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1senseBox | Die Kiste mit Sinn
https://sensebox.de/
6 years ago by @becker
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sensor
sensorbox
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2Feinstaubalarm in Stuttgart? | Live-Messwerte Stuttgart & Region - Stuttgarter Zeitung
https://scicar409635912018.sched.com/event/FmNX
6 years ago by @becker
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eva
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1Air Quality Measures on the National Environmental Health Tracking Network - Data.gov
https://catalog.data.gov/dataset/air-quality-measures-on-the-national-environmental-health-tracking-network
6 years ago by @becker
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1Luftverschmutzung in Stadtring Süd, Würzburg: Echtzeit-Karte des Luftqualitätsindex
http://aqicn.org/map/germany/bayern/wurzburg/stadtring-sud/de/
6 years ago by @becker
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1Airnet: a Machine Learning Dataset for Air Quality Forecasting - Semantic Scholar
https://www.semanticscholar.org/paper/Airnet%3A-a-Machine-Learning-Dataset-for-Air-Quality-XTRACTION/57e9c34b0c0ca1f5510f2be161888730ac67e02e
6 years ago by @becker
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14OpenLUR: Off-the-shelf air pollution modeling with open features and machine learning
F. Lautenschlager, M. Becker, K. Kobs, M. Steininger, P. Davidson, A. Krause, and A. Hotho. Atmospheric Environment, (2020)
4 years ago by @becker
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air
automl
bayes
eva
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lur
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project:bmbf
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airautomlbayesevaeveryawarelandlurmyownopenoptimizationproject:bmbfqualityregressionuse
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1Analysing the performance of low-cost air quality sensors, their drivers, relative benefits and calibration in cities---a case study in Sheffield
S. Munir, M. Mayfield, D. Coca, S. Jubb, and O. Osammor. Environmental Monitoring and Assessment, 191 (2): 94 (Jan 22, 2019)
5 years ago by @becker
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air
emote
eva
evaluation
plolution
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sensors
test
airemoteevaevaluationplolutionqualityreviewsensorsensorstest
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3W-Air: Enabling Personal Air Pollution Monitoring on Wearables
B. Maag, Z. Zhou, and L. Thiele. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2 (1): 24 (2018)
6 years ago by @becker
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p2map
air
quality
eva
everyaware
airprobe
airprobe2
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nn
neural
network
p2mapairqualityevaeveryawareairprobeairprobe2calibrationnnneuralnetwork
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2Variability in and Agreement between Modeled and Personal Continuously Measured Black Carbon Levels Using Novel Smartphone and Sensor Technologies
M. Nieuwenhuijsen, D. Donaire-Gonzalez, I. Rivas, M. de Castro, M. Cirach, G. Hoek, E. Seto, M. Jerrett, and J. Sunyer. Environmental Science & Technology, 49 (5): 2977--2982 (February 2015)
6 years ago by @becker
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2.5
air
bc
black
carbon
eva
map
mapping
model
p2map
pm
pm2.5
pm25
quality
school
2.5airbcblackcarbonevamapmappingmodelp2mappmpm2.5pm25qualityschool
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3A Neural Attention Model for Urban Air Quality Inference: Learning the Weights of Monitoring Stations
W. Cheng, Y. Shen, Y. Zhu, and L. Huang. AAAI, (2018)
6 years ago by @becker
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air
attention
eva
everyaware
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network
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airattentionevaeveryawaremodelnetworkneuralnnp2mapquality
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2Self-Adaptive Revised Land Use Regression Models for Estimating PM2. 5 Concentrations in Beijing, China
L. Hu, J. Liu, and Z. He. Sustainability, 8 (8): 786 (2016)
6 years ago by @becker
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p2map
eva
osm
lur
land
use
regression
open
street
map
air
quality
pollution
prediction
linear
citedby:scholar:count:4
citedby:scholar:timestamp:2018-2-21
p2mapevaosmlurlanduseregressionopenstreetmapairqualitypollutionpredictionlinearcitedby:scholar:count:4citedby:scholar:timestamp:2018-2-21
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2Land use regression models for ultrafine particles in six European areas
E. van Nunen, R. Vermeulen, M. Tsai, N. Probst-Hensch, A. Ineichen, M. Davey, M. Imboden, R. Ducret-Stich, A. Naccarati, D. Raffaele and 1 other author(s). Environmental science & technology, 51 (6): 3336--3345 (2017)
6 years ago by @becker
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air
citedby:scholar:count:7
citedby:scholar:timestamp:2018-2-21
eva
features
land
lur
map
open
osm
p2map
pollution
quality
regression
street
use
aircitedby:scholar:count:7citedby:scholar:timestamp:2018-2-21evafeatureslandlurmapopenosmp2mappollutionqualityregressionstreetuse
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6Exploring the modeling of spatiotemporal variations in ambient air pollution within the land use regression framework: Estimation of PM10 concentrations on a daily basis
M. Alam, and A. McNabola. Journal of the Air & Waste Management Association, 65 (5): 628-640 (2015)PMID: 25947321.
6 years ago by @becker
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air
eva
features
land
lur
map
open
osm
p2map
pollution
prediction
quality
regression
roads
street
use
airevafeatureslandlurmapopenosmp2mappollutionpredictionqualityregressionroadsstreetuse
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3ADVANCING THE USE OF MOBILE MONITORING DATA FOR AIR POLLUTION MODELLING
M. Adams. (2015)
6 years ago by @becker
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lur
land
use
regression
air
quality
pollution
neural
networks
nn
prediction
p2map
eva
lurlanduseregressionairqualitypollutionneuralnetworksnnpredictionp2mapeva
