R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing,Vienna, Austria. 2017. https://www.R-project.org/.

  17. Alessandrini ER, Faustini A, Chiusolo M, et al. Air pollution and mortality in twenty-five Italian cities: results of the EpiAir2 Project. Epidemiol Prev. 2013;37:220–9.

    PubMed  Google Scholar 

  18. Saifipour A, Azhari A, Pourmoghaddas A, et al. Association between ambient air pollution and hospitalization caused by atrial fibrillation. ARYA Atheroscler. 2019;15:106–12.

    PubMed  PubMed Central  Google Scholar 

  19. Davoodabadi Z, Soleimani A, Pourmoghaddas A, et al. Correlation between air pollution and hospitalization due to myocardial infarction. ARYA Atheroscler. 2019;15:161–7.

    PubMed  PubMed Central  Google Scholar 

  20. Motairek I, Junior SA, Khraishah H, AlAhmad B, Al-Dulaimi S, Khalil CA, et al. Burden of cardiovascular disease attributable to particulate matter pollution in the eastern Mediterranean region: analysis of the 1990-2019 global burden of disease. Eur J Prev Cardiol. 2023;30:256–63. https://doi.org/10.1093/EURJPC/ZWAC256.

  21. Gold DR, Mittleman MA. New insights into pollution and the cardiovascular system: 2010 to 2012. Circulation. 2013;127:1903–13.

    Article  PubMed  Google Scholar 

  22. Li W, Dorans KS, Wilker EH, et al. Ambient air pollution, adipokines, and glucose homeostasis: the framingham heart study. Environ Int. 2018;111:14–22.

    Article  CAS  PubMed  Google Scholar 

  23. Bevan GH, Al-Kindi SG, Brook R, Rajagopalan S. Ambient air pollution and atherosclerosis: recent updates. Curr Atheroscler Rep. 2021;23:63.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Ma Y, Xiao B, Liu C, Zhao Y, Zheng X. Association between ambient air pollution and emergency room visits for respiratory diseases in spring dust storm season in Lanzhou, China. Int J Environ Res Public Health. 2016;13:613.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Yang X, Zhang T, Zhang X, Chu C, Sang S. Global burden of lung cancer attributable to ambient fine particulate matter pollution in 204 countries and territories, 1990–2019. Environ Res. 2022;204:112023.

    Article  CAS  PubMed  Google Scholar 

  26. Raaschou-Nielsen O, Andersen ZJ, Beelen R, et al. Air pollution and lung cancer incidence in 17 European cohorts: prospective analyses from the European Study of Cohorts for Air Pollution Effects (ESCAPE). Lancet Oncol. 2013;14:813–22.

    Article  PubMed  Google Scholar 

  27. Coleman NC, Burnett RT, Ezzati M, Marshall JD, Robinson AL, Pope CA 3rd. Fine Particulate Matter Exposure and Cancer Incidence: Analysis of SEER Cancer Registry Data from 1992–2016. Environ Health Perspect. 2020;128:107004.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Loomis D, Grosse Y, Lauby-Secretan B, et al. The carcinogenicity of outdoor air pollution. Lancet Oncol. 2013;14:1262–3.

    Article  CAS  PubMed  Google Scholar 

  29. IARC International Agency for Research on Cancer. Outdoor air pollution. IARC Monographs on the Evaluation of Carcinogenic Risks to Humans. 2013;109. https://monographs.iarc.fr/ENG/Monographs/vol109/mono109-F01.pdf. Accessed 10 Nov 2022.

  30. Cesaroni G, Badaloni C, Romano V, Donato E, Perucci CA, Forastiere F. Socioeconomic position and health status of people who live near busy roads: the Rome Longitudinal Study (RoLS). Environ Health. 2010;9:41.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Forastiere F, Stafoggia M, Tasco C, et al. Socioeconomic status, particulate air pollution, and daily mortality: differential exposure or differential susceptibility. Am J Ind Med. 2007;50:208–16.

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

None

Funding

This research was carried out as a part of the framework of "Progetto Inquinamento Ancona- PIA", funded Health and Environment Department of Regione Marche, Municipality of Ancona, and Port Authority.

