scholarly journals Modeling Air Pollution Transmission Behavior as Complex Network and Mining Key Monitoring Station

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 121245-121254
Author(s):  
Chen Song ◽  
Guoyan Huang ◽  
Bing Zhang ◽  
Bo Yin ◽  
Huifang Lu
Keyword(s):  
Air Pollution ◽  
Complex Network ◽  
Monitoring Station ◽  
Pollution Transmission

scholarly journals Spatial Association Pattern of Air Pollution and Influencing Factors in the Beijing–Tianjin–Hebei Air Pollution Transmission Channel: A Case Study in Henan Province

2020 ◽  
Vol 17 (5) ◽  
pp. 1598
Author(s):  
Jianhui Qin ◽  
Suxian Wang ◽  
Linghui Guo ◽  
Jun Xu
Keyword(s):  
Air Pollution ◽  
Relative Humidity ◽  
Influencing Factors ◽  
Pollution Control ◽  
Sunshine Duration ◽  
Spatial Association ◽  
Henan Province ◽  
Air Pollution Control ◽  
Transmission Channel ◽  
Pollution Transmission

The Beijing–Tianjin–Hebei (BTH) air pollution transmission channel and its surrounding areas are of importance to air pollution control in China. Based on daily data of air quality index (AQI) and air pollutants (PM2.5, PM10, SO2, NO2, CO, and O3) from 2015 to 2016, this study analyzed the spatial and temporal characteristics of air pollution and influencing factors in Henan Province, a key region of the BTH air pollution transmission channel. The result showed that non-attainment days and NAQI were slightly improved at the provincial scale during the study period, whereas that in Hebi, Puyang, and Anyang became worse. PM2.5 was the largest contributor to the air pollution in all cities based on the number of non-attainment days, but its mean frequency decreased by 21.62%, with the mean occurrence of O3 doubled. The spatial distribution of NAQI presented a spatial agglomeration pattern, with high-high agglomeration area varying from Jiaozuo, Xinxiang, and Zhengzhou to Anyang and Hebi. In addition, the NAQI was negatively correlated with sunshine duration, temperature, relative humidity, wind speed, and positively to atmospheric pressure and relative humidity in all four clusters, whereas relationships between socioeconomic factors and NAQI differed among them. These findings highlight the need to establish and adjust regional joint prevention and control of air pollution as well as suggest that it is crucially important for implementing effective strategies for O3 pollution control.

scholarly journals Application of Internet of Things (IoT) on air pollution monitoring database system

2019 ◽  
Vol 125 ◽  
pp. 25005
Author(s):  
Sudarsono ◽  
Muhammad Andang Novianta ◽  
Cyrilla Indri Parwati
Keyword(s):  
Air Pollution ◽  
Internet Of Things ◽  
Structural Information ◽  
Line Graph ◽  
Pollution Index ◽  
Database System ◽  
Pollution Monitoring ◽  
Monitoring Station ◽  
Pollution Level ◽  
Air Pollution Monitoring

In the present work, a database system of air pollution monitoring is developed using Internet of Things (IoT) technology. The system aims to give structural information and trace of air pollution level at particular monitoring station. The particular monitoring location (node) is connected to IoT/M2M server via GSM network using GPRS feature and display on IoT/M2M application in web form. The database on IoT/M2M contains name, description, and location of the monitoring station, Pollution index and the time when the data are taken. On IoT/M2M, the data are displayed either in a color bar graph or a line graph. The color indicated the index value of the pollution. The data can be accessed via internet on isfuonline.info. The system is tested at laboratory environment to detect CO, SO2, NO2, O3, and PM. The test result shows that the system is worked well. Time required to transfer the monitoring data to the IoT server is about 15 minutes. Meanwhile, response time of the system is 30 minutes.

Modelling spatio-temporal air pollution data from a mobile monitoring station

2016 ◽  
Vol 86 (13) ◽  
pp. 2546-2559 ◽  
Author(s):  
Simone Del Sarto ◽  
Maria Giovanna Ranalli ◽  
David Cappelletti ◽  
Beatrice Moroni ◽  
Stefano Crocchianti ◽  
...  
Keyword(s):  
Air Pollution ◽  
Monitoring Station ◽  
Mobile Monitoring ◽  
Spatio Temporal

scholarly journals Analysis of a Pollution Transmission Process in Hefei City Based on Mobile Lidar

2020 ◽  
Vol 237 ◽  
pp. 02006 ◽  
Author(s):  
Shuai Zhang ◽  
Zhaoming Zhou ◽  
Conglei Ye ◽  
Jibing Shi ◽  
Peng Wang ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Lidar System ◽  
Transmission Process ◽  
A New Technique ◽  
Air Quality Monitoring Stations ◽  
Pollution Transmission ◽  
Monitoring Stations

The air pollution has been regional in China with the development of economy. To monitoring the air pollution transmission, a new technique, mobile lidar system (GBQ-S01), was introduced. In this paper, a pollution transmission process happened on October 26th, 2017, was analyzed with the use of mobile lidar, air quality monitoring stations data, and Hysplit backward trajectories. The results showed that the polluted air mass was transferred from northeast under the force of air pressure. Under the influences of air pollution transmission and bad meteorological diffusion conditions, The PM10 quality concentrations in Hefei increased a lot within 5 hours; among all the 10 national air quality monitoring stations, the Luyang District (the northernmost one) and Changjiang Middle Road (the easternmost one) received the most serious impact with PM10 concentration reached up to 252 μg/m3 and 219 μg/m3 at 22:00 (Beijing Time).