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6Exploring the modeling of spatiotemporal variations in ambient air pollution within the land use regression framework: Estimation of PM10 concentrations on a daily basis
M. Alam, and A. McNabola. Journal of the Air & Waste Management Association, 65 (5): 628--640 (2015)
6 years ago by @becker
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lur
eva
p2map
nn
pm10
neural
network
land
use
regression
air
pollution
quality
non
parametric
citedby:scholar:count:7
citedby:scholar:timestamp:2018-2-21
lurevap2mapnnpm10neuralnetworklanduseregressionairpollutionqualitynonparametriccitedby:scholar:count:7citedby:scholar:timestamp:2018-2-21
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2Mapping real-time air pollution health risk for environmental management: combining mobile and stationary air pollution monitoring with neural network models
M. Adams, and P. Kanaroglou. Journal of environmental management, (2016)
6 years ago by @becker
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lur
land
use
regression
air
pollution
quality
eva
p2map
neural
network
nn
mobile
stationary
prediction
citedby:scholar:count:31
citedby:scholar:timestamp:2018-2-21
lurlanduseregressionairpollutionqualityevap2mapneuralnetworknnmobilestationarypredictioncitedby:scholar:count:31citedby:scholar:timestamp:2018-2-21
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3A Spatiotemporal Prediction Framework for Air Pollution Based on Deep RNN
J. Fan, Q. Li, J. Hou, X. Feng, H. Karimian, and S. Lin. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, (2017)
6 years ago by @becker
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air
deep
eva
land
lur
network
neural
nn
p2map
pollution
quality
regression
temporal
use
airdeepevalandlurnetworkneuralnnp2mappollutionqualityregressiontemporaluse
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1Land Use Random Forests for Estimation of Exposure to Elemental Components of Particulate Matter
R. Brokamp. University of Cincinnati, (2016)
6 years ago by @becker
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air
dissertation
eva
forest
land
learning
lur
lurf
machine
p2map
pm
pollution
quality
random
regression
use
variance
airdissertationevaforestlandlearninglurlurfmachinep2mappmpollutionqualityrandomregressionusevariance
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3Exposure assessment models for elemental components of particulate matter in an urban environment: A comparison of regression and random forest approaches
C. Brokamp, R. Jandarov, M. Rao, G. LeMasters, and P. Ryan. Atmospheric Environment, (2017)
6 years ago by @becker
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air
citedby:scholar:count:11
citedby:scholar:timestamp:2018-2-21
comparison
eva
forest
land
learning
linear
lur
machine
ml
p2map
pollution
quality
random
regression
use
aircitedby:scholar:count:11citedby:scholar:timestamp:2018-2-21comparisonevaforestlandlearninglinearlurmachinemlp2mappollutionqualityrandomregressionuse
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3Mapping urban air quality in near real-time using observations from low-cost sensors and model information
P. Schneider, N. Castell, M. Vogt, F. Dauge, W. Lahoz, and A. Bartonova. Environment International, (2017)
7 years ago by @becker
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air
calibration
co2
eva
everyaware
multi
p2map
quality
sensor
aircalibrationco2evaeveryawaremultip2mapqualitysensor
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2Towards Smart City: Sensing Air Quality in City Based on Opportunistic Crowd-sensing
J. Dutta, C. Chowdhury, S. Roy, A. Middya, and F. Gazi. Proceedings of the 18th International Conference on Distributed Computing and Networking, page 42:1--42:6. New York, NY, USA, ACM, (2017)
7 years ago by @becker
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air
airprobe
collection
eva
everyaware
framework
pollution
quality
relatedwork
system
ubicon
airairprobecollectionevaeveryawareframeworkpollutionqualityrelatedworksystemubicon
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2CitiSense: improving geospatial environmental assessment of air quality using a wireless personal exposure monitoring system
N. Nikzad, N. Verma, C. Ziftci, E. Bales, N. Quick, P. Zappi, K. Patrick, S. Dasgupta, I. Krueger, T. Rosing and 1 other author(s). Proceedings of the conference on Wireless Health, page 11:1--11:8. (2012)
9 years ago by @becker
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air
citisense
collective
eva
eva21
map
participatory
proposal
quality
sensing
aircitisensecollectiveevaeva21mapparticipatoryproposalqualitysensing
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1Bridging the science-policy divide in urban air-quality management: evaluating ways to make models more robust through public engagement
S. Yearley. Environment and Planning C, 24 (5): 701 (2006)Possible motivation for using the public to verify measurements..
9 years ago by @becker
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air
collective
critique
eva
eva21
important
participatory
proposal
quality
sensing
aircollectivecritiqueevaeva21importantparticipatoryproposalqualitysensing
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2Calibrate without calibrating: an iterative approach in participatory sensing network
C. Xiang, P. Yang, C. Tian, H. Cai, and Y. Liu. Parallel and Distributed Systems, IEEE Transactions on, 26 (2): 351--361 (2015)
9 years ago by @becker
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air
airprobe
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collective
eva
eva21
everyaware
participatory
quality
sensing
airairprobecalibrationcollectiveevaeva21everyawareparticipatoryqualitysensing
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2Common sense community: scaffolding mobile sensing and analysis for novice users
W. Willett, P. Aoki, N. Kumar, S. Subramanian, and A. Woodruff. Pervasive Computing, Springer, (2010)
9 years ago by @becker
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air
airprobe
collective
eva
eva21
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participatory
quality
sensing
airairprobecollectiveevaeva21everyawareparticipatoryqualitysensing
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1senseBox | Die Kiste mit Sinn

