Impact of data quality and surface-to-column representativeness on the PM_2.5 / satellite AOD relationship for the contiguous United States

Satellite-derived aerosol optical depth (AOD) observations have been used to estimate particulate matter smaller than 2.5 μm (PM_2.5). However, such a relationship could be affected by the representativeness of satellite-derived AOD to surface aerosol particle mass concentration and satellite AOD data quality. Using purely measurement-based methods, we have explored the impacts of data quality and representativeness on the AOD-inferred PM_2.5 / AOD relationship for the contiguous United States (CONUS). This is done through temporally and spatially collocated data sets of PM_2.5 and AOD retrievals from Aqua/Terra Moderate Resolution Imaging Spectroradiometer (MODIS), Multi-angle Imaging Spectroradiometer (MISR), and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP). These analyses show that improving data quality of satellite AOD, such as done with data assimilation-grade retrievals, increases their correlation with PM_2.5. However, overall correlation is relatively low across the CONUS. Also, integrated extinction observed within 500 m above ground level (a.g.l.), as measured by CALIOP, is not well representative of the total column AOD. Surface aerosol in the eastern CONUS is better correlated with total column AOD than in the western CONUS. The best correlation values are found for estimated dry mass CALIOP extinction at 200–300 m a.g.l. and PM_2.5, but additional work is needed to address the ability of using actively sensed AOD as a proxy for PM_2.5 concentrations

Dust-wind interactions can intensify aerosol pollution over eastern China.

Eastern China has experienced severe and persistent winter haze episodes in recent years due to intensification of aerosol pollution. In addition to anthropogenic emissions, the winter aerosol pollution over eastern China is associated with unusual meteorological conditions, including weaker wind speeds. Here we show, based on model simulations, that during years with decreased wind speed, large decreases in dust emissions (29%) moderate the wintertime land-sea surface air temperature difference and further decrease winds by -0.06 (±0.05) m s-1 averaged over eastern China. The dust-induced lower winds enhance stagnation of air and account for about 13% of increasing aerosol concentrations over eastern China. Although recent increases in anthropogenic emissions are the main factor causing haze over eastern China, we conclude that natural emissions also exert a significant influence on the increases in wintertime aerosol concentrations, with important implications that need to be taken into account by air quality studies

Study of the correlation between columnar aerosol burden, suspended matter at ground and chemical components in a background European environment

Although routinely monitored by ground based air quality networks, the particulate matter distribution could be eventually better described with remote sensing techniques. However, valid relationships between ground level and columnar ground based quantities should be known beforehand. In this study we have performed a comparison between particulate matter measurements at ground level at different cut sizes (10, 2.5 and 1.0 mm), and the aerosol optical depth obtained by means of a ground based sunphotometer during a multiinstrumental field campaign held in El Arenosillo (Huelva, Spain) from 28 June to 4 July 2006. All the PM fractions were very well correlated with AOD with correlation coefficients that ranged from 0.71 to 0.81 for PM10, PM2.5 and PM1. Furthermore, the influence of the mixing layer height in the correlations was explored. The improvement in the correlation when the vertical distribution is taken into account was significant for days with a homogeneous mixing layer. Moreover, the chemical analysis of the individual size fractions allowed us to study the origin of the particulate matter. Secondary components were the most abundant and also well correlated in the three size fractions; but for PM10 fraction, chemical species related to marine origin were best correlated. Finally, we obtained a relationship between MODIS L3 AOD from collection 5.1 and the three PM cut sizes. In spite of being a relatively clean environment, all the techniques were able to capture similar day to day variations during this field campaign.Peer ReviewedPostprint (published version

Aerosol optical properties and composition over a table top complex mining area in the Monsoon trough region

Aerosol physiochemical properties over a varied mining plateau region at the eastern end of a monsoon trough are reported for the first time and analyzed at different time scales. Aerosol optical depth (single scattering albedo, SSA) is found to be 0.49 (0.9) in pre-monsoon, 0.4 (0.94) in monsoon, 0.46 (0.92) in post-monsoon, and 0.36 (0.89) in winter, with an annual mean of 0.43 (0.91). The volume-size distribution is tri-modal, with 0.02 (ultra-fine), 0.2 (accumulation) and 7 (coarse) µm, but with seasonal signatures. The angstrom exponent (AE) varies along with the AOD, especially in winter, although they are inversely related to each other during monsoons; the increase in size may be due to the effect of humidity. AODbc varies between 13.4%–4.7% of the total aerosols, with the highest contribution in March, when forest burning in the north east is at its peak. BC is the lowest in July, the mid monsoon month with the minimum biomass burning and brick-kiln activities. It is likely that the interactions of various minerals and intermittent rains help keep the aerosol size in a mixed state with regard to the relation between AE and AOD, although more work is needed to confirm this. The chemical composition of aerosols is derived from an aerosol chemical model based on the measured amount of black carbon and the assumed components. These components are selected based on back trajectories and earlier reports from the region. Their concentrations are adjusted by constraining the model output AOD and SSA to match (±2% @ 500 nm) that observed by a sun-sky radiometer. The chemical compositions of the winter and post-monsoon months are similar, while pre-monsoon period has more coarse mode minerals, and the monsoon period has more sea-salt (accu.). The component mass concentrations were grouped into various size bins based on their modal radii, and the results indicate that PM1 is at its maximum in winter whereas PM2.5 is highest in the post-monsoon period. Monsoons leads to the effective washout of 2.5–10 µm sized particles