Author information

Author notes

  1. Federico Mei and Matteo Renzi contributed equally as the first Author.

Authors and Affiliations

  1. Department of Biomedical Sciences and Public Health, Marche Polytechnic University, Ancona, Italy

    Federico Mei & Martina Bonifazi

  2. Respiratory Diseases Unit, Azienda Ospedaliero-Universitaria “Ospedali Riuniti”, Ancona, Italy

    Federico Mei & Martina Bonifazi

  3. Department of Epidemiology of Lazio Region, ASL Roma 1, Rome, Italy

    Matteo Renzi

  4. Honorary President Associazione Allergologi Immunologi Italiani Territoriali E Ospedalieri, , Firenze, Italy

    Floriano Bonifazi

  5. ARIANET S.R.L., Milan, Italy

    Nicola Pepe, Alessio D’Allura & Giuseppe Brusasca

  6. Institute of Clinical Physiology, National Research Council (CNR), Pisa, Italy

    Giovanni Viegi

  7. Institute of Translational Pharmacology, National Research Council (CNR), Palermo, Italy

    Francesco Forastiere

  8. Environmental Research Group, Imperial College, London, UK

    Francesco Forastiere

Authors
  1. Federico Mei
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Matteo Renzi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Martina Bonifazi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Floriano Bonifazi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Nicola Pepe
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Alessio D’Allura
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Giuseppe Brusasca
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Giovanni Viegi
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Francesco Forastiere
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

MR, FB, FF and FM take responsibility for (are the guarantors of) the content of the manuscript, including the data and analysis. This study was originally conceived by FB, FF, GV. MR, NP, AD, GB performed analyses. All authors were involved in data collection, data interpretation and all contributed to writing of the manuscript or revising it critically for important intellectual content. All the authors approved the final draft and agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Corresponding authors

Correspondence to Federico Mei or Matteo Renzi.

Ethics declarations

Ethics approval and consent to participate

The study was commissioned by the Municipal Authority within its responsibilities of assessing health risks for the population from environmental factors amenable for preventive interventions (Deliberation of Municipal Authority n. 494; Deliberation of Marche Region n.1676).

ARIANET provided environmental data by means of annual concentrations of pollutants by local monitoring stations of the Regional Environmental Protection Agencies. These data are public, so no permission was required. The municipal personal data were provided by the Municipality of Ancona to the P.F. PERFORMANCE AND STATISTICAL SYSTEM (SISTAR) of the Marche Region. The statistical data of the ISTAT were retrieved from the P.F. PERFORMANCE AND STATISTICAL SYSTEM (SISTAR) of the Marche Region and are public. The health data were provided by the hospitals to the Regional Health Agency (A.R.S.) of the Marche Region. These data were anonymized using the Hash function and provided to the P.F. PERFORMANCE AND STATISTICAL SYSTEM of the Marche Region for the treatment of linkage. With regard to data linkage, it should be noted that the P.F. PERFORMANCE AND STATISTICAL SYSTEM is officially authorized to do so as it is foreseen in the work (having code MAR-00007) of the National Statistical Program in force "Processing of data on mortality, hospitalization, outcomes of conception and links with data from municipal and/or regional health registers for purposes of support to regional planning e local. All data were anonymized and aggregated at the census tract level.

Ethics approval and informed consent were deemed unnecessary according to the national legislation referring to data protection in selected circumstances, including use of anonymized retrospective data with relevant healthcare research purposes for which the collection of informed consent would be unfeasible and would affect reliability of results (Garante per la protezione dei dati personali. GDPR – Doc. web n. 9124510 – Provvedimento recante le prescrizioni relative al trattamento di categorie particolari di dati, ai sensi dell’art. 21, comma 1 del d.lgs. 10 agosto 2018, n. 101, Pubblicato sulla Gazzetta Ufficiale Serie Generale n. 176 del 29 luglio 2019). Moreover, according to a recent judgment of the General European Court (uploaded), in cases of anonymized data (i.e. aggregate data, as the ones used in our analyses), there is no indication to apply data protection legislation (JUDGMENT OF 26. 4. 2023 – CASE T-557/20).

All methods were carried out in accordance with declaration of Helsinki.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mei, F., Renzi, M., Bonifazi, M. et al. Long-term effects of air pollutants on respiratory and cardiovascular mortality in a port city along the Adriatic sea. BMC Pulm Med 23, 395 (2023). https://doi.org/10.1186/s12890-023-02629-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12890-023-02629-8

Keywords

BMC Pulmonary Medicine

ISSN: 1471-2466

Contact us