Study and Implementation of PM2.5 Data Download Service Based on Python

2013 ◽  
Vol 411-414 ◽  
pp. 555-558
Author(s):  
Kun Cao ◽  
Fei Wang ◽  
Jin Gang Liu
Keyword(s):  
Air Quality ◽  
Data Storage ◽  
Pollution Control ◽  
Historical Data ◽  
Pollution Prevention ◽  
Implementation Process ◽  
Present Situation ◽  
Quality Data ◽  
Air Pollution Control ◽  
Air Quality Data

Aiming at the present situation of research institutions attaching great importance to air pollution control but air quality data acquisition is not convenient, solutions based on Python and Django to provide the service of the air quality data downloading such as PM2.5 is proposed along with their detailed implementation process in Web system design and development. The results indicate that, these techniques enhanced the developing efficiency, the system's service of data downloading can provide comprehensive monitoring data which can be used directly by researchers and improve their efficiency. Accumulated historical data storage is also very important to the study of air quality changing and the pollution prevention.

Utilizing Blockchain and Smart Contracts to Enable Audit 4.0: From the Perspective of Accountability Audit of Air Pollution Control in China

2019 ◽  
Vol 16 (2) ◽  
pp. 23-41 ◽  
Author(s):  
Jun Dai ◽  
Na He ◽  
Haizong Yu
Keyword(s):  
Air Pollution ◽  
Pollution Control ◽  
Quality Data ◽  
Smart Sensors ◽  
Chinese Government ◽  
Smart Contracts ◽  
Air Pollution Control ◽  
Government Officials ◽  
New Generation ◽  
Air Quality Data

ABSTRACT Industry 4.0 uses many technologies, such as smart sensors and IoT, to fundamentally improve manufacturing processes. These advanced tools can also be utilized by auditors for the purpose of achieving real-time auditing and monitoring, pushing the profession toward a new generation: “Audit 4.0.” Blockchains and smart contracts should be utilized to overcome new challenges in the transformation toward Audit 4.0. This paper explores the potential of blockchain and smart contracts to reengineer current audit procedures, thereby enabling Audit 4.0. First, this paper demonstrates a framework that summarizes where blockchain and smart contracts should be applied to help implement Audit 4.0. Then, it designs and implements a system to facilitate accountability audit for Chinese government officials regarding air pollution control. In this case, real air quality data are collected via crowdsourcing, verified and analyzed by blockchain and smart contracts to achieve a continuous audit of government officials' performance on air protection.

Determining the effectiveness of pollution control policies implemented by the Chinese government: Distribution fitting and testing of daily PM2.5 data

2021 ◽  
pp. 0958305X2110435
Author(s):  
Gang Peng ◽  
Jie Zhang ◽  
Kai Shi
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Pollution Control ◽  
Pollution Prevention ◽  
Sustainable Urban Development ◽  
Chinese Government ◽  
Air Pollution Control ◽  
Distribution Fitting ◽  
Control Policies ◽  
And Control

Air pollution has become an urgent issue affecting sustainable urban development. The Chinese government has implemented a series of air pollution control policies since 2012. Exploring the effectiveness of pollution control policies is important for future policy-making and improvements in air quality. Mean and variance tests were used for evaluation on the effectiveness of pollution control policies implemented in major cities and estimates of the heterogeneity among cities based on the distribution fitting and testing of daily PM2.5 data from January 2015 to January 2020. The nonparametric kernel density estimation adopted in this paper can effectively describe the data characteristics, and this is very important for air quality monitoring and control. Our findings demonstrate that air pollution prevention and control policies have significantly improved the levels and distribution of urban air quality in China.

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
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