Satellite remote sensing of particulate matter and air quality assessment over global cities

Gupta, Pawan; Christopher, Sundar A.; Wang, Jun; Gehrig, Robert; Lee, Yc; Kumar, Naresh

doi:10.1016/j.atmosenv.2006.03.016

Cited by 550 publications

(322 citation statements)

References 28 publications

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“…However, the correlation between AOT and surface PM 2.5 varies for different seasons and locations (Hoff and Christopher, 2009), and AOT hardly reflects the spatial pattern of surface PM 2.5 (Paciorek and Liu, 2008). What is more, all this research was carried out at PM 2.5 observations less than 100 μg/m 3 , because higher PM 2.5 concentrations lead to biased and inaccurate predictions (Gupta et al, 2006), which is always the case in Beijing. Besides, missing values frequently occur in AOT data, especially when the weather is cloudy or hazy.…”

Section: Introductionmentioning

confidence: 99%

Spatial-temporal characteristics of PM2.5 in China: A city-level perspective analysis

Fang

Wang

2016

J. Geogr. Sci.

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Abstract:Haze pollution has become a severe environmental problem in the daily life of the people in China. PM 2.5 makes a significant contribution to poor air quality. The spatio-temporal features of China's PM 2.5 concentrations should be investigated. This paper, based on observed data from 945 newly located monitoring sites in 2014 and industrial working population data obtained from International Standard Industrial Classification (ISIC), reveals the spatio-temporal variations of PM 2.5 concentrations in China and the correlations among different industries. We tested the spatial autocorrelation of PM 2.5 concentrations in the cities of China with the spatial autocorrelation model. A correlation coefficient to examine the correlativity of PM 2.5 concentrations and 23 characteristic variables for 190 cities in China in 2014, from which the most important ones were chosen, and then a regression model was built to further reveal the social and economic factors affecting PM 2.5 concentrations. Results: (1) The Hu Huanyong Line and the Yangtze River were the E-W divide and S-N divide between high and low values of China. (2) The PM 2.5 concentrations shows great seasonal variation, which is high in autumn and winter but low in spring and summer. The monthly average shows a U-shaped pattern, and daily average presents a periodic and impulse-shaped change. (3) PM 2.5 concentrations had a distinct characteristic of spatial agglomeration. The North China Plain was the predominant region of agglomeration, and the southeastern coastal area had stable good air quality.

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Section: Introductionmentioning

confidence: 99%

Spatial-temporal characteristics of PM2.5 in China: A city-level perspective analysis

Fang

Wang

2016

J. Geogr. Sci.

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“…All these studies were carried out with PM 2.5 observations of less than 100 mg/m 3 , because higher PM 2.5 concentrations will lead to biased and inaccurate predictions (Liu et al 2005), which happens in Beijing. Besides, missing values frequently occur in AOT data, especially when it is cloudy or hazy (Gupta et al 2006). Monitoring data is typically represented in high dimensionality data sets in which each pollutant is assigned a concentration for each time period of observation, which could well reflect the PM 2.5 concentration within the target region (Austin et al 2013).…”

Section: Introductionmentioning

confidence: 99%

Spatial-temporal characteristics and determinants of PM2.5 in the Bohai Rim Urban Agglomeration

2016

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“…Estimation of small-area variations in traffic pollution are important to the exposure experience of the population and may detect health effects that would have gone unnoticed with other exposure estimates [Watmough et al, 2014]. Despite increasing urban development and anthropogenic activities, monitored data on urban air pollution are sparse in Nigeria and many developing countries [Baumbach et al, 1995;Gupta et al, 2006;Abam and Unachukwu, 2009], hence the collection of accurate and reliable data necessary for the evaluation of urban air quality is therefore very important. This study aim to collect, analyze and map the gaseous traffic related air pollutants (CO, NO x , NO 2 and SO 2 ) at road junctions, intersections, and motor garages in order to facilitate the management of air pollutions in the study area.…”

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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Satellite remote sensing of particulate matter and air quality assessment over global cities

Cited by 550 publications

References 28 publications

Spatial-temporal characteristics of PM2.5 in China: A city-level perspective analysis

Spatial-temporal characteristics of PM2.5 in China: A city-level perspective analysis

Spatial-temporal characteristics and determinants of PM2.5 in the Bohai Rim Urban Agglomeration

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

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