Air pollution globalization, as a combined effect of atmospheric transport and international trade, can lead to notable transboundary health impacts. Life expectancy reduction attribution analysis of transboundary pollution can reveal the effect of pollution globalization on the lives of individuals. This study coupled five state-of-the-art models to link the regional per capita life expectancy reduction to cross-boundary pollution transport attributed to consumption in other regions. Our results revealed that pollution due to consumption in other regions contributed to a global population-weighted PM2.5 concentration of 9 μg/m3 in 2017, thereby causing 1.03 million premature deaths and reducing the global average life expectancy by 0.23 year (≈84 days). Trade-induced transboundary pollution relocation led to a significant reduction in life expectancy worldwide (from 5 to 155 days per person), and even in the least polluted regions, such as North America, Western Europe, and Russia, a 12–61-day life expectancy reduction could be attributed to consumption in other regions. Our results reveal the individual risks originating from air pollution globalization. To protect human life, all regions and residents worldwide should jointly act together to reduce atmospheric pollution and its globalization as soon as possible.

Life table data for "Bounce backs amid continued losses: Life expectancy changes since COVID-19"

cc-by Jonas Schöley, José Manuel Aburto, Ilya Kashnitsky, Maxi S. Kniffka, Luyin Zhang, Hannaliis Jaadla, Jennifer B. Dowd, and Ridhi Kashyap. "Bounce backs amid continued losses: Life expectancy changes since COVID-19".

These are CSV files of life tables over the years 2015 through 2021 across 29 countries analyzed in the paper "Bounce backs amid continued losses: Life expectancy changes since COVID-19".

40-lifetables.csv

Life table statistics 2015 through 2021 by sex and region with uncertainty quantiles based on Poisson replication of death counts.

30-lt_input.csv

Life table input data.

• `id`: unique row identifier
• `region_iso`: iso3166-2 region codes
• `sex`: Male, Female, Total
• `year`: iso year
• `age_start`: start of age group
• `age_width`: width of age group, Inf for age_start 100, otherwise 1
• `nweeks_year`: number of weeks in that year, 52 or 53
• `death_total`: number of deaths by any cause
• `population_py`: person-years of exposure (adjusted for leap-weeks and missing weeks in input data on all cause deaths)
• `death_total_nweeksmiss`: number of weeks in the raw input data with at least one missing death count for this region-sex-year stratum. missings are counted when the week is implicitly missing from the input data or if any NAs are encounted in this week or if age groups are implicitly missing for this week in the input data (e.g. 40-45, 50-55)
• `death_total_minnageraw`: the minimum number of age-groups in the raw input data within this region-sex-year stratum
• `death_total_maxnageraw`: the maximum number of age-groups in the raw input data within this region-sex-year stratum
• `death_total_minopenageraw`: the minimum age at the start of the open age group in the raw input data within this region-sex-year stratum
• `death_total_maxopenageraw`: the maximum age at the start of the open age group in the raw input data within this region-sex-year stratum
• `death_total_source`: source of the all-cause death data
• `population_midyear`: midyear population (July 1st)
• `population_source`: source of the population count/exposure data
• `death_covid`: number of deaths due to covid
• `death_covid_date`: number of deaths due to covid as of
• `death_covid_nageraw`: the number of age groups in the covid input data
• `ex_wpp_estimate`: life expectancy estimates from the World Population prospects for a five year period, merged at the midpoint year
• `ex_hmd_estimate`: life expectancy estimates from the Human Mortality Database
• `nmx_hmd_estimate`: death rate estimates from the Human Mortality Database
• `nmx_cntfc`: Lee-Carter death rate projections based on trend in the years 2015 through 2019

