Monitoring of urban air pollution from MODIS aerosol data: effect of meteorological parameters

Abstract

A B S T R A C T Remote sensors designed specifically for studying the atmosphere have been widely used to derive timely information on air pollution at various scales. Whether the satellite-generated aerosol optical thickness (AOT) data can be used to monitor air pollution, however, is subject to the effect of a number of meteorological parameters. This study analyses the influence of four meteorological parameters (air pressure, air temperature, relative humidity, and wind velocity) on estimating particulate matter (PM) from MODIS AOT data for the city of Nanjing, China during [2004][2005][2006]. After the PM data were correlated with the AOT data that had been divided into four chronological seasons, a minimum correlation coefficient of 0.47 was found for the winter season, but a much stronger correlation (r > 0.80) existed in summer and autumn. Similar analyses were carried out after all observations were clustered into four groups based on their meteorological similarity using the K-Means analysis. Grouping caused more observations to be useable in the monitoring of air pollution than season-based analysis. Of the four groups, three had a correlation coefficient higher than 0.60. Grouping-based analysis enables the pollution level to be determined more accurately from MODIS AOT data at a higher temperature and relative humidity, but a lower air pressure and wind velocity. The accuracy of monitoring air pollution is inversely related to the pollution level. Thus, remote sensing monitoring of air pollution has its limits.

Analysis of Spatial and Temporal Variability of PM10 Concentrations Using MODIS Aerosol Optical Thickness in the Pearl River Delta Region, China

“…Higher correlation coefficients and slopes were observed at urban sites (R = 0.466; Slope = 68.12) than suburban sites (R = 0.324, Slope = 37.98). Also, higher correlation coefficients were observed in summer (R = 0.453) and fall (R = 0.428) than those in spring (R = 0.392) and winter (R = 0.200).Such variations have also been reported in other studies (Tian and Chen, 2010;Zha et al, 2010).For example, Tian and Chen (2010) suggested that the correlation between AOT and PM was stronger in the spring and summer while weaker in the fall and winter for the area of southern Ontario. Zha et al (2010) found a minimum correlation coefficient between AOT and PM for the season of winter (R = 0.47), but a much stronger correlation (R > 0.80) in summer and autumn for the city of Nanjing in China.…”

“…Despite similar results observed in these studies, different explanations were given for these seasonal variations. In Tian and Chen's study (2010), the wavelength, at which the AOT product was derived from MODIS, was expected to be closely associated with the correlation between AOT and PM; while Zha et al (2010) reported that high pollution levels may reduce the accuracy of MODIS monitoring data in winter. Previous study also reported that MODIS retrieving algorithms has relatively good capability of retrieving fine mode aerosols (with effective radius between 0.1 µm to 0.25 µm), while poor ability for retrieving coarse mode aerosol (with effective radius between 1.0 µm to 2.5 µm) due to currently lack of MODIS data over high reflectance of natural dust sources (Dubovik et al, 2007;Santese et al, 2007).…”

Temporal Variability and Characterization of Aerosols across the Pakistan Region during the Winter Fog Periods

“…Additionally, MODIS AOD was also not available on certain days because of cloud cover. The observed correlation of r = 0.67 is quite good and is in agreement with the range stated in previous studies [89][90][91][92] for similar kinds of comparison. Furthermore, the correlation depends on various factors such as seasonal variation, meteorological parameters, spatial resolution of the satellite pixel (10 km in this case) and aloft aerosol layer [89][90][91][92].…”

Meteorological Influence on Predicting Air Pollution from MODIS-Derived Aerosol Optical Thickness: A Case Study in Nanjing, China

“…Therefore, PM 10 both summer and autumn have an improved accuracy while the accuracy for the spring and winter seasons is lower. In other words, the air pollution conditions recorded in satellite imagery are able to depict the pollution level better during June-November than during the rest of the year [16]. The likely reason to account for the varying degree of prediction accuracy is the meteorological conditions in different seasons that control the circulation of pollutants in the atmosphere.…”