Observation of PM2.5 using a combination of satellite remote sensing and low-cost sensor network in Siberian urban areas with limited reference monitoring

Lin, Changqing; Labzovskii, Lev; Mak, Hugo Wai Leung; Fung, Jimmy Chi Hung; Lau, Alexis K.H.; Kenea, Samuel Takele; Bilal, Muhhamad; Hey, Joshua Vande; Lu, Xingcheng; Ma, Jun

doi:10.1016/j.atmosenv.2020.117410

Cited by 47 publications

(28 citation statements)

References 53 publications

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“…The results obtained using the linear regression model showed a higher correlation with ground-based observations when compared with the values obtained using the model described in [7]. However, in both cases, a sufficiently high correlation with ground-based data was obtained.…”

Section: Fig 4 Comparison Of Pm Received From Ksc Posts and Calculamentioning

confidence: 55%

“…Its deterioration is facilitated by the fact that the city is the largest transport hub in Eastern Siberia; the presence of a number of large enterprises in the metallurgical, engineering and chemical industries aggravates the situation. It is shown in [7] that the concentration of PM2.5 in Krasnoyarsk is 64% higher than the average for Russia.…”

Section: Data and Measurement 21 Study Areamentioning

confidence: 88%

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Comparison of different models for assessing air quality in Krasnoyarsk using satellite data

Krasnoshchekov

Yakubailik

2020

E3S Web Conf.

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Methods for estimating the atmospheric pollution of Krasnoyarsk by particulate matter based on satellite data on the aerosol optical depth (AOD) are considered. Satellite data from the MODIS MAIAC algorithm with a spatial resolution of 1 km are used together with data from the ground-based PM2.5 environmental monitoring stations of the FRC KSC SB RAS research network. A comparative analysis of the relationship between the calculated values of PM2.5 obtained from AOD data and ground- based measurements of PM2.5 in the summer of 2019 is presented. Various models of the relationship between these parameters were investigated, and a high level of correlation of these values was obtained. The calculated coefficient of determination was about 0.7.

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Section: Fig 4 Comparison Of Pm Received From Ksc Posts and Calculamentioning

confidence: 55%

Section: Data and Measurement 21 Study Areamentioning

confidence: 88%

Comparison of different models for assessing air quality in Krasnoyarsk using satellite data

Krasnoshchekov

Yakubailik

2020

E3S Web Conf.

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“…2. This presented graph also shows the days on which the mode of unfavourable meteorological conditions (UMC) was announced, this was 7 times during the period under consideration, and the standard for the average daily maximum permissible concentration PM2.5 = 0.035 mg/m 3 .…”

Section: Resultsmentioning

confidence: 99%

“…For such study of the air environment over the territory of one million city, nine AOP equipped with dust meters are clearly not enough. This problem can be solved by installing relatively cheap air monitoring stations that measure PM concentration and some meteorological parameters [3].…”

Section: Introductionmentioning

confidence: 99%

Air monitoring system of the Krasnoyarsk Science Center SB RAS

2020

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The paper deals with the problem of monitoring the surface layer of the atmosphere of the Siberian city of Krasnoyarsk with a population of one million people in order to study the regularities of the formation of a space-time field of particulate matter concentrations in the air. It is shown that such research can be performed only with the help of a system of scientific monitoring of atmospheric air. The Krasnoyarsk Scientific Center SB RAS air monitoring system is a set of special equipment and software designed to collect and analyze objective data on the state of atmospheric air in Krasnoyarsk. The main task of creating and operating the monitoring system is to form an information and analytical basis for research and development aimed at solving the problems of improving the environmental situation in Krasnoyarsk. The article presents the results of almost two years of experience in the operation of the monitoring system and the main results.

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“…In this study, we removed two kinds of noisy data, including redundant features and high correlational features. Redundant features describe useless information for mining the influential features on the victim fatalities, so they are excluded from the experiment [40]. For example, features such as "POING_X", "POINT_Y", and "LAPDDIV" describe the spatial coordinates and the jurisdictional information, which are not the causes of traffic fatalities, and therefore, they are deleted.…”

Section: ) Data Cleaningmentioning

confidence: 99%

A Spatial Analysis Methodology Based on Lazy Ensembled Adaptive Associative Classifier and GIS For Examining the Influential Factors on Traffic Fatalities

Zhai¹,

Li²,

Jiang

et al. 2020

IEEE Access

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Analyzing the influential factors of traffic accidents has been a hot topic in city management. Most existing literature in this domain implemented linear based sensitivity analysis in statistics to study the problems. However, the linear assumption limits their model performance and therefore interferes with the detection of influential factors. Recent studies started to use nonlinear machine learning methods to explore the problem. One of the most popular ways is the association rule analysis. Based on the Support and Confidence value, researchers were able to identify the top influential factors. However, (1) the identification of the thresholds for Support and Confidence has not been well solved in related studies. This study, therefore, proposes Lazy ensembled adaptive Associative Classifier to tackle this problem. Besides, (2) most of the existing literature only analyzed the general relationships between the influential factors and the traffic fatality but did not further investigate their spatial connections. Those studies could not answer specific questions like "which region should be focused more on alcohol control?", or "where requires more attention on motorcycle control?". This study combines the road-based GIS analysis and the results from association rule analysis to spatially analyze the relationships between the impact factors and the traffic fatalities. Specific suggestions on city management and traffic control were proposed thereafter.

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Observation of PM2.5 using a combination of satellite remote sensing and low-cost sensor network in Siberian urban areas with limited reference monitoring

Cited by 47 publications

References 53 publications

Comparison of different models for assessing air quality in Krasnoyarsk using satellite data

Comparison of different models for assessing air quality in Krasnoyarsk using satellite data

Air monitoring system of the Krasnoyarsk Science Center SB RAS

A Spatial Analysis Methodology Based on Lazy Ensembled Adaptive Associative Classifier and GIS For Examining the Influential Factors on Traffic Fatalities

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