Deaths

• source:
  • STMF input data series (https://www.mortality.org/Public/STMF/Outputs/stmf.csv)
  • ONS for GB-EAW pre 2020
  • CDC for US pre 2020
• STMF:
  • harmonized to single ages via pclm
  • pclm iterates over country, sex, year, and within-year age grouping pattern and converts irregular age groupings, which may vary by country, year and week into a regular age grouping of 0:110
  • smoothing parameters estimated via BIC grid search seperately for every pclm iteration
  • last age group set to [110,111)
  • ages 100:110+ are then summed into 100+ to be consistent with mid-year population information
  • deaths in unknown weeks are considered; deaths in unknown ages are not considered
• ONS:
  • data already in single ages
  • ages 100:105+ are summed into 100+ to be consistent with mid-year population information
  • PCLM smoothing applied to for consistency reasons
• CDC:
  • The CDC data comes in single ages 0:100 for the US. For 2020 we only have the STMF data in a much coarser age grouping, i.e. (0, 1, 5, 15, 25, 35, 45, 55, 65, 75, 85+). In order to calculate life-tables in a manner consistent with 2020, we summarise the pre 2020 US death counts into the 2020 age grouping and then apply the pclm ungrouping into single year ages, mirroring the approach to the 2020 data

Population

• source:
  • for years 2000 to 2019: World Population Prospects 2019 single year-age population estimates 1950-2019
  • for year 2020: World Population Prospects 2019 single year-age population projections 2020-2100
• mid-year population
  • mid-year population translated into exposures:
    • if a region reports annual deaths using the Gregorian calendar definition of a year (365 or 366 days long) set exposures equal to mid year population estimates
    • if a region reports annual deaths using the iso-week-year definition of a year (364 or 371 days long), and if there is a leap-week in that year, set exposures equal to 371/364\*mid_year_population to account for the longer reporting period. in years without leap-weeks set exposures equal to mid year population estimates. further multiply by fraction of observed weeks on all weeks in a year.

COVID deaths

• source: COVerAGE-DB (https://osf.io/mpwjq/)
• the data base reports cumulative numbers of COVID deaths over days of a year, we extract the most up to date yearly total

External life expectancy estimates

• source:
  • World Population Prospects (https://population.un.org/wpp/Download/Files/1_Indicators%20(Standard)/CSV_FILES/WPP2019_Life_Table_Medium.csv), estimates for the five year period 2015-2019
  • Human Mortality Database (https://mortality.org/), single year and age tables

In 2024, the average life expectancy for those born in more developed countries was 76 years for men and 82 years for women. On the other hand, the respective numbers for men and women born in the least developed countries were 64 and 69 years. Improved health care has lead to higher life expectancy Life expectancy is the measure of how long a person is expected to live. Life expectancy varies worldwide and involves many factors such as diet, gender, and environment. As medical care has improved over the years, life expectancy has increased worldwide. Introduction to health care such as vaccines has significantly improved the lives of millions of people worldwide. The average worldwide life expectancy at birth has steadily increased since 2007, but dropped during the COVID-19 pandemic in 2020 and 2021. Life expectancy worldwide More developed countries tend to have higher life expectancies, for a multitude of reasons. Health care infrastructure and quality of life tend to be higher in more developed countries, as is access to clean water and food. Africa was the continent that had the lowest life expectancy for both men and women in 2023, while Oceania had the highest for men and Europe and Oceania had the highest for women.

Correlations between conversation style variables and laughter for both Latina and White-European mothers.

DALIA dataset (Campobasso, Italy). DOI: 10.5281/zenodo.15395163

de Francesco M.C., Carranza M.L., Capotorti G., Del Vico E., D’Angeli C., Montaldi A., Paura B., Santoianni L.A., Varricchione M., Stanisci A. (2025).

In the framework of the National Biodiversity Future Centre (NBFC), specifically within the “Urban Biodiversity” working group (Spoke 5), we developed the DALIA relational database, which contains records of tree, shrub, and liana taxa recorded in the Functional Urban Area of Campobasso (Southern Italy). The DALIA database includes 170 species and subspecies (126 native and 44 alien) belonging to 46 taxonomic families (35 natives, 23 aliens). Each taxon, whether native or alien, was classified according with multiple ecological, functional, and biogeographic groups.

