Dataset Information

Mortality induced by PM_2.5 exposure following the 1783 Laki eruption using reconstructed meteorological fields.

ABSTRACT: The 1783-1784 Laki eruption provides a natural experiment to evaluate the performance of chemistry-transport models in predicting the health impact of air particulate pollution. There are few existing daily meteorological observations during the second part of the 18^th century. Hence, creating reasonable climatological conditions for such events constitutes a major challenge. We reconstructed meteorological fields for the period 1783-1784 based on a technique of analogues described in the Methods. Using these fields and including detailed chemistry we describe the concentrations of sulphur (SO₂/SO₄) that prevail over the North Atlantic, the adjoining seas and Western Europe during these 2 years. To evaluate the model, we analyse these results through the prism of two datasets contemporary to the Laki period: • The date of the first appearance of 'dry fogs' over Europe, • The excess mortality recorded in French parishes over the period June-September 1783. The sequence of appearances of the dry fogs is reproduced with a very-high degree of agreement to the first dataset. High concentrations of SO₂/SO₄ are simulated in June 1783 that coincide with a rapid rise of the number of deceased in French parishes records. We show that only a small part of the deceased of the summer of 1783 can be explained by the present-day relationships between PM2.5 and relative risk. The implication of this result is that other external factors such as the particularly warm summer of 1783, and the lack of health care at the time, must have contributed to the sharp increase in mortality over France recorded from June to September 1783.

SUBMITTER: Balkanski Y

PROVIDER: S-EPMC6203706 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature

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Mortality induced by PM<sub>2.5</sub> exposure following the 1783 Laki eruption using reconstructed meteorological fields.

Balkanski Y Y Menut L L Garnier E E Wang R R Evangeliou N N Jourdain S S Eschstruth C C Vrac M M Yiou P P

Scientific reports 20181026 1

The 1783-1784 Laki eruption provides a natural experiment to evaluate the performance of chemistry-transport models in predicting the health impact of air particulate pollution. There are few existing daily meteorological observations during the second part of the 18<sup>th</sup> century. Hence, creating reasonable climatological conditions for such events constitutes a major challenge. We reconstructed meteorological fields for the period 1783-1784 based on a technique of analogues described in ...[more]

PMID: 30367172

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Project description:It is well recognized that exposure to fine particulate matter (PM2.5) affects health adversely, yet few studies from South America have documented such associations due to the sparsity of PM2.5 measurements. Lima's topography and aging vehicular fleet results in severe air pollution with limited amounts of monitors to effectively quantify PM2.5 levels for epidemiologic studies. We developed an advanced machine learning model to estimate daily PM2.5 concentrations at a 1 km2 spatial resolution in Lima, Peru from 2010 to 2016. We combined aerosol optical depth (AOD), meteorological fields from the European Centre for Medium-Range Weather Forecasts (ECMWF), parameters from the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem), and land use variables to fit a random forest model against ground measurements from 16 monitoring stations. Overall cross-validation R2 (and root mean square prediction error, RMSE) for the random forest model was 0.70 (5.97 μg/m3). Mean PM2.5 for ground measurements was 24.7 μg/m3 while mean estimated PM2.5 was 24.9 μg/m3 in the cross-validation dataset. The mean difference between ground and predicted measurements was -0.09 μg/m3 (Std.Dev. = 5.97 μg/m3), with 94.5% of observations falling within 2 standard deviations of the difference indicating good agreement between ground measurements and predicted estimates. Surface downwards solar radiation, temperature, relative humidity, and AOD were the most important predictors, while percent urbanization, albedo, and cloud fraction were the least important predictors. Comparison of monthly mean measurements between ground and predicted PM2.5 shows good precision and accuracy from our model. Furthermore, mean annual maps of PM2.5 show consistent lower concentrations in the coast and higher concentrations in the mountains, resulting from prevailing coastal winds blown from the Pacific Ocean in the west. Our model allows for construction of long-term historical daily PM2.5 measurements at 1 km2 spatial resolution to support future epidemiological studies.

Project description:RationalePM2.5-induced adverse effects on respiratory health may be driven by epigenetic modifications in airway cells. The potential impact of exposure duration on epigenetic alterations in the airways is not yet known.ObjectivesWe aimed to study associations of fine particulate matter PM2.5 exposure with DNA methylation in nasal cells.MethodsWe conducted nasal epigenome-wide association analyses within 503 children from Project Viva (mean age 12.9 y), and examined various exposure durations (1-day, 1-week, 1-month, 3-months and 1-year) prior to nasal sampling. We used residential addresses to estimate average daily PM2.5 at 1 km resolution. We collected nasal swabs from the anterior nares and measured DNA methylation (DNAm) using the Illumina MethylationEPIC BeadChip. We tested 719,075 high quality autosomal CpGs using CpG-by-CpG and regional DNAm analyses controlling for multiple comparisons, and adjusted for maternal education, household smokers, child sex, race/ethnicity, BMI z-score, age, season at sample collection and cell-type heterogeneity. We further corrected for bias and genomic inflation. We tested for replication in a cohort from the Netherlands (PIAMA).ResultsIn adjusted analyses, we found 362 CpGs associated with 1-year PM2.5 (FDR < 0.05), 20 CpGs passing Bonferroni correction (P < 7.0x10-8) and 10 Differentially Methylated Regions (DMRs). In 445 PIAMA participants (mean age 16.3 years) 11 of 203 available CpGs replicated at P < 0.05. We observed differential DNAm at/near genes implicated in cell cycle, immune and inflammatory responses. There were no CpGs or regions associated with PM2.5 levels at 1-day, 1-week, or 1-month prior to sample collection, although 2 CpGs were associated with past 3-month PM2.5.ConclusionWe observed wide-spread DNAm variability associated with average past year PM2.5 exposure but we did not detect associations with shorter-term exposure. Our results suggest that nasal DNAm marks reflect chronic air pollution exposure.

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Mortality induced by PM_2.5 exposure following the 1783 Laki eruption using reconstructed meteorological fields.

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Mortality induced by PM<sub>2.5</sub> exposure following the 1783 Laki eruption using reconstructed meteorological fields.

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Mortality induced by PM2.5 exposure following the 1783 Laki eruption using reconstructed meteorological fields.

Publications

Mortality induced by PM<sub>2.5</sub> exposure following the 1783 Laki eruption using reconstructed meteorological fields.

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OmicsDI is part of the ELIXIR infrastructure

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Mortality induced by PM_2.5 exposure following the 1783 Laki eruption using reconstructed meteorological fields.