Methods for characterizing fine particulate matter using ground observations and remotely sensed data: potential use for environmental public health surveillance.
about
The public health exposome: a population-based, exposure science approach to health disparities researchEnvironmental Public Health Applications Using Remotely Sensed Data.Analysis of an environmental exposure health questionnaire in a metropolitan minority population utilizing logistic regression and Support Vector Machines.Spatio-temporal modeling of particulate air pollution in the conterminous United States using geographic and meteorological predictors.Fine particulate matter and incident cognitive impairment in the REasons for Geographic and Racial Differences in Stroke (REGARDS) cohortLocal Variability in the Impacts of Residential Particulate Matter and Pest Exposure on Children's Wheezing Severity: A Geographically Weighted Regression Analysis of Environmental Health Justice.Air quality measurements-From rubber bands to tapping the rainbow.Heat Maps of Hypertension, Diabetes Mellitus, and Smoking in the Continental United States.Fine particulate air pollution and premature ventricular contractions: The REasons for Geographic And Racial Differences in Stroke (REGARDS) Study.Fine Particulate Matter (PM2.5) and the Risk of Stroke in the REGARDS Cohort.The association of remotely sensed outdoor fine particulate matter with cancer incidence of respiratory system in the USA.Spatio-temporal models to estimate daily concentrations of fine particulate matter in Montreal: Kriging with external drift and inverse distance-weighted approaches.Investigating the effects of environmental factors on autism spectrum disorder in the USA using remotely sensed data.Fine particulate matter and incident coronary heart disease in the REGARDS cohort.Fine Particulate Matter Pollution and Risk of Community-Acquired Sepsis.Pollution monitoring at heritage sites in developing and emerging economies
P2860
Q28384998-489C904C-8518-4C53-9109-C537F2A4A7A7Q30829877-899C6434-24B9-4976-86D8-5053FE2A8FA3Q33771762-ED22DF18-C36F-47E6-B186-406B0F1CA495Q34063296-7BB56E0B-887F-4BF0-B59E-2BC4DA395C5BQ35005323-5138F8C4-A305-4B90-8CF6-BF43286A0335Q36233315-EC3B9561-07C8-4C8F-8B17-988E0ABB09F5Q38883499-BF9F6002-1382-4757-8CAE-771144809654Q39028365-23BDCC60-02A0-45D4-8E4C-10893A136738Q39035623-7DF6450B-4031-4DBB-9D9B-225BD2AD2A58Q40221885-89D241CB-08FB-4285-AC27-54CF2B2543DEQ40300498-10B6E781-AA11-4ADA-8F2C-AA6E87EF0C67Q40336241-BE407416-F2D9-4C29-A140-2F99740EC9A2Q47201485-8A642E28-CCD1-498E-B1E4-867E15213670Q49972073-ED402994-6C45-4347-8001-75B560C06F32Q55346197-31ED9646-1CAC-427E-AF91-F5FDBFC65A3CQ58586579-B12D5E7F-E4C7-4811-AEB0-93D8D1366EB9
P2860
Methods for characterizing fine particulate matter using ground observations and remotely sensed data: potential use for environmental public health surveillance.
description
2009 nî lūn-bûn
@nan
2009 թուականի Յուլիսին հրատարակուած գիտական յօդուած
@hyw
2009 թվականի հուլիսին հրատարակված գիտական հոդված
@hy
2009年の論文
@ja
2009年論文
@yue
2009年論文
@zh-hant
2009年論文
@zh-hk
2009年論文
@zh-mo
2009年論文
@zh-tw
2009年论文
@wuu
name
Methods for characterizing fin ...... al public health surveillance.
@ast
Methods for characterizing fin ...... al public health surveillance.
@en
type
label
Methods for characterizing fin ...... al public health surveillance.
@ast
Methods for characterizing fin ...... al public health surveillance.
@en
prefLabel
Methods for characterizing fin ...... al public health surveillance.
@ast
Methods for characterizing fin ...... al public health surveillance.
@en
P2093
P1476
Methods for characterizing fin ...... al public health surveillance.
@en
P2093
Amanda Sue Niskar
Amber H Sinclair
Ashutosh S Limaye
Dale A Quattrochi
Judith R Qualters
Kafayat A Adeniyi
Maurice G Estes
Mohammad Z Al-Hamdan
William L Crosson
P2860
P304
P356
10.3155/1047-3289.59.7.865
P577
2009-07-01T00:00:00Z