The database contains 6 tables described below:

Table “Taxonomy” including the scientific names according to WFO (WFO, 2024) and Flora d'Italia (Portal to the Flora of Italy, 2024) with the relative authors, and the common name in English and Italian languages (Portal to the Flora of Italy, 2024); the taxonomic family (Bartolucci et al. 2024), the classification in natives, archeophytes, neophytes and their status (invasive, casual, naturalized) (Galasso et al. 2024).

Table “Chorology” including the geographic distribution of the native species (Pignatti 1982) and the origin area of the archaeophyte and neophyte species (WFO, 2024).

Table “Traits” including life form and growth form categories according to the Raunkiær system (Pignatti et al. 2017; Raunkiær 1934); the plant growth habits, differentiating plants in tree, shrub, and liana (Diaz et al. 2022); the maximum height reached by the plants (Diaz et al. 2022); the leaf types based on leaf morphology, anatomy, and persistence (Chytry et al. 2024).

Table “Bloom & Dispersion” including flowering periods expressed as bloom months, total flowering length, and seasons (Pignatti et al. 2017); the generative diaspores, the classification of seeds, fruits, and any appendages serving a role in dispersal (fleshy, non-fleshy indehiscent, pappose, winged, unspecialized) (Sádlo et al. 2014); the dispersion modes (Lososová et al. 2023).

Table “Indicators” including the values for the EIVE’s - Ecological Indicator Values for Europe (Dengler et al. 2023); for the Disturbance Indices - Disturbance indicator values for European plants (Midolo et al. 2023); the GRIME values for the CSR strategies in plants (Pierce et al. 2016).

Table “Conservation status” including the possible diagnostic role for Habitat Directive (92/43/EEC) (Habitat Directive 92-43-CEE, 2024); for EUNIS habitats (Chytrý et al., 2020); the IUCN Status and Trend Population for Europe (IUCN, 2024).

The DALIA database reveals a high woody plant diversity for the FUA of Campobasso when compared with other similar studies (Roma-Marzio et al. 2016), with a high percentage of native species (Quaranta et al. 2025). This insight greatly differs from what has been recorded in large cities where aliens in the urban floras make up 40% of the total number of taxa (Pyšek 1998; Ricotta et al. 2009; Lososová et al. 2012).

DALIA is expected to act as a useful pilot tool for Nature-based Solutions (NBS) and environmental restoration actions in cities of Italian and Mediterranean inner territories. It also provides valuable ecological information that can be utilized in urban greenery projects, emphasizing the added value of avoidance of invasive and competitive alien plants while favoring native species found within the EU forest habitats of the nearby Natura 2000 areas (Capotorti et al. 2016; Resemini et al. 2025; EC2023).

References

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Capotorti, G, Del Vico, E, Anzellotti, I, Celesti-Grapow, L (2016). Combining the conservation of biodiversity with the provision of ecosystem services in urban green infrastructure planning: Critical features arising from a case study in the metropolitan area of Rome. Sustainability, 9(1), 10. https://doi.org/10.3390/su9010010

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Chytrý M, Tichý L, Hennekens SM, Knollová I, Janssen JAM, …, Schaminée JHJ (2020) EUNIS Habitat Classification: Expert system, characteristic species combinations and distribution maps of European habitats. Applied Vegetation Science 23: 648–675. https://doi.org/10.1111/avsc.12519

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Díaz S, Kattge J, Cornelissen JHC et al. (2022) The global spectrum of plant form and function: enhanced species-level trait dataset. Scientific Data 9: 755. https://doi.org/10.1038/s41597-022-01774-9

Díaz S, Kattge J, Cornelissen JHC et al. (2022) The global spectrum of plant form and function: enhanced species-level trait dataset. Scientific Data 9: 755. https://doi.org/10.1038/s41597-022-01774-9

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Main inputs used and dry matter yield for European and non-European crops serving as feed ingredients for animal feed (BR: Brazil, US: United States of America, UK: United Kingdom, MY: Malaysia, PK: Pakistan, UA: Ukraine).