Application of air pollution models and remote sensing in Air Quality Management

Dubey, Bhawna

doi:10.15373/2249555x/may2014/78

Cited by 6 publications

(5 citation statements)

References 9 publications

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“…Against this background, there is a need for improved information on levels of traffic-related air pollution which can be used to help investigate the relationship involved as inputs to health risk assessment, to assist in establishing and monitoring air quality standards [Raju et al, 2012;Dons et al, 2013]. It has been established that the measuring urban air pollutants requires a spatiotemporal data management, which is provided by advances in geographic information systems (GIS) and earth observations [Wu, 2006;ESRI, 2007;Hadjimitsis et al, 2010;Dubey, 2014;Sameen et al, 2014]. GIS-based pollution mapping, using interpolation techniques, such as inverse distance weighting and kriging have proved efficient in the assessment of urban air pollution [van der Kassteele , 2006;ESRI, 2007;Chattopadhyay et al, 2010].…”

Section: Introductionmentioning

confidence: 99%

Mapping of Traffic-Related Air Pollution Using GIS Techniques in Ijebu-Ode, Nigeria

Adedeji

Oluwafunmilayo

Oluwaseun

2016

IJG

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Spatial and temporal characteristics of traffic related air pollutants (CO, NO, NO 2 and SO 2 ) in Ijebu-ode, Nigeria were determined using replicate portable gas detectors (Land Duo Multi Gas Monitor) at selected road junctions, motor garages and markets. Mapping of different concentration of air pollutants was carried out using kriging type of interpolation method in GIS environment. Concentration of CO ranges from 4.8 ppm at Erinlu/Molipa Roundabout to 137ppm on Sagamu/Ore Expressway. Concentrations of NO 2 range from 100-662 ppb with overall average value (OAV) of 299.8 ppb, while concentration of nitrogen oxide (NO) ranges between 67-302 ppb and OAV of 166.23 ppb. SO 2 had concentration ranging between 38-245 ppb and an OAV of 139.07 ppb all of which are above standard ambient air quality standards. AQI indicated very unhealthy air quality in most areas which calls for the need to establish and strengthen the health-based standard for air pollutants. Abstrak Karakteristik spasial dan temporal polutan udara terkait lalu lintas (CO, NO, NO 2 dan SO 2 ) di Ijebu-ode, Nigeria ditentukan dengan detektor gas portable replika (Land Duo Multi Gas Monitor) di persimpangan jalan yang dipilih, garasi bermotor dan pasar. Pemetaan berbagai konsentrasi polutan udara dilakukan dengan menggunakan jenis kriging dari metode interpolasi di GIS. Konsentrasi CO berkisar dari 4,8 ppm di bundaran Erinlu

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Section: Introductionmentioning

confidence: 99%

Mapping of Traffic-Related Air Pollution Using GIS Techniques in Ijebu-Ode, Nigeria

Adedeji

Oluwafunmilayo

Oluwaseun

2016

IJG

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“…The monitoring procedure aids in identifying pollution patterns, the types and concentrations of pollutants and, the detrimental consequences they have (Nayeb et al, 2015;Raimi et al, 2018a). On regional, national and international levels, monitoring and modeling of pollution are encouraged (Dubey, 2014). Modern tools are replacing outdated monitoring methods as a result of technological advancements.…”

Section: Introductionmentioning

confidence: 99%

“…Traditional ground measurements which are often used for mapping areas with significant levels of air pollution are expensive and time consuming (Dubey, 2014). For this method, the number of air pollution stations in each location limits the coverage of the sensors, such as air samples, which are extremely expensive.…”

Section: Introductionmentioning

confidence: 99%

“…Remote sensing and geographic information system (GIS) are better. They have the advantage of being more accurate, faster and can cover wider areas leading to precise and timely geographic information due to their broad spatial coverage and trustworthy repeated measurements (Dubey, 2014;Sameen et al, 2014). Urban air pollution has been effectively assessed using GIS-based and interpolation methods inverse distance weighting and kriging (Watmough et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

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Identification of the spatial patterns of air pollution and its sources in Ogui New Layout, South-East of Nigeria, using remote sensing and GIS technology

