Surveillance efficiency evaluation of air quality monitoring networks for air pollution episodes in industrial parks: Pollution detection and source identification

Dense air quality monitoring network (AQMN) is one of main ways to surveil industrial air pollution. This paper is concerned with the design of a dense AQMN for H2S for a chemical industrial park in Shanghai, China. An indicator (Surveillance Efficiency, SE) for the long-term performance of AQMN was constructed by averaging pollution detection efficiency (rd) and source identification efficiency (rb). A ranking method was developed by combing Gaussian puff model and Source area analysis for improving calculation efficiency. Candidate combinations with highest score were given priority in the selection of next site. Two existing monitors were suggested to relocate to the west and southwest of this park. SE of optimized AQMN increased quickly with monitor number, and then the growth trend started to flatten when the number reached about 60. The highest SE occurred when the number reached 110. Optimal schemes of AQMNs were suggested which can achieve about 98% of the highest SE, while using only about 60 monitors. Finally, the reason why the highest SE is less than 1 and the variation characteristics of rd and rb were discussed. Overall, the proposed method is an effective tool for designing AQMN with optimal SE in industrial parks.

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A Cluster Analysis of PM2.5 Using CMAQ Model Results for Representativeness of Air Quality Monitoring Networks in Busan, Korea

10.5194/egusphere-egu2020-3189 ◽

2020 ◽

Author(s):

Woo-Sik Jung ◽

Woo-Gon Do

Keyword(s):

Air Pollution ◽

Cluster Analysis ◽

Air Quality ◽

Monitoring Network ◽

Hierarchical Cluster ◽

Quality Monitoring ◽

Quality Data ◽

Air Quality Monitoring ◽

Monitoring Networks ◽

Monitoring Stations

With increasing interest in air pollution, the installation of air quality monitoring networks for regular measurement is considered a very important task in many countries. However, operation of air quality monitoring networks requires much time and money. Therefore, the representativeness of the locations of air quality monitoring networks is an important issue that has been studied by many groups worldwide. Most such studies are based on statistical analysis or the use of geographic information systems (GIS) in existing air quality monitoring network data. These methods are useful for identifying the representativeness of existing measuring networks, but they cannot verify the need to add new monitoring stations. With the development of computer technology, numerical air quality models such as CMAQ have become increasingly important in analyzing and diagnosing air pollution. In this study, PM2.5 distributions in Busan were reproduced with 1-km grid spacing by the CMAQ model. The model results reflected actual PM2.5 changes relatively well. A cluster analysis, which is a statistical method that groups similar objects together, was then applied to the hourly PM2.5 concentration for all grids in the model domain. Similarities and differences between objects can be measured in several ways. K-means clustering uses a non-hierarchical cluster analysis method featuring an advantageously low calculation time for the fast processing of large amounts of data. K-means clustering was highly prevalent in existing studies that grouped air quality data according to the same characteristics. As a result of the cluster analysis, PM2.5 pollution in Busan was successfully divided into groups with the same concentration change characteristics. Finally, the redundancy of the monitoring stations and the need for additional sites were analyzed by comparing the clusters of PM2.5 with the locations of the air quality monitoring networks currently in operation.This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education(2017R1D1A3B03036152).

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Source area identification with observation from limited monitor sites for air pollution episodes in industrial parks

Atmospheric Environment ◽

10.1016/j.atmosenv.2015.08.048 ◽

2015 ◽

Vol 122 ◽

pp. 1-9 ◽

Cited By ~ 10

Author(s):

Zihan Huang ◽

Yuan Wang ◽

Qi Yu ◽

Weichun Ma ◽

Yan Zhang ◽

...

Keyword(s):

Air Pollution ◽

Source Area ◽

Industrial Parks ◽

Air Pollution Episodes ◽

Pollution Episodes

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The design of air quality monitoring networks — Air pollution problems

The Science of The Total Environment ◽

10.1016/0048-9697(84)90071-8 ◽

1984 ◽

Vol 34 (3) ◽

pp. 299

Author(s):

Michel Benarie

Keyword(s):

Air Pollution ◽

Air Quality ◽

Quality Monitoring ◽

Air Quality Monitoring ◽

Monitoring Networks

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Novel Air Pollution Measurement System Based on Ethereum Blockchain

