Beer-Sheva monitoring station data collected during measurement time.

<p>WS = wind speed; WD = wind direction; SS = synoptic system.</p

Stellaria monosperma Hamilt. var. japonica Maxim.

原著和名: オホヤマハコベ科名: ナデシコ科 = Caryophyllaceae採集地: 群馬県 碓氷郡 松井田町 横川 (丁須峯口) (上野 碓氷郡 松井田町 横川 (丁須峯口))採集日: 1983/8/22採集者: 萩庭丈壽整理番号: JH008480国立科学博物館整理番号: TNS-VS-95848

Map showing the locations of 23 measurement points (red dots) around the city of Beer-Sheva.

<p>Location of the Beer Sheva monitoring station is indicated by a yellow dot.</p

Total PM₁₀ levels (lowest to highest arranged left to right) per measurement site over an entire dust season (December-April)

<p>Total PM₁₀ levels (lowest to highest arranged left to right) per measurement site over an entire dust season (December-April)</p

Descriptive statistics stratified by long term exposure: Hospital admissions by type of admission across New-England for the years 2000–2006.

<p>Descriptive statistics stratified by long term exposure: Hospital admissions by type of admission across New-England for the years 2000–2006.</p

Sensitivity analysis (estimated percent increase in hospital admissions for a 10 μg/m³ increase in chronic PM_2.5 exposure) for all respiratory admission causes.

<p>Note: ^aValues are percent.</p

Map of the study area showing the residential location of admission cases juxtaposed over a sample PM2.5 10×10 km pollution grid for 01/07/2001.

<p> Map of the study area showing the residential location of admission cases juxtaposed over a sample PM2.5 10×10 km pollution grid for 01/07/2001.</p

Assessing PM_2.5 Exposures with High Spatiotemporal Resolution across the Continental United States

A number of models have been developed to estimate PM_2.5 exposure, including satellite-based aerosol optical depth (AOD) models, land-use regression, or chemical transport model simulation, all with both strengths and weaknesses. Variables like normalized difference vegetation index (NDVI), surface reflectance, absorbing aerosol index, and meteoroidal fields are also informative about PM_2.5 concentrations. Our objective is to establish a hybrid model which incorporates multiple approaches and input variables to improve model performance. To account for complex atmospheric mechanisms, we used a neural network for its capacity to model nonlinearity and interactions. We used convolutional layers, which aggregate neighboring information, into a neural network to account for spatial and temporal autocorrelation. We trained the neural network for the continental United States from 2000 to 2012 and tested it with left out monitors. Ten-fold cross-validation revealed a good model performance with a total <i>R</i>² of 0.84 on the left out monitors. Regional <i>R</i>² could be even higher for the Eastern and Central United States. Model performance was still good at low PM_2.5 concentrations. Then, we used the trained neural network to make daily predictions of PM_2.5 at 1 km × 1 km grid cells. This model allows epidemiologists to access PM_2.5 exposure in both the short-term and the long-term

Descriptive statistics for short term PM_2.5 exposure, long term PM_2.5 exposure and temperature in New-England for 2000–2006.

<p>Note: Q1 and Q3 are quartiles.</p

Map of the study area showing the MEDICARE population within and outside 20_2.5 monitor.

<p>Map of the study area showing the MEDICARE population within and outside 20_2.5 monitor.</p