Dataset Information

Developing an Advanced PM2.5 Exposure Model in Lima, Peru.

ABSTRACT: 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 ?g/m3). Mean PM2.5 for ground measurements was 24.7 ?g/m3 while mean estimated PM2.5 was 24.9 ?g/m3 in the cross-validation dataset. The mean difference between ground and predicted measurements was -0.09 ?g/m3 (Std.Dev. = 5.97 ?g/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.

SUBMITTER: Vu BN

PROVIDER: S-EPMC6671674 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

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Developing an Advanced PM2.5 Exposure Model in Lima, Peru.

Vu Bryan N BN Sánchez Odón O Bi Jianzhao J Xiao Qingyang Q Hansel Nadia N NN Checkley William W Gonzales Gustavo F GF Steenland Kyle K Liu Yang Y

Remote sensing 20190316 6

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 co ...[more]

PMID: 31372305

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Project description:BackgroundAsthma affects millions of people worldwide. Lima, Peru is one of the most polluted cities in the Americas but has insufficient ground PM2.5 (particulate matter that are 2.5 μm or less in diameter) measurements to conduct epidemiologic studies regarding air pollution. PM2.5 estimates from a satellite-driven model have recently been made, enabling a study between asthma and PM2.5.ObjectiveWe conducted a daily time-series analysis to determine the association between asthma emergency department (ED) visits and estimated ambient PM2.5 levels in Lima, Peru from 2010 to 2016.MethodsWe used Poisson generalized linear models to regress aggregated counts of asthma on district-level population weighted PM2.5. Indicator variables for hospitals, districts, and day of week were included to account for spatial and temporal autocorrelation while assessing same day, previous day, day before previous and average across all 3-day exposures. We also included temperature and humidity to account for meteorology and used dichotomous percent poverty and gender variables to assess effect modification.ResultsThere were 103,974 cases of asthma ED visits during the study period across 39 districts in Lima. We found a 3.7% (95% CI: 1.7%-5.8%) increase in ED visits for every interquartile range (IQR, 6.02 μg/m3) increase in PM2.5 same day exposure with no age stratification. For the 0-18 years age group, we found a 4.5% (95% CI: 2.2%-6.8%) increase in ED visits for every IQR increase in PM2.5 same day exposure. For the 19-64 years age group, we found a 6.0% (95% CI: 1.0%-11.0%) increase in ED visits for every IQR in average 3-day exposure. For the 65 years and up age group, we found a 16.0% (95% CI: 7.0%-24.0%) decrease in ED visits for every IQR increase in PM2.5 average 3-day exposure, although the number of visits in this age group was low (4,488). We found no effect modification by SES or gender.DiscussionResults from this study provide additional literature on use of satellite-driven exposure estimates in time-series analyses and evidence for the association between PM2.5 and asthma in a low- and middle-income (LMIC) country.

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.

Dataset Information

Developing an Advanced PM2.5 Exposure Model in Lima, Peru.

Publications

Developing an Advanced PM<sub>2.5</sub> Exposure Model in Lima, Peru.

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