Estimating Air Particulate Matter Using MODIS Data and Analyzing Its Spatial and Temporal Pattern over the Yangtze Delta Region

Abstract

Abstract:The deteriorating air quality in the Yangtze delta region is attracting growing public concern. In this paper, seasonal estimation models of the surface particulate matter (PM) were established by using aerosol optical thickness (AOT) retrievals from the moderate resolution imaging spectro-radiometer (MODIS) on board NASA's Terra satellite. The change of the regional distribution of the atmospheric mixed layer, relative humidity and meteorological elements have been taken into account in these models. We also used PM mass concentrations of ground measurements to evaluate the estimation accuracy of those models. The results show that model estimation of PM 2.5 and PM 10 mass concentrations were in good agreement with the ground-based observation of PM mass concentrations (p < 0.01, the R 2 value of the PM 2.5 concentrations experimental model for four seasons are 0.48, 0.62, 0.61 and 0.52 respectively. The R 2 value of PM 10 concentrations experimental model for four seasons are 0.57, 0.56, 0.64 and 0.68 respectively). At the same time, spatial and temporal variations of PM 2.5 and PM 10 mass concentrations were analysed over the Yangtze delta region from 2000 to 2013. The results show that PM 2.5 and PM 10 show a trend of increase in the Yangtze delta region from 2000 to 2013 and change periodically. The maximum mass concentration of PM 2.5 and PM 10 was in January-February, and the minimum was in July-August. The highest values of PM 2.5 and PM 10 mass concentration are in the region of urban agglomeration which is grouped to a delta-shaped region by Shanghai, Hangzhou and Nanjing, while the low values are in the forest far away from the city. PM mass concentration over main cities and rural areas increased gradually year by year, and were increasing more quickly in urban areas than in rural areas.

Analysis of waste management effect on the climate related disease in Larangan Village, Sidoarjo

“…This was demonstrated in the present study, as previously described by Wang et al [20]. Usually, many predictors, such as road density [18,21,22], MODIS AOD [13,23,24], meteorological data [12,13,23,24], Landsat 8 bands and Indices [12,18,21], and topography [25], are applied to predicting the PM2.5 air quality level. However, in this study, we used only Landsat 8 bands, environmental indices, and slope to estimate the PM2.5 air quality.…”

Application of machine learning models and landsat 8 data for estimating seasonal pm 2.5 concentrations

“…This was demonstrated in the present study, as previously described by Wang et al [ 20 ]. Usually, many predictors, such as road density [ 18 , 21 , 22 ], MODIS AOD [ 13 , 23 , 24 ], meteorological data [ 12 , 13 , 23 , 24 ], Landsat 8 bands and Indices [ 12 , 18 , 21 ], and topography [ 25 ], are applied to predicting the PM 2.5 air quality level. However, in this study, we used only Landsat 8 bands, environmental indices, and slope to estimate the PM 2.5 air quality.…”

Machine-Learning Approaches for Assessing Aerosol Optical Depth (AOD) in Ghana, West Africa

“…This metric directly reflects the extent of aerosol presence, providing valuable insights into the overall concentration of optically active particles within each geographical location (Dandou et al, 2002). In the domain of satellite-based aerosol retrievals, AOD datasets are predominantly sourced from two key satellite observations: the Moderate Resolution Imaging Spectroradiometer (MODIS) (Kanabkaew, 2013;Hu et al, 2013;Xu et al, 2016), and the Terra Multi-angle Imaging SpectroRadiometer (MISR) (Liu et al, 2007;Kahn and Gaitley, 2015).…”