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Airborne particulate matter (PM) is derived from diverse sources--natural and anthropogenic. Climate change processes and remote sensing measurements are affected by the PM properties, which are often lumped into homogeneous size fractions that show spatiotemporal variation. Since different sources are attributed to different geographic locations and show specific spatial and temporal PM patterns, we explored the spatiotemporal characteristics of the PM2.5/PM10 ratio in different areas. Furthermore, we examined the statistical relationships between AERONET aerosol optical depth (AOD) products, satellite-based AOD, and the PM ratio, as well as the specific PM size fractions. PM data from the northeastern United States, from San Joaquin Valley, CA, and from Italy, Israel, and France were analyzed, as well as the spatial and temporal co-measured AOD products obtained from the MultiAngle Implementation of Atmospheric Correction (MAIAC) algorithm. Our results suggest that when both the AERONET AOD and the AERONET fine-mode AOD are available, the AERONET AOD ratio can be a fair proxy for the ground PM ratio. Therefore, we recommend incorporating the finemode AERONET AOD in the calibration of MAIAC. Along with a relatively large variation in the observed PM ratio (especially in the northeastern United States), this shows the need to revisit MAIAC assumptions on aerosol microphysical properties, and perhaps their seasonal variability, which are used to generate the look-up tables and conduct aerosol retrievals. Our results call for further scrutiny of satellite-borne AOD, in particular its errors, limitations, and relation to the vertical aerosol profile and the particle size, shape, and composition distribution. This work is one step of the required analyses to gain better understanding of what the satellite-based AOD represents. Implications: The analysis results recommend incorporating the fine-mode AERONET AOD in MAIAC calibration. Specifically, they indicate the need to revisit MAIAC regional aerosol microphysical model assumptions used to generate look-up tables (LUTs) and conduct retrievals. Furthermore, relatively large variations in measured PM ratio shows that adding seasonality in aerosol microphysics used in LUTs, which is currently static, could also help improve accuracy of MAIAC retrievals. These results call for further scrutiny of satellite-borne AOD for better understanding of its limitations and relation to the vertical aerosol profile and particle size, shape, and composition. [ABSTRACT FROM AUTHOR]

Aerosol loadings in vertical atmospheric columns are commonly measured by satellite-borne or ground instruments that remotely sense the spectral extinction through the optical path. However, correlations of these integrated measurements with ground-level particulate matter concentrations are highly influenced by local meteorology, seasonality, and the surface albedo. Moreover, as most measurements are based on solar radiation, they are limited to daytime. To account for these limitations, we study the feasibility of using a ground hyperspectral camera for acquiring images of artificial light sources through horizontal urban-scale open paths during the night, in order to retrieve the apparent spectral aerosol optical thickness. Laboratory-scale measurements demonstrated a linear response of the camera and set the spectral operational range. A procedure for night-time imaging of illuminating targets through ambient open paths has been developed to enable a consistent selection of pixels for analysis, providing measurable apparent aerosol optical thickness. We demonstrate the validity of this procedure by field acquisition of hyperspectral signatures through different arid and rural open paths in the Negev desert, Israel. An open path of 180 m provided a test case for imaging during clear and stable ambient conditions, from which an inherent measurement error of ∼4% was estimated. Imaging through a very long open path indicated an uppermost open path limit of about 4 km, resulting from a significant attenuation of the sensor's spectral response. Imaging and aerosol optical thickness retrieval under common environmental conditions through urban-scale open paths of about 1 km in Haifa is also demonstrated. Copyright 2012 American Association for Aerosol Research [ABSTRACT FROM AUTHOR]