Aerosol optical properties and composition over a table top complex mining area in a monsoon trough region

Aerosol physiochemical properties over a varied mining plateau region at the eastern end of a monsoon trough are reported for the first time and analyzed at different time scales. Aerosol optical depth (single scattering albedo, SSA) is found to be 0.49 (0.9) in pre-monsoon, 0.4 (0.94) in monsoon, 0.46 (0.92) in post-monsoon, and 0.36 (0.89) in winter, with an annual mean of 0.43 (0.91). The volume-size distribution is tri-modal, with 0.02 (ultra-fine), 0.2 (accumulation) and 7 (coarse) µm, but with seasonal signatures. The angstrom exponent (AE) varies along with the AOD, especially in winter, although they are inversely related to each other during monsoons; the increase in size may be due to the effect of humidity. AODbc varies between 13.4%–4.7% of the total aerosols, with the highest contribution in March, when forest burning in the north east is at its peak. BC is the lowest in July, the mid monsoon month with the minimum biomass burning and brick-kiln activities. It is likely that the interactions of various minerals and intermittent rains help keep the aerosol size in a mixed state with regard to the relation between AE and AOD, although more work is needed to confirm this. The chemical composition of aerosols is derived from an aerosol chemical model based on the measured amount of black carbon and the assumed components

Effects of Meteorology Changes on Inter-Annual Variations of Aerosol Optical Depth and Surface PM2.5 in China—Implications for PM2.5 Remote Sensing

PM2.5 retrieval from satellite-observed aerosol optical depth (AOD) is still challenging due to the strong impact of meteorology. We investigate influences of meteorology changes on the inter-annual variations of AOD and surface PM2.5 in China between 2006 and 2017 using a nested 3D chemical transport model, GEOS-Chem, by fixing emissions at the 2006 level. We then identify major meteorological elements controlling the inter-annual variations of AOD and surface PM2.5 using multiple linear regression. We find larger influences of meteorology changes on trends of AOD than that of surface PM2.5. On the seasonal scale, meteorology changes are beneficial to AOD and surface PM2.5 reduction in spring (1–50%) but show an adverse effect on aerosol reduction in summer. In addition, major meteorological elements influencing variations of AOD and PM2.5 are similar between spring and fall. In winter, meteorology changes are favorable to AOD reduction (−0.007 yr−1, −1.2% yr−1; p < 0.05) but enhanced surface PM2.5 between 2006 and 2017. The difference in winter is mainly attributed to the stable boundary layer that isolates surface PM2.5 from aloft. The significant decrease in AOD over the years is related to the increase in meridional wind speed at 850 hPa in NCP (p < 0.05). The increase of surface PM2.5 in NCP in winter is possibly related to the increased temperature inversion and more stable stratification in the boundary layer. This suggests that previous estimates of wintertime surface PM2.5 using satellite measurements of AOD corrected by meteorological elements should be used with caution. Our findings provide potential meteorological elements that might improve the retrieval of surface PM2.5 from satellite-observed AOD on the seasonal scale

Assessing the Challenges of Surface‐Level Aerosol Mass Estimates From Remote Sensing During the SEAC4RS and SEARCH Campaigns: Baseline Surface Observations and Remote Sensing in the Southeastern United States

The Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC4RS) campaign conducted in the southeast United States (SEUS) during the summer of 2013 provided a singular opportunity to study local aerosol chemistry and investigate aerosol radiative properties and PM2.5 relationships, focusing on the complexities involved in simplifying the relationship into a linear regression. We utilize three Southeastern Aerosol Research and Characterization network sites and one Environmental Protection Agency Chemical Speciation Network station that afforded simultaneous Aerosol Robotic Network (AERONET) aerosol optical depth (AOD) and aerosol mass, chemistry, and light scattering monitoring. Prediction of AERONET AOD using linear regression of daily‐mean PM2.5 during the SEAC4RS campaign yielded r2 of 0.36–0.53 and highly variable slopes across four sites. There were further reductions in PM2.5 predictive skill using Moderate Resolution Imaging Spectroradiometer (MODIS) and Multi‐angle Imaging SpetroRadiometer (MISR) AOD data, which have shorter correlation lengths and times relative to surface PM2.5. Long‐term trends in aerosol chemistry and optical properties in the SEUS are also investigated and compared to SEAC4RS period data, establishing that the SEUS experienced significant reduction in aerosol mass, corresponding with changes in both aerosol chemistry and optical properties. These changes have substantial impact on the PM2.5‐AOD linear regression relationship and reinforce the need for long‐term aerosol observation stations in addition to concentrated field campaigns

