Estimating  The Spatial Distribution  Of Soil Heavy Metals In Oil Mining Area Using Air Quality Data Based on Hybrid Geostatistical Method

2021 ◽  
Author(s):  
Song yingqiang ◽  
Kang Lu ◽  
Lin Fan ◽  
Aizezi Aziguli ◽  
Yang Zhongkang ◽  
...  
Keyword(s):  
Heavy Metals ◽  
Spatial Distribution ◽  
Air Quality ◽  
Mining Area ◽  
Quality Data ◽  
Soil Heavy Metals ◽  
Geostatistical Method ◽  
Air Quality Data

scholarly journals Making Air Quality Data Meaningful

2020 ◽  
Author(s):  
Szu-Yu (Cyn) Liu ◽  
Justin Cranshaw ◽  
Asta Roseway
Keyword(s):  
Air Quality ◽  
Quality Data ◽  
Air Quality Data

Feature Selection and Analysis in Air Quality Data

2021 ◽  
Author(s):  
Manish Mahajan ◽  
Santosh Kumar ◽  
Bhasker Pant ◽  
Umesh Kumar Tiwari ◽  
Rijwan Khan
Keyword(s):  
Feature Selection ◽  
Air Quality ◽  
Quality Data ◽  
Air Quality Data

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.

aiRe - A web-based R application for simple, accessible and repeatable analysis of urban air quality data

2021 ◽  
Vol 138 ◽  
pp. 104976
Author(s):  
Juan José Díaz ◽  
Ivan Mura ◽  
Juan Felipe Franco ◽  
Raha Akhavan-Tabatabaei
Keyword(s):  
Air Quality ◽  
Urban Air Quality ◽  
Quality Data ◽  
Urban Air ◽  
Web Based ◽  
Air Quality Data

A recursive estimation approach to the spatio-temporal analysis and modelling of air quality data

2006 ◽  
Vol 21 (6) ◽  
pp. 759-769 ◽  
Author(s):  
R. Romanowicz ◽  
P. Young ◽  
P. Brown ◽  
P. Diggle
Keyword(s):  
Air Quality ◽  
Temporal Analysis ◽  
Recursive Estimation ◽  
Quality Data ◽  
Spatio Temporal ◽  
Air Quality Data

Linear Regression Based Air Quality Data Analysis and Prediction using Python

2021 ◽  
Author(s):  
S B Sonu ◽  
A. Suyampulingam
Keyword(s):  
Data Analysis ◽  
Air Quality ◽  
Linear Regression ◽  
Quality Data ◽  
Air Quality Data

An Air Quality Data Analysis System for Interrelating Effects, Standards, and Needed Source Reductions: Part 10. Potential Ambient O3Standards to Limit Soybean Crop Reduction

JAPCA ◽  
1988 ◽  
Vol 38 (12) ◽  
pp. 1497-1503 ◽  
Author(s):  
Ralph I. Larsen ◽  
Thomas R. McCurdy ◽  
Pamela M. Johnson ◽  
Walter W. Heck
Keyword(s):  
Data Analysis ◽  
Air Quality ◽  
Quality Data ◽  
Soybean Crop ◽  
Analysis System ◽  
Data Analysis System ◽  
Air Quality Data
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