Spatial variability of air pollution in the vicinity of a permanent monitoring station in central Paris

2005 ◽  
Vol 39 (15) ◽  
pp. 2725-2736 ◽  
Author(s):  
S VARDOULAKIS ◽  
N GONZALEZFLESCA ◽  
B FISHER ◽  
K PERICLEOUS
Keyword(s):  
Air Pollution ◽  
Spatial Variability ◽  
Monitoring Station

scholarly journals EVALUASI TINGKAT POLUTAN DARI KEGIATAN PENGELOLAAN SAMPAH DI RUMAH KOMPOS SRIKANA DAN KEPUTRAN KOTA SURABAYA

2013 ◽  
Vol 11 (2) ◽  
Author(s):  
Irma Nur Asifa ◽  
Imam Thohari ◽  
Waluyo Jati
Keyword(s):  
Air Pollution ◽  
Descriptive Study ◽  
Quality Standard ◽  
Residential Area ◽  
Organic Wastes ◽  
Pollution Monitoring ◽  
Monitoring Station ◽  
Air Pollution Monitoring ◽  
Pollutant Level

Garbage materials are able to contaminate the environment in three ways:physically, chemically, and biologically. The large composition of garbage require managementand processing to reduce garbage load entering the TPA. In relation to this, Dinas Kebersihandan Pertamanan Kota 5urabaya has initiated composting of organic wastes partly to reducetheir volume. This study was aimed at measuring pollutants in compost materials especially interms of CO, 502, and H25 parameters. This activity was carried out in Composting Houses of5rikana and Keputran 5urabaya.This descriptive study was performed by measuring CO, SOb and H25 andlaboratory examination. The population under study was composting houses (18 sites) and thesample size was 2 composting houses.Results showed that pollutant level of CO, 502 and H25 in the air was not exceedingthe quality standard being implemented in East Java. The CO level in 5rikana CompostingHouse was 4.21 «(JgjNm3) and in residential area was 3.37 «(JgjNm3). 502 level in compostinghouse was 12.04 «(Jg/Nm3), while outside of house of 16.71 «(JgjNm3) and in residential areawas 4.16 «(Jg/Nm3). The H25 level in composting house was 9.13 «(Jg/Nm3), outside the housewas 6.54«(JgjNm3) and in residential area was 3.22 «(JgjNm3). The CO level in KeputranComposting House was 9.66 «(Jg/Nm3), in outside of the house was 15,74 (uq/Nrn") and in thesurrounding market was 4.18 «(Jg/Nm3). The 502 level in composting house was 6,88«(Jg/Nm3), outside of compost house 19.38«(Jg/Nm3), and in the surrounding market was 2,76«(Jg/Nm3). H2S level in composting house was 16,14 «(Jg/Nm3), in outside of the house was4,49 «(JgjNm3) and in the surrounding market was 1.97 «(Jg/Nm3). Temperature, humidity, andwind velocity lase have some influence as well but not too significant to increase pollutantlevel.It is suggested to Dinas Kebersihan dan Pertamanan Kota Surabaya to addcomposting house in its list of air pollution monitoring station.

scholarly journals Industrial agglomeration and air pollution: A new perspective from enterprises in Atmospheric Pollution Transmission Channel Cities (APTCC) of Beijing-Tianjin-Hebei and its surrounding areas, China

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0255036
Author(s):  
Cuicui Xiao ◽  
Jingbo Zhou ◽  
Xin Wang ◽  
Shumin Zhang
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Atmospheric Pollution ◽  
Pollutant Emissions ◽  
Industrial Agglomeration ◽  
Transmission Channel ◽  
Pollution Impacts ◽  
New Perspective ◽  
Surrounding Areas ◽  
Pollution Transmission

Air quality in China has gradually been improving in recent years; however, the Beijing-Tianjin-Hebei (BTH) region continues to be the most polluted area in China, with the worst air quality index. BTH and its surrounding areas experience high agglomeration of heavy-polluting manufacturers that generate electric power, process petroleum and coal, and carry out smelting and pressing of ferrous metals, raw chemical materials, chemical products, and non-metallic mineral products. This study presents evidence of the air pollution impacts of industrial agglomeration using the Ellison–Glaeser index, Herfindahl–Hirschman index, and spatial autocorrelation analysis. This was based on data from 73,353 enterprises in “2+26” atmospheric pollution transmission channel cities in BTH and its surrounding areas (herein referred to as BTH “2+26” cities). The results showed that Beijing, Yangquan, Puyang, Kaifeng, Taiyuan, and Jinan had the highest Ellison–Glaeser index among the BTH “2+26” cities; this represents the highest enterprise agglomeration. Beijing, Langfang, Tianjin, Baoding, and Tangshan also showed a low Herfindahl–Hirschman index of pollutant emissions, which have a relatively high degree of industrial agglomeration in BTH “2+26” cities. There was an inverted U-shaped relationship between enterprise agglomeration and air quality in the BTH “2+26” cities. This means that air quality improved with increased industrial agglomeration up to a certain level; beyond this point, the air quality begins to deteriorate with a decrease in industrial agglomeration.

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