Characterizing concentration of NO2 in Indonesia using satellite instrument of GOME 2 MetOp-B

Raden Kokoh Haryo Putro; Arie Dipareza Syafei; Rachmat Boedisantoso; Abdu Fadli Assomadi; Agus Slamet; Joni Hermana

doi:10.1166/asl.2017.8355

Characterizing concentration of NO₂ in Indonesia using satellite instrument of GOME 2 MetOp-B

Raden Kokoh Haryo Putro, Arie Dipareza Syafei^*, Rachmat Boedisantoso, Abdu Fadli Assomadi, Agus Slamet, Joni Hermana

^*Corresponding author for this work

Institut Teknologi Sepuluh Nopember

Research output: Contribution to journal › Article › peer-review

Abstract

Indonesia is a country with rapid development of industries, trades, tourism and transportation in recent years. This causes a progressive increase in energy use, and as a result, more emission of pollutants; one of them is nitrogen dioxide (NO₂). NO₂ is a pollutant resulting from the burning of fossil fuels. Based on that fact, air quality monitoring is crucial as part of air quality management. However, air quality monitoring stations in Indonesia are still lacking, and there is only a few that remain active, such as in Jakarta and Surabaya. To overcome this problem, one can use NO₂ information from satellite instruments. The linear correlation analysis based on NO₂ concentration data and totals column data from satellite remote sensing of GOME 2 MetOp-B was conducted to make spatial predictions of NO₂ concentration in Indonesia. It is observed that relationship between NO₂ ground measurements (y) and total column NO₂ (x) follows regression formula y = 2·10⁻⁵x +0.0002 with R² value of 0.436. This technique demonstrates a promising alternative in characterizing and predicting NO₂ concentration in areas where there are no monitoring stations.

Original language	English
Pages (from-to)	1384-1387
Number of pages	4
Journal	Advanced Science Letters
Volume	23
Issue number	2
DOIs	https://doi.org/10.1166/asl.2017.8355
Publication status	Published - Feb 2017

Keywords

GOME 2 MetOp B
NO
NO total column
Urban air pollution

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1166/asl.2017.8355

Cite this

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title = "Characterizing concentration of NO2 in Indonesia using satellite instrument of GOME 2 MetOp-B",

abstract = "Indonesia is a country with rapid development of industries, trades, tourism and transportation in recent years. This causes a progressive increase in energy use, and as a result, more emission of pollutants; one of them is nitrogen dioxide (NO2). NO2 is a pollutant resulting from the burning of fossil fuels. Based on that fact, air quality monitoring is crucial as part of air quality management. However, air quality monitoring stations in Indonesia are still lacking, and there is only a few that remain active, such as in Jakarta and Surabaya. To overcome this problem, one can use NO2 information from satellite instruments. The linear correlation analysis based on NO2 concentration data and totals column data from satellite remote sensing of GOME 2 MetOp-B was conducted to make spatial predictions of NO2 concentration in Indonesia. It is observed that relationship between NO2 ground measurements (y) and total column NO2 (x) follows regression formula y = 2·10−5x +0.0002 with R2 value of 0.436. This technique demonstrates a promising alternative in characterizing and predicting NO2 concentration in areas where there are no monitoring stations.",

keywords = "GOME 2 MetOp B, NO, NO total column, Urban air pollution",

author = "Putro, {Raden Kokoh Haryo} and Syafei, {Arie Dipareza} and Rachmat Boedisantoso and Assomadi, {Abdu Fadli} and Agus Slamet and Joni Hermana",

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AU - Putro, Raden Kokoh Haryo

AU - Syafei, Arie Dipareza

AU - Boedisantoso, Rachmat

AU - Assomadi, Abdu Fadli

AU - Slamet, Agus

AU - Hermana, Joni

PY - 2017/2

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N2 - Indonesia is a country with rapid development of industries, trades, tourism and transportation in recent years. This causes a progressive increase in energy use, and as a result, more emission of pollutants; one of them is nitrogen dioxide (NO2). NO2 is a pollutant resulting from the burning of fossil fuels. Based on that fact, air quality monitoring is crucial as part of air quality management. However, air quality monitoring stations in Indonesia are still lacking, and there is only a few that remain active, such as in Jakarta and Surabaya. To overcome this problem, one can use NO2 information from satellite instruments. The linear correlation analysis based on NO2 concentration data and totals column data from satellite remote sensing of GOME 2 MetOp-B was conducted to make spatial predictions of NO2 concentration in Indonesia. It is observed that relationship between NO2 ground measurements (y) and total column NO2 (x) follows regression formula y = 2·10−5x +0.0002 with R2 value of 0.436. This technique demonstrates a promising alternative in characterizing and predicting NO2 concentration in areas where there are no monitoring stations.

AB - Indonesia is a country with rapid development of industries, trades, tourism and transportation in recent years. This causes a progressive increase in energy use, and as a result, more emission of pollutants; one of them is nitrogen dioxide (NO2). NO2 is a pollutant resulting from the burning of fossil fuels. Based on that fact, air quality monitoring is crucial as part of air quality management. However, air quality monitoring stations in Indonesia are still lacking, and there is only a few that remain active, such as in Jakarta and Surabaya. To overcome this problem, one can use NO2 information from satellite instruments. The linear correlation analysis based on NO2 concentration data and totals column data from satellite remote sensing of GOME 2 MetOp-B was conducted to make spatial predictions of NO2 concentration in Indonesia. It is observed that relationship between NO2 ground measurements (y) and total column NO2 (x) follows regression formula y = 2·10−5x +0.0002 with R2 value of 0.436. This technique demonstrates a promising alternative in characterizing and predicting NO2 concentration in areas where there are no monitoring stations.

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