Application of Moderate Resolution Imaging Spectroradiometer (MODIS) Aerosol Optical Depth (AOD) and Weather Research Forecasting (WRF) model meteorological data for assessment of fine particulate matter (PM2.5) over India

Sathe, Yogesh; Kulkarni, Santosh H.; Gupta, Pawan; Kaginalkar, Akshara; Islam, Sahidul; Gargava, Prashant

doi:10.1016/j.apr.2018.08.016

Cited by 54 publications

(17 citation statements)

References 65 publications

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“…4 are weak (R = 0.34 for London, R = 0.23 for Birmingham) and do not improve if we apply a less strict threshold for the number of observations required to calculate monthly means. The poor correlation may be due to environmental factors that complicate the relationship between AOD and surface PM 2.5 , such as variability in meteorological conditions, aerosol composition, enhancements in aerosols above the boundary layer, and the aerosol radiative properties (Schaap et al, 2009;van Donkelaar et al, 2016;Shaddick et al, 2018;Sathe et al, 2019). We find that the same assessment is not feasible for Delhi or Kanpur as the record of surface PM 2.5 and PM 10 in these cities is too short.…”

Section: Assessment Of Modis Aodmentioning

confidence: 84%

Long-term trends in air quality in major cities in the UK and India: a view from space

et al. 2021

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Abstract. Air quality networks in cities can be costly and inconsistent and typically monitor a few pollutants. Space-based instruments provide global coverage spanning more than a decade to determine trends in air quality, augmenting surface networks. Here we target cities in the UK (London and Birmingham) and India (Delhi and Kanpur) and use observations of nitrogen dioxide (NO2) from the Ozone Monitoring Instrument (OMI), ammonia (NH3) from the Infrared Atmospheric Sounding Interferometer (IASI), formaldehyde (HCHO) from OMI as a proxy for non-methane volatile organic compounds (NMVOCs), and aerosol optical depth (AOD) from the Moderate Resolution Imaging Spectroradiometer (MODIS) for PM2.5. We assess the skill of these products at reproducing monthly variability in surface concentrations of air pollutants where available. We find temporal consistency between column and surface NO2 in cities in the UK and India (R = 0.5–0.7) and NH3 at two of three rural sites in the UK (R = 0.5–0.7) but not between AOD and surface PM2.5 (R < 0.4). MODIS AOD is consistent with AERONET at sites in the UK and India (R ≥ 0.8) and reproduces a significant decline in surface PM2.5 in London (2.7 % a−1) and Birmingham (3.7 % a−1) since 2009. We derive long-term trends in the four cities for 2005–2018 from OMI and MODIS and for 2008–2018 from IASI. Trends of all pollutants are positive in Delhi, suggesting no air quality improvements there, despite the roll-out of controls on industrial and transport sectors. Kanpur, identified by the WHO as the most polluted city in the world in 2018, experiences a significant and substantial (3.1 % a−1) increase in PM2.5. The decline of NO2, NH3, and PM2.5 in London and Birmingham is likely due in large part to emissions controls on vehicles. Trends are significant only for NO2 and PM2.5. Reactive NMVOCs decline in Birmingham, but the trend is not significant. There is a recent (2012–2018) steep (> 9 % a−1) increase in reactive NMVOCs in London. The cause for this rapid increase is uncertain but may reflect the increased contribution of oxygenated volatile organic compounds (VOCs) from household products, the food and beverage industry, and domestic wood burning, with implications for the formation of ozone in a VOC-limited city.

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Section: Assessment Of Modis Aodmentioning

confidence: 84%

Long-term trends in air quality in major cities in the UK and India: a view from space

et al. 2021

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“…The AOD is always measured at a specific wavelength and represents columnar loading of atmospheric aerosols from the surface to top-of-the-atmosphere. There have been many studies ( Gupta et al, 2006 ; Liu, Paciorek, & Koutrakis, 2009 ; van Donkelaar, Martin, Li, & Burnett, 2019 ; Sathe et al, 2019 ; Yang et al, 2019; Christopher & Gupta, 2020 ) over the years to demonstrate the relationship between AOD and surface mass concentration of PM 2.5 . In this study, we have not used AOD to derive PM 2.5 but used it as a surrogate to assess the relative change in particle pollution in the atmosphere due to lockdown.…”

Section: Resultsmentioning

confidence: 99%

Surface and satellite observations of air pollution in India during COVID-19 lockdown: Implication to air quality

Sathe

Gupta

Bawase

et al. 2021

Sustainable Cities and Society

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“…The traditional ground measurement method using PM 2.5 monitoring network can provide the accurate spatial and temporal data; however, it cannot be used for deriving PM 2.5 concentrations where the monitors are unavailable (Liu et al 2005;Yang et al 2018). The aerosol optical depth (AOD) derived from satellites [e.g., Moderate Resolution Imaging Spectroradiometer (MODIS), Multi-Angle Implementation of Atmospheric Correction (MAIAC)] have been widely utilized for predicting PM 2.5 concentrations in different scales (Zeng et al 2018;Goldberg et al 2019;Mhawish et al 2019;Sathe et al 2019).…”

Section: Introductionmentioning

confidence: 99%

Estimating PM2.5 Concentrations in Contiguous Eastern Coastal Zone of China Using MODIS AOD and a Two-Stage Random Forest Model

Yang

Xu²,

2021

Journal of Atmospheric and Oceanic Technology

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The coarse moderate-resolution imaging spectroradiometer (MODIS) aerosol optical depth (AOD) product (spatial resolution: 3 km) retrieved by dark-target algorithm always generates the missing values when being adopted to estimate the ground-level PM2.5 concentrations. In this study, we developed a two-stage random forest using MODIS 3 km AOD to obtain the PM2.5 concentrations with full-coverage in a contiguous coastal developed region, i.e., Yangtze River Delta-Fujian-Pearl River Delta region of China (YRD-FJ-PRD). A first-stage random forest integrated six meteorological fields was employed to predict the missing values of AOD product, and the combined AOD (i.e., random forest derived AOD and MODIS 3 km AOD) incorporated with other ancillary variables were developed for predicting PM2.5 concentrations within a second-stage random forest model. The results showed that the first-stage random forest could explain 94% of the AOD variability over YRD-FJ-PRD region, and we achieved a site-based cross validation (CV) R2 of 0.87 and a time-based CV R2 of 0.85, respectively. The full-coverage PM2.5 concentrations illustrated a spatial pattern with annual-mean PM2.5 of 46, 40 and 35 μg/m3 in YRD, PRD and FJ, respectively, sharing the same trend with previous studies. Our results indicated that the proposed two-stage random forest model could be effectively used for PM2.5 estimation in different areas.

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Application of Moderate Resolution Imaging Spectroradiometer (MODIS) Aerosol Optical Depth (AOD) and Weather Research Forecasting (WRF) model meteorological data for assessment of fine particulate matter (PM2.5) over India

Cited by 54 publications

References 65 publications

Long-term trends in air quality in major cities in the UK and India: a view from space

Long-term trends in air quality in major cities in the UK and India: a view from space

Surface and satellite observations of air pollution in India during COVID-19 lockdown: Implication to air quality

Estimating PM2.5 Concentrations in Contiguous Eastern Coastal Zone of China Using MODIS AOD and a Two-Stage Random Forest Model

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