Dataset Information

WRF 1960-2014 Winter Season Simulations of Particulate Matter in the Sahel: Implications for Air Quality and Respiratory Health.

ABSTRACT: We use the Weather Research and Forecast model using the Goddard Global Ozone Chemistry Aerosol Radiation and Transport (GOCART) dust module (WRF-CHEM) to simulate the particulate matter (PM) variations in the Sahel during the winter seasons (January-March) of 1960-2014. Two simulations are undertaken where the direct aerosol feedback is turned off, and only transport is considered and where the direct aerosol feedback is turned on. We find that simulated Sahelian PM10 and PM2.5 concentrations were lower in the 1960s and after 2003 and higher during the period between 1988 and 2002. Higher Sahelian PM10 concentrations are due to stronger winds between the surface and 925 hPa over the Sahara, which transport dust into the Sahel. Negative PM10 concentration anomalies are found over the Bodele Depression and associated with weaker 925 wind anomalies after 1997 through 2014. Further west, positive PM10 concentration anomalies are found across the Adrar Plateau in the Sahara and responsible for dust transport to the Western Sahel. The North Atlantic Oscillation (NAO) is positively correlated to Sahelian dust concentrations especially during the periods of 1960-1970 and 1988-2002. The temporal/spatial patterns of PM10 concentrations have significant respiratory health implications for inhabitants of the Sahel.

SUBMITTER: Jenkins GS

PROVIDER: S-EPMC7007090 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

WRF 1960-2014 Winter Season Simulations of Particulate Matter in the Sahel: Implications for Air Quality and Respiratory Health.

Jenkins Gregory S GS Gueye Moussa M

GeoHealth 20180821 8

We use the Weather Research and Forecast model using the Goddard Global Ozone Chemistry Aerosol Radiation and Transport (GOCART) dust module (WRF-CHEM) to simulate the particulate matter (PM) variations in the Sahel during the winter seasons (January-March) of 1960-2014. Two simulations are undertaken where the direct aerosol feedback is turned off, and only transport is considered and where the direct aerosol feedback is turned on. We find that simulated Sahelian PM<sub>10</sub> and PM<sub>2.5< ...[more]

PMID: 32159017

Similar Datasets

Project description:Autism is a heterogeneous disorder with genetic and environmental factors likely contributing to its origins. Examination of hazardous pollutants has suggested the importance of air toxics in the etiology of autism, yet little research has examined its association with local levels of air pollution using residence-specific exposure assignments.To examine the relationship between traffic-related air pollution, air quality, and autism.This population-based case-control study includes data obtained from children with autism and control children with typical development who were enrolled in the Childhood Autism Risks from Genetics and the Environment study in California. The mother's address from the birth certificate and addresses reported from a residential history questionnaire were used to estimate exposure for each trimester of pregnancy and first year of life. Traffic-related air pollution was assigned to each location using a line-source air-quality dispersion model. Regional air pollutant measures were based on the Environmental Protection Agency's Air Quality System data. Logistic regression models compared estimated and measured pollutant levels for children with autism and for control children with typical development.Case-control study from California.A total of 279 children with autism and a total of 245 control children with typical development.Crude and multivariable adjusted odds ratios (AORs) for autism.Children with autism were more likely to live at residences that had the highest quartile of exposure to traffic-related air pollution, during gestation (AOR, 1.98 [95% CI, 1.20-3.31]) and during the first year of life (AOR, 3.10 [95% CI, 1.76-5.57]), compared with control children. Regional exposure measures of nitrogen dioxide and particulate matter less than 2.5 and 10 ?m in diameter (PM2.5 and PM10) were also associated with autism during gestation (exposure to nitrogen dioxide: AOR, 1.81 [95% CI, 1.37-3.09]; exposure to PM2.5: AOR, 2.08 [95% CI, 1.93-2.25]; exposure to PM10: AOR, 2.17 [95% CI, 1.49-3.16) and during the first year of life (exposure to nitrogen dioxide: AOR, 2.06 [95% CI, 1.37-3.09]; exposure to PM2.5: AOR, 2.12 [95% CI, 1.45-3.10]; exposure to PM10: AOR, 2.14 [95% CI, 1.46-3.12]). All regional pollutant estimates were scaled to twice the standard deviation of the distribution for all pregnancy estimates.Exposure to traffic-related air pollution, nitrogen dioxide, PM2.5, and PM10 during pregnancy and during the first year of life was associated with autism. Further epidemiological and toxicological examinations of likely biological pathways will help determine whether these associations are causal.

