Variation of industrial air pollution emissions based on VIIRS thermal anomaly data

Sun, Shuang; Wu, Zhihong; Gautam, Atul; Li, Jinxiang; Zhao, Wenji

doi:10.1016/j.atmosres.2020.105021

Cited by 19 publications

(7 citation statements)

References 34 publications

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“…1), it can be seen that all three SF seasons show a trend of gradual decrease before the festival and gradual increase after the festival. The highest weekly value in 2019 was 6.22×10 15 molecules/cm 2 in the 5th week before the SF; the highest value in 2020 was 5.30×10 15 molecules/cm 2 in the 7th week before the SF; the highest weekly value in 2021 was 5.87×10 15 molecules/cm 2 in the 7th week before the SF. The lowest NO2 column concentrations in the three years of the SF season occurred in the rst week after the SF in 2019.…”

Section: Characteristics Of Tropno2 Column Concentrations In China During the Sf Season In The Last Three Yearsmentioning

confidence: 91%

“…The number of industrial thermal anomalies can indicate the spatial distribution of industrial production as well as the various characteristics, and the magnitude of radiation intensity can characterize the scale of industrial production and energy consumption, which indirectly re ects the air quality condition (Sun et al, 2020;Sun et al, 2019). Figure 6 shows the distribution of industrial thermal anomalies and the change of kernel density of radiation intensity in the BTH in the SF season in 2020.…”

Section: Temporal Variation Of Industrial Thermal Anomalies Under the In Uence Of Epidemicsmentioning

confidence: 99%

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The Short-Term Impact of the COVID-19 Epidemic on Socio-Economic Activities in China Using OMI-NO2 Monitoring Results

Cao

Han

2021

Preprint

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As an air pollutant closely related to urban traffic and heavy industrial capacity, the variation of NO2 (Nitrogen Dioxide) concentration can directly reflect the strength of socio-economic activities. Using the weekly average results of daily product synthesis of tropospheric NO2 column concentrations from OMI satellite inversion, a weekly-scale variation series of standardized socioeconomic activity index during the Spring Festival period of 2019-2021 is constructed. The results show that the Spring Festival holiday also suppresses socioeconomic activity in normal years, but the Coronavirus disease 2019 (COVID-19) epidemic leads to an extended period of 2-3 weeks of weakened socioeconomic activity in China after the holiday, while the minimum value of socioeconomic activity intensity decreases by 0.12. Although socio-economic activity is significantly suppressed in the short term, the intensity of socio-economic activity rises steadily with the gradual resumption of work and production everywhere from the third week after the Spring Festival in China and has reached 60.91% of the highest level before the holiday in the seventh week after the holiday.

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Section: Characteristics Of Tropno2 Column Concentrations In China During the Sf Season In The Last Three Yearsmentioning

confidence: 91%

Section: Temporal Variation Of Industrial Thermal Anomalies Under the In Uence Of Epidemicsmentioning

confidence: 99%

The Short-Term Impact of the COVID-19 Epidemic on Socio-Economic Activities in China Using OMI-NO2 Monitoring Results

Cao

Han

2021

Preprint

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“…The number of industrial thermal anomalies can indicate the spatial distribution of industrial production as well as the various characteristics, and the magnitude of radiation intensity can characterize the scale of industrial production and energy consumption, which indirectly reflects the air quality condition (Sun et al 2020(Sun et al , 2019. Thermal anomalies are mostly distributed in three heavy industrial cities, Tangshan, Tianjin, and Handan in China (Fig.…”

Section: Temporal Variation Of Industrial Thermal Anomalies Under the Influence Of Epidemicsmentioning

confidence: 99%

The short-term impact of the COVID-19 epidemic on socioeconomic activities in China based on the OMI-NO2 data

Cao

Han

2021

Environ Sci Pollut Res

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“…In addition, air pollution sources were detected from MODIS remote sensing data to view aerosols in 1 km resolution using Glowworm Swarm Optimization (GSO) (Chen et al 2017). Other studies on monitoring air pollution with remote sensing data are related to determining industrial pollution emissions from VIIRS Nightfire data (Sun et al 2020), measuring aerosols in the metropolitan area (Vratolis et al 2020), measuring PM 2.5 concentration with MODIS data and machine learning (X. , measuring air pollution from motor vehicles on urban roads (Smit et al 2019), and detection of aerosols using MODIS data (Filonchyk et al 2017). In another study, research of methane variability in Pakistan, Afghanistan and surrounding areas was carried out using Sciamachy/ Envisat data (ul-Haq et al 2015) Other studies related to the use of remote sensing data for air pollution analysis are studies that look at air pollution inputs to a specific desert area, the Mojave Desert, using the airborne, in-situ and remote sensing satellite data as desert ecosystems are particularly vulnerable to pollution from urban activities.…”

Section: Introductionmentioning

confidence: 99%

Monitoring Of Co, No2 And So2 Levels During The Covid-19 Pandemic In Iran Using Remote Sensing Imagery

Sari

Kuncoro

2021

GES

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The COVID-19 pandemic has had a major impact on various sectors. Iran is one of the countries most affected by this pandemic. After considering the huge impact, the government imposed strict rules prohibiting social gatherings and restricting travel for the entire population following the large number of victims in the country. These restrictions lead to changes in the environment, especially air quality. The purpose of this study was to find out how the COVID-19 pandemic affected air quality in Iran following the activity restrictions in the region. The method used in this research was based on the use of multitemporal Sentinel-5P data processing with scripts available on the Google Earth Engine applied on the images, acquired in the period before and after the COVID-19 pandemic. The data used included the image collection of Sentinel-5P NRTI CO: Near Real-Time Carbon Monoxide, Sentinel-5P NRTI NO2: Near Real-Time Nitrogen Dioxide and Sentinel-5P NRTI SO2: Near Real-Time Sulphur Dioxide. The results showed, that for Iran in general, changes in the concentration of CO are clearly visible in urban areas with high population activity such as Tehran, where there was a decrease from 0.05 to 0.0286 mol/m2, while for other areas it is also influenced by the varying climate conditions, which affect the level of pollution. For the NO2 pollutant, there was a significant decrease in pollution levels in big cities such as Tehran, Qom, Isfahan and Mashhad from 0.0002 to 0.000114 mol/m2. For the SO2 pollutant, there was a decrease in pollution levels in Iran’s big cities from 0.0005 to 0.0000714 mol/m2. For Tehran province, which is the most populous and busiest province in Iran, it can be observed that there was also a decrease in the concentration of pollutants after the lockdown compared to the pre-lockdown period. The CO concentration decreased from 0.043 to 0.036 mol/m2, while for the NO2 pollutant there was a decrease from 0.0002 to 0.000142 mol/m2 and for the SO2 pollutant, there was a decrease from 0.0005 to 0.000143 mol/m2.

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Variation of industrial air pollution emissions based on VIIRS thermal anomaly data

Cited by 19 publications

References 34 publications

The Short-Term Impact of the COVID-19 Epidemic on Socio-Economic Activities in China Using OMI-NO2 Monitoring Results

The Short-Term Impact of the COVID-19 Epidemic on Socio-Economic Activities in China Using OMI-NO2 Monitoring Results

The short-term impact of the COVID-19 epidemic on socioeconomic activities in China based on the OMI-NO2 data

Monitoring Of Co, No2 And So2 Levels During The Covid-19 Pandemic In Iran Using Remote Sensing Imagery

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