2Feinstaubalarm in Stuttgart? | Live-Messwerte Stuttgart & Region - Stuttgarter Zeitung

1Air Quality Measures on the National Environmental Health Tracking Network - Data.gov

1Luftverschmutzung in Stadtring Süd, Würzburg: Echtzeit-Karte des Luftqualitätsindex

1Airnet: a Machine Learning Dataset for Air Quality Forecasting - Semantic Scholar

publications (hide)22
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14OpenLUR: Off-the-shelf air pollution modeling with open features and machine learning

1Analysing the performance of low-cost air quality sensors, their drivers, relative benefits and calibration in cities---a case study in Sheffield

3W-Air: Enabling Personal Air Pollution Monitoring on Wearables

2Variability in and Agreement between Modeled and Personal Continuously Measured Black Carbon Levels Using Novel Smartphone and Sensor Technologies

3A Neural Attention Model for Urban Air Quality Inference: Learning the Weights of Monitoring Stations

2Self-Adaptive Revised Land Use Regression Models for Estimating PM2. 5 Concentrations in Beijing, China

2Land use regression models for ultrafine particles in six European areas

6Exploring the modeling of spatiotemporal variations in ambient air pollution within the land use regression framework: Estimation of PM10 concentrations on a daily basis

3ADVANCING THE USE OF MOBILE MONITORING DATA FOR AIR POLLUTION MODELLING

6Exploring the modeling of spatiotemporal variations in ambient air pollution within the land use regression framework: Estimation of PM10 concentrations on a daily basis

2Mapping real-time air pollution health risk for environmental management: combining mobile and stationary air pollution monitoring with neural network models

3A Spatiotemporal Prediction Framework for Air Pollution Based on Deep RNN

1Land Use Random Forests for Estimation of Exposure to Elemental Components of Particulate Matter

3Exposure assessment models for elemental components of particulate matter in an urban environment: A comparison of regression and random forest approaches

3Mapping urban air quality in near real-time using observations from low-cost sensors and model information

2Towards Smart City: Sensing Air Quality in City Based on Opportunistic Crowd-sensing

2CitiSense: improving geospatial environmental assessment of air quality using a wireless personal exposure monitoring system

1Bridging the science-policy divide in urban air-quality management: evaluating ways to make models more robust through public engagement

2Calibrate without calibrating: an iterative approach in participatory sensing network

2Common sense community: scaffolding mobile sensing and analysis for novice users

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