• Corpus ID: 16801207

A Cloud-Based Knowledge Discovery System for Monitoring Fine-Grained Air Quality

  title={A Cloud-Based Knowledge Discovery System for Monitoring Fine-Grained Air Quality},
  author={Yu Zheng and Xuxu Chen and Qiwei Jin and Yubiao Chen and Xiangyun Qu and Xin Liu and Eric Chang and Wei-Ying Ma and Yongqin Rui and Weiwei Sun},
Many developing countries are suffering from air pollution recently. Governments have built a few air quality monitoring stations in cities to inform people the concentration of air pollutants. Unfortunately, urban air quality is highly skewed in a city, depending on multiple complex factors, such as the meteorology, traffic volume, and land uses. Building more monitoring stations is very costly in terms of money, land uses, and human resources. As a result, people do not really know the fine… 
Inferring Air Quality for Station Location Recommendation Based on Urban Big Data
A semi-supervised inference model utilizing existing monitoring data together with heterogeneous city dynamics, including meteorology, human mobility, structure of road networks, and point of interests is designed and an entropy-minimization model is proposed to suggest the best locations to establish new monitoring stations.
A Crowdsource-Based Sensing System for Monitoring Fine-Grained Air Quality in Urban Environments
An intelligent algorithm is developed to detect vehicular air exchange state, then extracts the concentration of pollutant in the condition that the concentration trend is convergent after opening the windows, and the sensed convergent value is denoted as the equivalent air quality level of the surrounding environment.
Forecasting Fine-Grained Air Quality Based on Big Data
In this paper, we forecast the reading of an air quality monitoring station over the next 48 hours, using a data-driven method that considers current meteorological data, weather forecasts, and air
AirVis: Visual Analytics of Air Pollution Propagation
AirVis is developed, a novel visual analytics system that assists domain experts in efficiently capturing and interpreting the uncertain propagation patterns of air pollution based on graph visualizations and develops a novel pattern mining framework to model pollutant transportation and extract frequent propagation patterns efficiently from large-scale atmospheric data.
AirCloud: a cloud-based air-quality monitoring system for everyone
This work presents the design, implementation, and evaluation of AirCloud -- a novel client-cloud system for pervasive and personal air-quality monitoring at low cost, and shows that AirCloud is able to achieve good accuracies at much lower cost than previous solutions.
Towards a Framework Air Pollution Monitoring System Based on IoT Technology
One of the most discussed and concerning environmental issues nowadays is air pollution. Fast-growing population and urbanization have resulted in deteriorated air quality in urban areas.
Fast Fine-Grained Air Quality Index Level Prediction Using Random Forest Algorithm on Cluster Computing of Spark
  • Chuanting Zhang, D. Yuan
  • Computer Science
    2015 IEEE 12th Intl Conf on Ubiquitous Intelligence and Computing and 2015 IEEE 12th Intl Conf on Autonomic and Trusted Computing and 2015 IEEE 15th Intl Conf on Scalable Computing and Communications and Its Associated Workshops (UIC-ATC-ScalCom)
  • 2015
An air quality prediction model using the parallelized random forest algorithm implemented using Spark on the basis of resilient distributed dataset and shared variable and the results prove the effectiveness and scalability of the method when deal with big data.
From What and When Happen, to Why Happen in Air Pollution Using Open Big Data
The idea is to connect unstructured data and structured spatial data through the reconciliation of spatial-temporal correspondences between them to discover new geographic knowledge on Air Pollution phenomenon in Mexico City.
PGA: Physics Guided and Adaptive Approach for Mobile Fine-Grained Air Pollution Estimation
PGA, a physics guided and adaptive approach to estimate fine-grained air pollution with vehicle fleets is presented, which achieves up to 4.0× reduction on average error compared to state-of-the-art approaches.
Regional air quality forecasting using spatiotemporal deep learning
A hierarchical deep learning model named DL-Air that embodies three components for air quality forecasting, which shows better performance with around 30% reduced RMSE and MAE, 37% reduced AAD, 11% improved R2 and 8% improved accuracy in AQI category prediction than the best performing baseline approaches.


U-Air: when urban air quality inference meets big data
This paper infer the real-time and fine-grained air quality information throughout a city, based on the (historical and real- time) air quality data reported by existing monitor stations and a variety of data sources the authors observed in the city, such as meteorology, traffic flow, human mobility, structure of road networks, and point of interests (POIs).
Discovering regions of different functions in a city using human mobility and POIs
This paper proposes a framework (titled DRoF) that Discovers Regions of different Functions in a city using both human mobility among regions and points of interests (POIs) located in a region.
MAQS: a personalized mobile sensing system for indoor air quality monitoring
This paper describes MAQS, a personalized mobile sensing system for IAQ monitoring that incorporates an accurate temporal n-gram augmented Bayesian room localization method that requires few Wi-Fi fingerprints and a zone-based proximity detection method for collaborative sensing, which saves energy and enables data sharing among users.
Modelling air quality in street canyons : a review
High pollution levels have been often observed in urban street canyons due to the increased traffic emissions and reduced natural ventilation. Microscale dispersion models with different levels of
An Interactive-Voting Based Map Matching Algorithm
This work proposes an Interactive Voting-based Map Matching (IVMM) algorithm that does not only consider the spatial and temporal information of a GPS trajectory but also devise a voting-based strategy to model the weighted mutual influences between GPS points.
Urban computing with taxicabs
Failed urban planning is detected using the GPS trajectories of taxicabs traveling in urban areas using the trajectories generated by 30,000 taxis from March to May in 2009 and 2010 in Beijing, and the results can evaluate the effectiveness of the carried out planning.
Analyzing the effectiveness and applicability of co-training
It is demonstrated that when learning from labeled and unlabeled data, algorithms explicitly leveraging a natural independent split of the features outperform algorithms that do not and may out-perform algorithms not using a split.