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Estimation of citywide air pollution in Beijing.

ABSTRACT: There has been discrepancies between the daily air quality reports of the Beijing municipal government, observations recorded at the U.S. Embassy in Beijing, and Beijing residents' perceptions of air quality. This study estimates Beijing's daily area PM(2.5) mass concentration by means of a novel technique SPA (Single Point Areal Estimation) that uses data from the single PM(2.5) observation station of the U.S Embassy and the 18 PM(10) observation stations of the Beijing Municipal Environmental Protection Bureau. The proposed technique accounts for empirical relationships between different types of observations, and generates best linear unbiased pollution estimates (in a statistical sense). The technique extends the daily PM(2.5) mass concentrations obtained at a single station (U.S. Embassy) to a citywide scale using physical relations between pollutant concentrations at the embassy PM(2.5) monitoring station and at the 18 official PM(10) stations that are evenly distributed across the city. Insight about the technique's spatial estimation accuracy (uncertainty) is gained by means of theoretical considerations and numerical validations involving real data. The technique was used to study citywide PM(2.5) pollution during the 423-day period of interest (May 10, 2010 to December 6, 2011). Finally, a freely downloadable software library is provided that performs all relevant calculations of pollution estimation.

SUBMITTER: Wang JF

PROVIDER: S-EPMC3539974 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundPost-transcriptional modifications of RNA constitute fundamental mechanisms of gene regulation. N6-methyladenosine (m6A) is critical for health and disease and is modulated by cellular stressors. However, associations between environmental exposures and m6A have not been studied in humans. We aimed to examine associations between tobacco smoking and particulate air pollution with m6A and mRNA expression levels of its reader, writer and eraser (RWE) genes in blood.MethodsUsing the Beijing Truck Driver Air Pollution Study, we investigated global m6A in RNA from peripheral blood collected from 106 human subjects in Beijing, China, in 2008. We measured m6A with nano-flow liquid chromatography-tandem mass spectrometry and investigated gene expression of six m6A RWEs with real-time-quantitative PCR. Using linear models, we examined associations with smoking status, pack-years, and smoking on day of visit in men, and with environmental tobacco smoke in nonsmokers. We also examined associations with ambient PM10 (particulate matter ≤ 10 µm in diameter), and personal black carbon (BC) and PM2.5 measured with a portable monitor.ResultsSmoking in men was significantly associated with a relative 10.7% decrease in global m6A levels in comparison to nonsmokers (p = 0.02). In men, smoking greater than 3.8 pack-years was associated with a 14.9% lower m6A than in nonsmokers. BC exposure trended towards positive associations with m6A (5.95% per 10 μg/m3 increase in BC; 95% CI: -0.96, 13.3). Global m6A levels were not correlated with RWE gene expression levels. No associations were detected between smoking or air pollutants and m6A RWE gene expression.Discussionm6A was negatively associated with long-term smoking, yet positively associated with short-term BC exposure. These results indicate variable m6A responses to environmental stressors, providing early evidence into the impacts of toxicants on RNA modifications and suggesting potential for m6A as a biomarker or mechanism in environmental health research.

Project description:BackgroundThe era of big data has enabled sophisticated models to predict air pollution concentrations over space and time. Historically these models have been evaluated using overall metrics that measure how close predictions are to monitoring data. However, overall methods are not designed to distinguish error at timescales most relevant for epidemiologic studies, such as day-to-day errors that impact studies of short-term health associations.MethodsWe introduce frequency band model performance, which quantifies health estimation capacity of air quality prediction models for time series studies of air pollution and health. Frequency band model performance uses a discrete Fourier transform to evaluate prediction models at timescales of interest. We simulated fine particulate matter (PM2.5), with errors at timescales varying from acute to seasonal, and health time series data. To compare evaluation approaches, we use correlations and root mean squared error (RMSE). Additionally, we assess health estimation capacity through bias and RMSE in estimated health associations. We apply frequency band model performance to PM2.5 predictions at 17 monitors in 8 US cities.ResultsIn simulations, frequency band model performance rates predictions better (lower RMSE, higher correlation) when there is no error at a particular timescale (e.g., acute) and worse when error is added to that timescale, compared to overall approaches. Further, frequency band model performance is more strongly associated (R2 = 0.95) with health association bias compared to overall approaches (R2 = 0.57). For PM2.5 predictions in Salt Lake City, UT, frequency band model performance better identifies acute error that may impact estimated short-term health associations.ConclusionsFor epidemiologic studies, frequency band model performance provides an improvement over existing approaches because it evaluates models at the timescale of interest and is more strongly associated with bias in estimated health associations. Evaluating prediction models at timescales relevant for health studies is critical to determining whether model error will impact estimated health associations.

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Estimation of citywide air pollution in Beijing.

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