Project description:Using daily fine particulate matter (PM2.5) composition data from the 2000-2005 U.S. EPA Chemical Speciation Network (CSN) for over 200 sites, we applied multivariate methods to identify and quantify the major fine particulate matter (PM2.5) source components in the U.S. Novel aspects of this work were: (1) the application of factor analysis (FA) to multi-city daily data, drawing upon both spatial and temporal variations of chemical species; and, (2) the exclusion of secondary components (sulfates, nitrates and organic carbon) from the source identification FA to more clearly discern and apportion the PM2.5 mass to primary emission source categories. For the quantification of source-related mass, we considered two approaches based upon the FA results: 1) using single key tracers for sources identified by FA in a mass regression; and, 2) applying Absolute Principal Component Analysis (APCA). In each case, we followed a two-stage mass regression approach, in which secondary components were first apportioned among the identified sources, and then mass was apportioned to the sources and to other secondary mass not explained by the individual sources. The major U.S. PM2.5 source categories identified via FA (and their key elements) were: Metals Industry (Pb, Zn); Crustal/Soil Particles (Ca, Si); Motor Vehicle Traffic (EC, NO2); Steel Industry (Fe, Mn); Coal Combustion (As, Se); Oil Combustion (V, Ni); Salt Particles (Na, Cl) and Biomass Burning (K). Nationwide spatial plots of the source-related PM2.5 impacts were confirmatory of the factor interpretations: ubiquitous sources, such as Traffic and Soil, were found to be spread across the nation, more unique sources (such as Steel and Metals Processing) being highest in select industrialized cities, Biomass Burning was highest in the U.S. Northwest, while Residual Oil combustion was highest in cities in the Northeastern U.S. and in cities with major seaports. The sum of these source contributions and the secondary PM2.5 components agreed well with the U.S. PM2.5 observed during the study period (mean=14.3 ug/m3; R2= 0.91). Apportionment regression analyses using single-element tracers for each source category gave results consistent with the APCA estimates. Comparisons of nearby sites indicated that the PM2.5 mass and the secondary aerosols were most homogenous spatially, while traffic PM2.5 and its tracer, EC, were among the most spatially representative of the source-related components. Comparison of apportionment results to a previous analysis of the 1979-1982 IP Network revealed similar and correlated major U.S. source category factors, albeit at lower levels than in the earlier period, suggesting a consistency in the U.S. spatial patterns of these source-related exposures over time, as well. These results indicate that applying source apportionment methods to the nationwide CSN can be an informative avenue for identifying and quantifying source components for the subsequent estimation of source-specific health effects, potentially contributing to more efficient regulation of PM2.5.

Project description:BackgroundEpigenetic age, as defined by DNA methylation, may be influenced by air pollution exposure.ObjectiveTo evaluate the relationship between NO2, particulate matter (PM), PM components and accelerated epigenetic age.MethodsIn a sample of non-Hispanic white women living in the contiguous U.S. (n = 2747), we estimated residential exposure to PM2.5, PM10 and NO2 using a model incorporating land-use regression and kriging. Predictive k-means was used to assign participants to clusters representing different PM2.5 component profiles. We measured DNA methylation (DNAm) in blood using the Illumina's Infinium HumanMethylation450 BeadChip and calculated DNAm age using the Hannum, Horvath and Levine epigenetic clocks. Age acceleration was defined based on residuals after regressing DNAm age on chronological age. We estimated associations between interquartile range (IQR) increases in pollutants and age acceleration using linear regression. For PM2.5, we stratified by cluster membership. We examined epigenome-wide associations using robust linear regression models corrected with false discovery rate q-values.ResultsNO2 was inversely associated with age acceleration using the Hannum clock (β = -0.24, 95% CI: -0.47, -0.02). No associations were observed for PM10. For PM2.5, the association with age acceleration varied by PM2.5 component cluster. For example, with the Levine clock, an IQR increase in PM2.5 was associated with an over 6-year age acceleration in a cluster that has relatively high fractions of crustal elements relative to overall PM2.5 (β = 6.57, 95% CI: 2.68, 10.47), and an almost 2-year acceleration in a cluster characterized by relatively low sulfur fractions (β = 1.88, 95% CI: 0.51, 3.25). In a cluster distinguished by lower relative nitrate concentrations, PM2.5 was inversely associated with age acceleration (β = -1.33, 95% CI: -2.43, -0.23). Across the epigenome, NO2 was associated with methylation at 2 CpG sites.ConclusionAir pollution was associated with epigenetic age, a marker of mortality and disease risk, among certain PM2.5 component profiles.

Dataset Information

WRF 1960-2014 Winter Season Simulations of Particulate Matter in the Sahel: Implications for Air Quality and Respiratory Health.

Publications

WRF 1960-2014 Winter Season Simulations of Particulate Matter in the Sahel: Implications for Air Quality and Respiratory Health.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets