Particular matter prediction using synergy of multiple source urban big data in smart cities

2021 ◽  
pp. 1-15
Author(s):  
Ali Reza Honarvar ◽  
Ashkan Sami
Keyword(s):  
Air Pollution ◽  
Big Data ◽  
Air Quality ◽  
Smart Cities ◽  
Data Sources ◽  
Quality Data ◽  
Data Set ◽  
Multiple Data Sources ◽  
Multiple Data ◽  
Air Quality Data

At present, the issue of air quality in populated urban areas is recognized as an environmental crisis. Air pollution affects the sustainability of the city. In controlling air pollution and protecting its hazards from humans, air quality data are very important. However, the costs of constructing and maintaining air quality registration infrastructure are very expensive and high, and air quality data recording at one point will not be generalizable to even a few kilometers. Some of the gains come from the integration of multiple data sources, which can never be achieved through independent single-source processing. Urban organizations in each city independently produce and record data relevant to the organization’s goals and objectives. These issues create separate data silos associated with an urban system. These data are varied in model and structure, and the integration of such data provides an appropriate opportunity to discover knowledge that can be useful in urban planning and decision making. This paper aims to show the generality of our previous research, which proposed a novel model to predict Particulate Matter (PM) as the main factor of air quality in the regions of the cities where air quality sensors are not available through urban big data resources integration, by extending the model and experiments with various configuration for different settings in smart cities. This work extends the evaluation scenarios of the model with the extended dataset of city of Aarhus, in Denmark, and compare the model performance against various specified baselines. Details of removing the heterogeneity of multiple data sources in the Multiple Data Set Aggregator & Heterogeneity Remover (MDA&HR) and improving the operation of Train Data Splitter (TDS) part of the model by focusing on the finding more similar pattern of air quality also are presented in this paper. The acceptable accuracy of the results shows the generality of the model.

scholarly journals APPLICATION OF OPEN AIR MODEL (R PACKAGE) TO ANALYZE AIR POLLUTION DATA

2017 ◽  
Vol 1 (1) ◽  
pp. 94 ◽  
Author(s):  
Intan Agustine ◽  
Hernani Yulinawati ◽  
Endro Suswantoro ◽  
Dodo Gunawan
Keyword(s):  
Air Pollution ◽  
Big Data ◽  
Air Quality ◽  
Meteorological Data ◽  
Ambient Air ◽  
Quality Data ◽  
Fluctuation Analysis ◽  
Ambient Air Quality ◽  
Long Time ◽  
Air Quality Data

Air pollution problem is faced by many countries in the world. Ambient air quality studies and monitoring need a long time period of data to cover various atmospheric conditions, which create big data. A tool is needed to make easier and more effective to analyze big data. <strong>Aims: </strong>This study aims to analyze various application of <em>openair</em> model, which is available in open-source, for analyzing urban air quality data. <strong>Methodology and results: </strong>Each pollutant and meteorological data were collected through their sampling-analysis methods (active, passive or real-time) from a certain period of time. The data processed and imported in the <em>openair</em> model were presented in <em>comma separated value</em> (csv) format. The input data must consist of date-time, pollutant, and meteorological data. The analysis is done by selecting six functions: <em>theilSen</em> for trend analysis, <em>timeVariation</em> for temporal variations, <em>scatterPlot</em> for linear correlation analysis,<em> timePlot</em> for fluctuation analysis, <em>windRose</em> for wind rose creation, and <em>polarPlot</em> for creating pollution rose. Results from these functions are discussed. <strong>Conclusion, significance and impact study: </strong><em>Openair</em> model is capable of analyzing a long time air quality data. Application of <em>openair</em> model is possible to cities in Indonesia that already monitor ambient air quality but have not analyzed the data yet

scholarly journals Handling Complex Missing Data Using Random Forest Approach for an Air Quality Monitoring Dataset: A Case Study of Kuwait Environmental Data (2012 to 2018)

2021 ◽  
Vol 18 (3) ◽  
pp. 1333
Author(s):  
Ahmad R. Alsaber ◽  
Jiazhu Pan ◽  
Adeeba Al-Hurban 
Keyword(s):  
Air Quality ◽  
Missing Data ◽  
Random Forest ◽  
Missing Values ◽  
Imputation Method ◽  
Environmental Data ◽  
Environmental Research ◽  
Quality Data ◽  
Data Set ◽  
Air Quality Data

In environmental research, missing data are often a challenge for statistical modeling. This paper addressed some advanced techniques to deal with missing values in a data set measuring air quality using a multiple imputation (MI) approach. MCAR, MAR, and NMAR missing data techniques are applied to the data set. Five missing data levels are considered: 5%, 10%, 20%, 30%, and 40%. The imputation method used in this paper is an iterative imputation method, missForest, which is related to the random forest approach. Air quality data sets were gathered from five monitoring stations in Kuwait, aggregated to a daily basis. Logarithm transformation was carried out for all pollutant data, in order to normalize their distributions and to minimize skewness. We found high levels of missing values for NO2 (18.4%), CO (18.5%), PM10 (57.4%), SO2 (19.0%), and O3 (18.2%) data. Climatological data (i.e., air temperature, relative humidity, wind direction, and wind speed) were used as control variables for better estimation. The results show that the MAR technique had the lowest RMSE and MAE. We conclude that MI using the missForest approach has a high level of accuracy in estimating missing values. MissForest had the lowest imputation error (RMSE and MAE) among the other imputation methods and, thus, can be considered to be appropriate for analyzing air quality data.

Big data and portfolio optimization: A novel approach integrating DEA with multiple data sources

Omega ◽  
2021 ◽  
pp. 102479
Author(s):  
Zhongbao Zhou ◽  
Meng Gao ◽  
Helu Xiao ◽  
Rui Wang ◽  
Wenbin Liu
Keyword(s):  
Big Data ◽  
Portfolio Optimization ◽  
Data Sources ◽  
Multiple Data Sources ◽  
Multiple Data ◽  
Novel Approach

A spatio-temporally weighted hybrid model to improve estimates of personal PM2.5 exposure: Incorporating big data from multiple data sources

2019 ◽  
Vol 253 ◽  
pp. 403-411 ◽  
Author(s):  
YuJie Ben ◽  
FuJun Ma ◽  
Hao Wang ◽  
Muhammad Azher Hassan ◽  
Romanenko Yevheniia ◽  
...  
Keyword(s):  
Big Data ◽  
Hybrid Model ◽  
Data Sources ◽  
Multiple Data Sources ◽  
Multiple Data ◽  
Pm2.5 Exposure

scholarly journals Tocilizumab in rheumatoid arthritis: A case study of safety evaluations of a large postmarketing data set from multiple data sources

2015 ◽  
Vol 44 (4) ◽  
pp. 381-388 ◽  
Author(s):  
Jeffrey R. Curtis ◽  
Susana Perez-Gutthann ◽  
Samy Suissa ◽  
Pavel Napalkov ◽  
Natasha Singh ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Data Sources ◽  
Data Set ◽  
Multiple Data Sources ◽  
Multiple Data

scholarly journals Changes in air-pollution related behaviour measured by Google Trends Search Volume Index in response to reported air quality in Poland

2021 ◽  
Author(s):  
Wojciech Nazar ◽  
Katarzyna Plata-Nazar
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Winter Season ◽  
Google Trends ◽  
Volume Index ◽  
Quality Data ◽  
Coefficient Of Determination ◽  
Pollution Levels ◽  
Search Volume ◽  
Air Quality Data

Abstract Background Decreased air quality is connected to a higher number of hospital admissions and an increase in daily mortality rates. Thus, Poles’ behavioural response to sometimes elevated air pollution levels is vital. The aim of this study was to carry out analysis of changes in air-pollution related information seeking behaviour in response to nationwide reported air quality in Poland. Methods Google Trends Search Volume Index data was used to investigate Poles’ interest in air pollution-related keywords. PM10 and PM2.5 concentrations measured across Poland between 2016 and 2019 were collected from the Chief Inspectorate of Environmental Protection databases. Pearson Product-Moment Correlation and the R2 correlation coefficient of determination were used to measure spatial and seasonal correlations between reported air pollution levels and the popularity of search queries. Results The highest PM10 and PM2.5 concentrations were observed in southern voivodeships and during the winter season. Similar trends were observed for Poles’ interest in air-pollution related keywords. All R2 coefficient of determination values were > 0.5 and all correlations were statistically significant. Conclusion Poland’s air quality does not meet the World Health Organisation guidelines. Also, the air quality is lower in southern Poland and during the winter season. It appears that Poles are aware of this issue and search for daily air quality data in their location. Greater interest in air quality data in Poland strongly correlates with both higher regional and higher seasonal air pollution levels.

scholarly journals Developing Web-Based 3D Health-Aware Routing for Pedestrians and Cyclists

2020 ◽  
Vol 171 ◽  
pp. 02009
Author(s):  
Rosanny Sihombing ◽  
Sabo Kwada Sini ◽  
Matthias Fitzky
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Health Concern ◽  
Quality Data ◽  
Quality Level ◽  
3D Navigation ◽  
Index Numbers ◽  
Web Based ◽  
Air Quality Data ◽  
Monitoring Stations

As the population of people migrating to cities keeps increasing, concerns have been raised about air quality in cities and how it impacts everyday life. Thus, it is important to demonstrate ways of avoiding polluted areas. The approach described in this paper is intended to draw attention to polluted areas and help pedestrians and cyclists to achieve the lowest possible level of air pollution when planning daily routes. We utilise real-time air quality data which is obtained from monitoring stations across the world. The data consist of the geolocation of monitoring stations as well as index numbers to scale the air quality level in every corresponding monitoring stations. When the air quality level is considered having a moderate health concern for people with respiratory disease, such as asthma, an alternative route that avoid air pollution will be calculated so that pedestrians and cyclists can be informed. The implementation can visualize air quality level in several areas in 3D map as well as informs health-aware route for pedestrian and cyclist. It automatically adjusts the observed air quality areas based on the availability of monitoring stations. The proposed approach results in a prototype of a health-aware 3D navigation system for pedestrian and cyclist.

scholarly journals Three Hierarchical Levels of Big-Data Market Model Over Multiple Data Sources for Internet of Things

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 31269-31280 ◽  
Author(s):  
Busik Jang ◽  
Sangdon Park ◽  
Joohyung Lee ◽  
Sang-Geun Hahn
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Data Sources ◽  
Market Model ◽  
Multiple Data Sources ◽  
Multiple Data ◽  
Hierarchical Levels ◽  
Data Market
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