Estimating urban PM10 and PM2.5 concentrations, based on synergistic MERIS/AATSR aerosol observations, land cover and morphology data

Abstract

This study evaluates alternative spatio-temporal approaches for quantitative estimation of daily mean Particulate Matter (P M) concentrations. Both fine (P M 2.5) and coarse (P M 10) concentrations were estimated over the area of London (UK) for the 2002-2012 time period, using Aerosol Optical Thickness (AOT) derived from MERIS (Medium Resolution Imaging Spectrometer) / AATSR (Advanced Along-Track Scanning Radiometer) synergistic observations at 1 km × 1 km resolution. Relative humidity, temperature and the K-Index obtained from MODIS (Moderate Resolution Imaging Spectroradiometer) sensor were used as additional predictors. High-resolution (100 m × 100 m) local urban land cover and morphology datasets were incorporated in the analysis in order to capture the effects of local scale emissions and sequestration. Spatial (2-D) and spatio-temporal (3-D) kriging were applied to in situ urban P M measurements to investigate their association with satellite-derived AOT while accounting for differences in spatial support. Linear mixed-effects models with day-specific and site-specific random intercepts and slopes were estimated to associate satellite-derived products with kriged P M concentration and their

A Review on Predicting Ground PM2.5 Concentration Using Satellite Aerosol Optical Depth

“…Furthermore, with AOD derived from Medium Resolution Imaging Spectrometer (MERIS) and Advanced Along-Track Scanning Radiometer (AATSR) synergistic observations. Beloconi et al [108] applied MEM to the evaluation of the day-specific and site-specific random effects in London. Their results showed a CV-R 2 value of 0.846 between 2002 and 2012.…”

Fusing Observational, Satellite Remote Sensing and Air Quality Model Simulated Data to Estimate Spatiotemporal Variations of PM2.5 Exposure in China

“…Satellite-derived aerosol optical depth (AOD) has been successfully associated with ground PM 2.5 [24] and has thus been used to generate spatiotemporal estimators of PM 2.5 by acting as a primary predictor in statistical models such as LUR [25][26][27] or being calibrated by ratios (PM 2.5 /AOD) simulated by a chemical transport model (e.g., GEOS-Chem) [28,29]. However, due to meteorological or geographical conditions, non-randomly missing values in satellite-derived AOD caused absent estimates of PM 2.5 in specific periods (e.g., winter [26]) or areas (e.g., deserts [28]).…”

“…Liu et al [18] utilized the Ensemble Kalman Filter (EnKF) to assemble in situ observations with daily stimulations of PM 2.5 from an air quality model across China, and applied the analyzed products in risk assessment of chronic exposure to ambient pollution. Beloconi et al [27] mixed spatiotemporal Kriging maps of monitoring data and satellite AOD to estimate fine-scale (1 km × 1 km) daily estimates of PM 2.5 and PM 10 during 2002-2012 over London.…”

Mapping Urban Green Infrastructure: A Novel Landscape-Based Approach to Incorporating Land Use and Land Cover in the Mapping of Human-Dominated Systems

“…Furthermore, Urban Atlas [43] and U.K. Land Cover Map [44] data reveal inconsistencies resulting from variation in mapping units and resolution (see Figures 5 and 7). These inconsistencies, although expected for datasets employing different mapping units and resolution, are relevant given the prevalent use of both of these datasets in international research into urban environments (e.g., [39,[45][46][47][48][49][50][51]) and policy guidance [22].…”