Works matching DE "AIR pollution forecasting"
Results: 78
RCP 8.5-A scenario of comparatively high greenhouse gas emissions.
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- Climatic Change, 2011, v. 109, n. 1/2, p. 33, doi. 10.1007/s10584-011-0149-y
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Temporal Variations of Major Air Pollutants and Pollution Standard Index in the Great Tehran Area.
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- Environmental Forensics, 2012, v. 13, n. 1, p. 55, doi. 10.1080/15275922.2011.643337
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A back-trajectory and air mass climatology for the Northern Shenandoah Valley, USA.
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- International Journal of Climatology, 2010, v. 30, n. 4, p. 569, doi. 10.1002/joc.1896
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Simulations of Mesoscale Circulations in the Center of the Iberian Peninsula for Thermal Low Pressure Conditions. Part II: Air-Parcel Transport Patterns.
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- Journal of Applied Meteorology (1988), 2001, v. 40, n. 5
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The Forecasting of Air Pollution in an Ecological Landfill for Urban Waste.
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- Bulletin of the University of Agricultural Sciences & Veterinary Medicine Cluj-Napoca. Agriculture, 2010, v. 67, n. 2, p. 33, doi. 10.15835/buasvmcn-agr:5177
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Atmospheric Scale Interaction on Wintertime Intermountain West Low-Level Inversions.
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- Weather & Forecasting, 2010, v. 25, n. 4, p. 1196, doi. 10.1175/2010WAF2222380.1
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3-Day-Ahead Forecasting of Regional Pollution Index for the Pollutants NO<sub>2</sub>, CO, SO<sub>2</sub>, and O<sub>3</sub> Using Artificial Neural Networks in Athens, Greece.
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- Water, Air & Soil Pollution, 2010, v. 209, n. 1-4, p. 29, doi. 10.1007/s11270-009-0179-5
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Valuation of Urban Air Pollution: A Case Study of Kanpur City in India.
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- Environmental & Resource Economics, 2008, v. 41, n. 3, p. 315, doi. 10.1007/s10640-008-9193-0
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SPAIN CONFIRMS IT WILL EXCEED AIR POLLUTANT CAPS.
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- Air Quality & Climate Change, 2009, v. 43, n. 4, p. EI-3
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Projection of CO<sub>2</sub> emissions in Malaysia.
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- Environmental Progress & Sustainable Energy, 2011, v. 30, n. 4, p. 658, doi. 10.1002/ep.10512
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Daily ambient air mean NO concentration modeling and forecasting based on the elliptic-orbit model with weekly quasi-periodic extension: a case study.
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- Stochastic Environmental Research & Risk Assessment, 2015, v. 29, n. 2, p. 547, doi. 10.1007/s00477-014-0895-2
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One-day-ahead prediction of maximum carbon monoxide concentration in urban environments.
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- Stochastic Environmental Research & Risk Assessment, 2013, v. 27, n. 2, p. 561, doi. 10.1007/s00477-012-0601-1
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Air pollutant emission rates and concentrations in medieval churches.
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- Journal of Atmospheric Chemistry, 2008, v. 60, n. 2, p. 169, doi. 10.1007/s10874-008-9116-z
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National-Scale Estimates of Ground-Level PM2.5 Concentration in China Using Geographically Weighted Regression Based on 3 km Resolution MODIS AOD.
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- Remote Sensing, 2016, v. 8, n. 3, p. 184, doi. 10.3390/rs8030184
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Intercity transferability of land use regression models for estimating ambient concentrations of nitrogen dioxide.
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- Journal of Exposure Science & Environmental Epidemiology, 2009, v. 19, n. 1, p. 107, doi. 10.1038/jes.2008.15
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Improvement of ozone forecast over Beijing based on ensemble Kalman filter with simultaneous adjustment of initial conditions and emissions.
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- Atmospheric Chemistry & Physics Discussions, 2011, v. 11, n. 3, p. 7811, doi. 10.5194/acpd-11-7811-2011
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Overview of the European project FUMAPEX.
