24 research outputs found
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 PM<sub>10</sub> levels (lowest to highest arranged left to right) per measurement site over an entire dust season (December-April)
<p>Total PM<sub>10</sub> 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/m<sup>3</sup> increase in chronic PM<sub>2.5</sub> exposure) for all respiratory admission causes.
<p>Note: <sup>a</sup>Values 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<sub>2.5</sub> Exposures with High Spatiotemporal Resolution across the Continental United States
A number of models
have been developed to estimate PM<sub>2.5</sub> 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<sub>2.5</sub> 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><sup>2</sup> of 0.84 on the left out monitors. Regional <i>R</i><sup>2</sup> could be even higher for the Eastern and Central United States.
Model performance was still good at low PM<sub>2.5</sub> concentrations.
Then, we used the trained neural network to make daily predictions
of PM<sub>2.5</sub> at 1 km × 1 km grid cells. This model allows
epidemiologists to access PM<sub>2.5</sub> exposure in both the short-term
and the long-term
Descriptive statistics for short term PM<sub>2.5</sub> exposure, long term PM<sub>2.5</sub> 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<sub>2.5</sub> monitor.
<p>Map of the study area showing the MEDICARE population within and outside 20<sub>2.5</sub> monitor.</p