Improved Estimation of PM2.5 Using Lagrangian Satellite-Measured Aerosol Optical Depth

Suspended particulate matter (aerosols) with aerodynamic diameters less than 2.5 μm (PM2.5) has negative effects on human health, plays an important role in climate change and also causes the corrosion of structures by acid deposition. Accurate estimates of PM2.5 concentrations are thus relevant in air quality, epidemiology, cloud microphysics and climate forcing studies. Aerosol optical depth (AOD) retrieved by the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite instrument has been used as an empirical predictor to estimate ground-level concentrations of PM2.5. These estimates usually have large uncertainties and errors. The main objective of this work is to assess the value of using upwind (Lagrangian) MODIS-AOD as predictors in empirical models of PM2.5. The upwind locations of the Lagrangian AOD were estimated using modeled backward air trajectories. Since the specification of an arrival elevation is somewhat arbitrary, trajectories were calculated to arrive at four different elevations at ten measurement sites within the continental United States. A systematic examination revealed trajectory model calculations to be sensitive to starting elevation. With a 500 m difference in starting elevation, the 48-hr mean horizontal separation of trajectory endpoints was 326 km. When the difference in starting elevation was doubled and tripled to 1000 m and 1500m, the mean horizontal separation of trajectory endpoints approximately doubled and tripled to 627 km and 886 km, respectively. A seasonal dependence of this sensitivity was also found: the smallest mean horizontal separation of trajectory endpoints was exhibited during the summer and the largest separations during the winter. A daily average AOD product was generated and coupled to the trajectory model in order to determine AOD values upwind of the measurement sites during the period 2003-2007. Empirical models that included in situ AOD and upwind AOD as predictors of PM2.5 were generated by multivariate linear regressions using the least squares method. The multivariate models showed improved performance over the single variable regression (PM2.5 and in situ AOD) models. The statistical significance of the improvement of the multivariate models over the single variable regression models was tested using the extra sum of squares principle. In many cases, even when the R-squared was high for the multivariate models, the improvement over the single models was not statistically significant. The R-squared of these multivariate models varied with respect to seasons, with the best performance occurring during the summer months. A set of seasonal categorical variables was included in the regressions to exploit this variability. The multivariate regression models that included these categorical seasonal variables performed better than the models that didn\u27t account for seasonal variability. Furthermore, 71% of these regressions exhibited improvement over the single variable models that was statistically significant at a 95% confidence level

An case of extreme particulate matter concentrations over Central Europe caused by dust emitted over the southern Ukraine

On 24 March 2007, an extraordinary dust plume was observed in the Central European troposphere. Satellite observations revealed its origins in a dust storm in Southern Ukraine, where large amounts of soil were resuspended from dried-out farmlands at wind gusts up to 30 m s?1. Along the pathway of the plume, maximum particulate matter (PM10) mass concentrations between 200 and 1400 ?g m?3 occurred in Slovakia, the Czech Republic, Poland, and Germany. Over Germany, the dust plume was characterised by a volume extinction coefficient up to 400 Mm?1 and a particle optical depth of 0.71 at wavelength 0.532 ?m. In-situ size distribution measurements as well as the wavelength dependence of light extinction from lidar and Sun photometer measurements confirmed the presence of a coarse particle mode with diameters around 2?3 ?m. Chemical particle analyses suggested a fraction of 75% crustal material in daily average PM10 and up to 85% in the coarser fraction PM10?2.5. Based on the particle characteristics as well as a lack of increased CO and CO2 levels, a significant impact of biomass burning was ruled out. The reasons for the high particle concentrations in the dust plume were twofold: First, dust was transported very rapidly into Central Europe in a boundary layer jet under dry conditions. Second, the dust plume was confined to a relatively stable boundary layer of 1.4?1.8 km height, and could therefore neither expand nor dilute efficiently. Our findings illustrate the capacity of combined in situ and remote sensing measurements to characterise large-scale dust plumes with a variety of aerosol parameters. Although such plumes from Southern Eurasia seem to occur rather infrequently in Central Europe, its unexpected features highlights the need to improve the description of dust emission, transport and transformation processes needs, particularly when facing the possible effects of further anthropogenic desertification and climate change