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- Atmospheric Chemistry & Physics Discussions, 2005, v. 5, n. 6, p. 11679, doi. 10.5194/acpd-5-11679-2005
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Evaluation of very high-resolution simulations with the non-hydrostatic numerical weather prediction model Lokalmodell for urban air pollution episodes in Helsinki, Oslo and Valencia.
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- Atmospheric Chemistry & Physics Discussions, 2005, v. 5, n. 5, p. 8233, doi. 10.5194/acpd-5-8233-2005
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The shipping forecast.
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- Nature, 2007, v. 449, n. 7160, p. 272, doi. 10.1038/449272a
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Air pollution forecasting from sky images with shallow and deep classifiers.
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- Earth Science Informatics, 2018, v. 11, n. 3, p. 413, doi. 10.1007/s12145-018-0334-x
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Low-carbon scenario analysis on urban transport of one metropolitan in China in 2020.
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- Journal of Industrial Engineering & Management, 2012, v. 5, n. 2, p. 344, doi. 10.3926/jiem.640
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Hybrid Model for Urban Air Pollution Forecasting: A Stochastic Spatio-Temporal Approach.
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- Mathematical Geosciences, 2014, v. 46, n. 1, p. 75, doi. 10.1007/s11004-013-9483-0
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Voluntary Greenhouse Gas Reporting—Not an Option for Much Longer.
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- Environmental Claims Journal, 2008, v. 20, n. 2, p. 110, doi. 10.1080/10406020802045739
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Comparisons of Three-Dimensional Variational Data Assimilation and Model Output Statistics in Improving Atmospheric Chemistry Forecasts.
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- Advances in Atmospheric Sciences, 2018, v. 35, n. 7, p. 813, doi. 10.1007/s00376-017-7179-y
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Skill in forecasting extreme ozone pollution episodes with a global atmospheric chemistry model.
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- Atmospheric Chemistry & Physics, 2014, v. 14, n. 5, p. 6261, doi. 10.5194/acpd-14-6261-2014
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A Bayesian inversion estimate of N<sub>2</sub>O emissions for western and central Europe and the assessment of aggregation errors.
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- Atmospheric Chemistry & Physics, 2011, v. 11, n. 7, p. 3443, doi. 10.5194/acp-11-3443-2011
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Projections of air pollutant emissions and its impacts on regional air quality in China in 2020.
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- Atmospheric Chemistry & Physics, 2011, v. 11, n. 7, p. 3119, doi. 10.5194/acp-11-3119-2011
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Impact of model grid spacing on regional- and urban- scale air quality predictions of organic aerosol.
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- Atmospheric Chemistry & Physics, 2011, v. 11, n. 7, p. 3107, doi. 10.5194/acp-11-3107-2011
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Comparison of OMI NO<sub>2</sub> tropospheric columns with an ensemble of global and European regional air quality models.
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- Atmospheric Chemistry & Physics, 2010, v. 10, n. 7, p. 3273, doi. 10.5194/acp-10-3273-2010
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The impact of biogenic carbon sources on aerosol absorption in Mexico City.
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- Atmospheric Chemistry & Physics, 2009, v. 9, n. 5, p. 1537, doi. 10.5194/acp-9-1537-2009
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Computation of geographic variables for air pollution prediction models in South Korea.
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- Environmental Health & Toxicology, 2015, v. 30, p. 1, doi. 10.5620/eht.e2015010
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Accounting for model error in air quality forecasts: an application of 4DEnVar to the assimilation of atmospheric composition using QG-Chem 1.0.
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- Geoscientific Model Development, 2016, v. 9, n. 11, p. 3933, doi. 10.5194/gmd-9-3933-2016
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Artificial Intelligence applied in Air Pollution Forecast.