Obiageli,

Ernest,

Eucharia

2023

Sci. Res. Essays

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Air pollution is one of the main risk factors for human health. It is the deadliest form of environmental pollution, which the World Health Organization has recognized as a significant indicator of the status of the environment. Therefore, there is a need for monitoring of air pollution to reduce health hazards associated with it. In this study, Remote Sensing and Geographic Information System were used to examine the spatiotemporal distribution of air pollutants in Ogui New Layout, South-East, Nigeria so as to advise the populace and relevant agencies on the steps and needs in reducing air pollution. Based on dense industrial clusters, heavy traffic and increasing human population density, strategic places were sampled between two peak hours (8 am and 4 pm). A Haze-dust particulate monitor and a Gasman Air Monitor were used to measure the amounts of PM10, CO, SO2, and NO2. The global positioning system (GPS) coordinates of the sampling spots were recorded using the portable Germin-300 GPS device analyzer which was also used in data processing for creating spatial interpolation maps in ArcMap. ArcGIS 10.5 Erdas Imaging 9.5 software was used to analyze the spatial distribution of contaminants from a source using remote sensing and geographic information system (GIS) methods. Results showed that during the dry season, the air pollutants (PM10, CO, SO2 , and NO2) had maximum values of 225, 10.72, 1.74, 1.93 respectively and minimum values of 184, 8.86, 0.82, and 0.45 respectively. During the wet season, PM10, CO, SO2 and NO2 had maximum values of 202, 16.66, 1.65, 0.73 respectively and minimum values of 108, 11.85, 0.69 and 0.26 respectively. Based on the study, it is advised that the local people of Ogui New Layout reduce their exposure to the outdoors especially during the dry season.

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“…Against this background, there is a need for improved information on levels of urban related air pollution, this can be used to help investigate the relationship involved as inputs to health risk assessment, to assist in establishing and monitoring of air quality standards (Raju, Partheeban and Hemamalini, 2012;Dons, Van Poppel, Kochan and Wets, 2013). It has been established that the measuring of urban air pollutants requires a spatial and multivariate data management, which is provided by advances in geographic information systems and earth observations (Wu, 2006;ESRI, 2007;Hadjimitsis et al, 2010;Dubey, 2014;Sameen, Al Kubaisy, Nahhas, Ali, Othman and Hason, 2014;Adedeji et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Assessment of the Spatial Relationship between Air Pollutants in Kaduna Metropolis, Nigeria

Daful¹,

Adewuyi²,

Muhammad³

et al. 2020

JSD

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This study analyzes the varying spatial relationship between air pollutants [Carbon monoxide (CO), Sulphur dioxide (SO2), Carbon dioxide (CO2) and Particulate Matter (PM2.5 and PM10)] in Kaduna metropolis. This was aimed at having more insight into be characteristics of air pollutants in the study area, which was geared towards efficient environmental management of the city. Validated portable pollutant monitors (MSA Altair 5x Gas Detectora, CO2 meter Model AZ-0002-DL and CW-HAT200 Particulate Counter) were utilized for data collection, on the concentration of air pollutants CO and SO2; CO2 and particulate matters (PM2.5 and PM10) respectively. Pearson correlation and Geographical Weighed Regression (GWR) were used for data analysis, whereas, maps and tables were utilized for data presentation. The findings revealed that a non - perfect positive linear relationship exists between the air pollutants. The GWR model revealed that the distribution of the local R2 values vary spatially across Kaduna Metropolis, indicating that there is a spatially varying relationship between the air pollutants. Higher R2 values were mostly in the central and southern part of the metropolis except for PM2.5 and PM10 in the Northern part of the metropolis. The finding further reveals that CO has the strongest association with SO2, SO2 has the strongest association with PM2.5 and PM10, CO2 with SO2, PM10 has the highest association with PM2.5, likewise PM2.5 with PM10. The use of GWR to establish the existence of locally varying relationship between the air pollutants was successful. Hence, this study recommends the use of GWR to model air pollutants in cities with limited monitoring capability.

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Application of air pollution models and remote sensing in Air Quality Management

Cited by 6 publications

References 9 publications

Mapping of Traffic-Related Air Pollution Using GIS Techniques in Ijebu-Ode, Nigeria

Mapping of Traffic-Related Air Pollution Using GIS Techniques in Ijebu-Ode, Nigeria

Identification of the spatial patterns of air pollution and its sources in Ogui New Layout, South-East of Nigeria, using remote sensing and GIS technology

Assessment of the Spatial Relationship between Air Pollutants in Kaduna Metropolis, Nigeria

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