Journal of Sensor and Actuator Networks ◽

10.3390/jsan9040049 ◽

2020 ◽

Vol 9 (4) ◽

pp. 49

Author(s):

Daniele Sofia ◽

Nicoletta Lotrecchiano ◽

Paolo Trucillo ◽

Aristide Giuliano ◽

Luigi Terrone

Keyword(s):

Air Pollution ◽

Particulate Matter ◽

Air Quality ◽

Query Language ◽

Environmental Data ◽

Quality Monitoring ◽

Traffic Information ◽

Quality Data ◽

Air Quality Monitoring ◽

Monitoring Networks

The need to protect sensitive data is growing, and environmental data are now considered sensitive. The application of last-generation procedures such as blockchains coupled with the implementation of new air quality monitoring technology allows the data protection and validation. In this work, the use of a blockchain applied to air pollution data is proposed. A blockchain procedure has been designed and tested. An Internet of Things (IoT)-based sensor network provides air quality data in terms of particulate matter of two different diameters, particulate matter (PM)10 and PM2.5, volatile organic compounds (VOC), and nitrogen dioxide (NO2) concentrations. The dataset also includes meteorological parameters and vehicular traffic information. This work foresees that the data, recovered from traditional Not Structured Query Language (NoSQL) database, and organized according to some specifications, are sent to the Ethereum blockchain daily automatically and with the possibility to choose the period of interest manually. There was also the development of a transaction management and recovery system aimed at retrieving data, formatting it according to the specifications and organizing it into files of various formats. The blockchain procedure has therefore been used to track data provided by air quality monitoring networks unequivocally.

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Chemical Components, Variation, and Source Identification of PM1 during the Heavy Air Pollution Episodes in Beijing in December 2016

Journal of Meteorological Research ◽

10.1007/s13351-018-7051-8 ◽

2018 ◽

Vol 32 (1) ◽

pp. 1-13 ◽

Cited By ~ 15

Author(s):

Yangmei Zhang ◽

Yaqiang Wang ◽

Xiaoye Zhang ◽

Xiaojing Shen ◽

Junying Sun ◽

...

Keyword(s):

Air Pollution ◽

Source Identification ◽

Chemical Components ◽

Air Pollution Episodes ◽

Pollution Episodes

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Next-Generation Community Air Quality Sensors for Identifying Air Pollution Episodes

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph16183268 ◽

2019 ◽

Vol 16 (18) ◽

pp. 3268 ◽

Cited By ~ 6

Author(s):

Seto ◽

Carvlin ◽

Austin ◽

Shirai ◽

Bejarano ◽

...

Keyword(s):

Air Pollution ◽

Air Quality ◽

Air Monitoring ◽

Large Network ◽

Monitoring Networks ◽

Study Region ◽

Spatial Coverage ◽

Community Monitoring ◽

Air Pollution Episodes ◽

Pollution Episodes

Conventional regulatory air quality monitoring sites tend to be sparsely located. The availability of lower-cost air pollution sensors, however, allows for their use in spatially dense community monitoring networks, which can be operated by various stakeholders, including concerned residents, organizations, academics, or government agencies. Networks of many community monitors have the potential to fill the spatial gaps between existing government-operated monitoring sites. One potential benefit of finer scale monitoring might be the ability to discern elevated air pollution episodes in locations that have not been identified by government-operated monitoring sites, which might improve public health warnings for populations sensitive to high levels of air pollution. In the Imperial Air study, a large network of low-cost particle monitors was deployed in the Imperial Valley in Southeastern California. Data from the new monitors is validated against regulatory air monitoring. Neighborhood-level air pollution episodes, which are defined as periods in which the PM2.5 (airborne particles with sizes less than 2.5 μm in diameter) hourly average concentration is equal to or greater than 35 μg m−3, are identified and corroborate with other sites in the network and against the small number of government monitors in the region. During the period from October 2016 to February 2017, a total of 116 episodes were identified among six government monitors in the study region; however, more than 10 times as many episodes are identified among the 38 community air monitors. Of the 1426 episodes identified by the community sensors, 723 (51%) were not observed by the government monitors. These findings suggest that the dense network of community air monitors could be useful for addressing current limitations in the spatial coverage of government air monitoring to provide real-time warnings of high pollution episodes to vulnerable populations.

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