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- Petroleum - Gas University of Ploiesti Bulletin, Technical Series, 2009, v. 61, n. 3, p. 389
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Markov Weighted Fuzzy Time-Series Model Based on an Optimum Partition Method for Forecasting Air Pollution.
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- International Journal of Fuzzy Systems, 2020, v. 22, n. 5, p. 1468, doi. 10.1007/s40815-020-00841-w
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LaSVM-based big data learning system for dynamic prediction of air pollution in Tehran.
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- Environmental Monitoring & Assessment, 2018, v. 190, n. 5, p. 1, doi. 10.1007/s10661-018-6659-6
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Multiple linear regression and regression with time series error models in forecasting PM<sub>10</sub> concentrations in Peninsular Malaysia.
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- Environmental Monitoring & Assessment, 2018, v. 190, n. 2, p. 1, doi. 10.1007/s10661-017-6419-z
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Air pollution forecasting in Ankara, Turkey using air pollution index and its relation to assimilative capacity of the atmosphere.
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- Environmental Monitoring & Assessment, 2010, v. 166, n. 1-4, p. 11, doi. 10.1007/s10661-009-0981-y
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On the evaluation of box model results: the case of BOXURB model.
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- Environmental Monitoring & Assessment, 2009, v. 155, n. 1-4, p. 103, doi. 10.1007/s10661-008-0421-4
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Estimating Air Quality in a Traffic Tunnel Using a Forecasting Combination Model.
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- Environmental Monitoring & Assessment, 2006, v. 112, n. 1-3, p. 327, doi. 10.1007/s10661-006-1073-x
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Using Improved Neural Network Model to Analyze RSP, NO<sub>x</sub> and NO<sub>2</sub> Levels in Urban Air in Mong Kok, Hong Kong.
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- Environmental Monitoring & Assessment, 2003, v. 87, n. 3, p. 235, doi. 10.1023/A:1024819309108
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A Spatiotemporal Prediction Framework for Air Pollution Based on Deep RNN.
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- ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences, 2017, v. 4, n. 4/W2, p. 15, doi. 10.5194/isprs-annals-IV-4-W2-15-2017
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The Cost of Keeping Our Cool.
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- Current History, 2007, v. 106, n. 703, p. 384, doi. 10.1525/curh.2007.106.703.384
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Can the World Wean Itself from Fossil Fuels?
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- Current History, 2007, v. 106, n. 703, p. 376, doi. 10.1525/curh.2007.106.703.376
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FEATURE SELECTION FOR THE PREDICTION OF TROPOSPHERIC OZONE CONCENTRATION USING A WRAPPER METHOD.
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- Intelligent Automation & Soft Computing, 2011, v. 17, n. 4, p. 403, doi. 10.1080/10798587.2011.10643157
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Predicting Chronic Fine and Coarse Particulate Exposures Using Spatiotemporal Models for the Northeastern and Midwestern United States.
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- Environmental Health Perspectives, 2009, v. 117, n. 4, p. 522, doi. 10.1289/ehp.11692
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RAQ--A Random Forest Approach for Predicting Air Quality in Urban Sensing Systems.
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- Sensors (14248220), 2016, v. 16, n. 1, p. 86, doi. 10.3390/s16010086
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Multivariate Receptor Models for Spatially Correlated Multipollutant Data.
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- Technometrics, 2013, v. 55, n. 3, p. 309, doi. 10.1080/00401706.2013.765321
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The impact of chemical lateral boundary conditions on CMAQ predictions of tropospheric ozone over the continental United States.
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- Environmental Fluid Mechanics, 2009, v. 9, n. 1, p. 43, doi. 10.1007/s10652-008-9092-5
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Forecasting SO<sub>2</sub> pollution incidents by means of quantile curves based on additive models.
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- Environmetrics, 2016, v. 27, n. 3, p. 147, doi. 10.1002/env.2384
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The inclusion of exogenous variables in functional autoregressive ozone forecasting .
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- Environmetrics, 2002, v. 13, n. 7, p. 759, doi. 10.1002/env.527
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