This study evaluates the performance of MODerate resolution Imaging Spectroradiometer (MODIS) Collection 6 (C6) AOD retrieval algorithms, including Dark Target (DT) aerosol optical depth (AOD) at 3 and 10 km spatial resolutions, Deep Blue (DB) AOD at 10 km, and the merged DT-DB AOD at 10 km across the Indo-Gangetic Plain (IGP), South Asia. A total of 14,736 collocated Aqua MODIS C6 AOD at 550 nm were evaluated against AOD from six AERONET stations over IGP, measured during the satellite overpass (± 1 h) from 2006 to 2015. The effects of aerosol heterogeneity, in terms of both aerosol loading and the aerosol type, on the uncertainty of the satellite-borne AOD retrieval were examined. The DT algorithm at both resolutions (3 km and 10 km) overestimated the AOD by 14–25%, with only 51.37–61.29% of the retrievals falling within the expected error (EE). The DT 3 km algorithm underestimates the surface reflectance in comparison to the DT 10 km, with the latter outperforming the former both in terms of number of collocations and retrieval accuracy, especially over urban areas. The DB 10 km was able to retrieve AOD over both arid/desert regions and vegetated surfaces even under low aerosol loading conditions. Yet, its performance was still poor, with retrieval accuracy of 53.76%, low RMSE (0.214), and generally underestimated AOD across the IGP. The merged DT-DB AOD product was mostly dominated by DT retrievals (73%–100%), except over bright land surfaces and 56.03% of the merged DT-DB retrievals fell within the EE. The retrieval accuracy of MODIS C6 products was found to be strongly dependent on the estimated surface reflectance and the aerosol type. Across IGP, DB predicted the surface reflectance better while DT at both resolutions overestimated the surface reflectance at varying extent. For high aerosol loading conditions with varying aerosol size, retrieval accuracy of DT 10 km poses lower sensitivity while DT at 3 km exhibits larger uncertainty in estimating surface reflectance. In contrast, DB 10 km shows greater bias that depends on the aerosol size. For very high aerosol loading conditions, dominated by fine or mixed aerosols, all the algorithms have errors in the aerosol model. The DT 10 km, DB 10 km and the merged AOD performed almost equally within the threshold level while the DT 3 km showed the poorest performance in terms of retrieval accuracy and RMSE. We conclude that across IGP, DB 10 km AOD has the highest accuracy in retrieving fine mode aerosols while DT 10 km AOD has almost identical accuracy in retrieving varying aerosol types. For coarse dominated aerosols, when the dissimilarity between DT and DB remains highest, the merged AOD is found to have higher accuracy in retrieving AOD across IGP. [ABSTRACT FROM AUTHOR]

Abstract The Multiangle Implementation of Atmospheric Correction (MAIAC) is a new generic algorithm applied to collection 6 (C6) MODIS measurements to retrieve Aerosol Optical Depth (AOD) over land at high spatial resolution (1 km). This study is the first evaluation of the MAIAC AOD from MODIS Aqua (A) and Terra (T) satellites between 2006 and 2016 over South Asia. The retrieval accuracy of MAIAC was assessed by comparing it to ground-truth AErosol RObotic NETwork (AERONET) AOD, as well as to AOD retrieved by the two operational MODIS algorithms: Dark Target (DT) and Deep Blue (DB). MAIAC AOD showed higher spatial coverage and retrieval frequency than either the DT or the DB AOD retrievals. The high spatial resolution of the MAIAC retrievals enhances the capability to distinguish aerosol sources and to determine fine aerosol features, such as wildfire smoke plumes and haze over complex geographical regions, and provides more retrievals in conditions that are cloudy or when the surface is partially covered by snow. In comparison to AERONET AOD, MAIAC AOD shows a better accuracy than both DT and DB AOD. A higher number of MAIAC-AERONET AOD matchups demonstrate the capability of MAIAC to retrieve AOD over varied surfaces, different aerosol types and loadings. Our results demonstrate high retrieval accuracy in term of the Expected Error (EE) (A/T, EE: 72.22%, 73.50%), and low root mean square error (A/T, RMSE: 0.148, 0.164), root mean bias (RMB) (A/T, RMB: 0.978, 1.049) and mean absolute error (MAE) (A/T, MAE: 0.098, 0.096). Moreover, MAIAC has a lower bias as a function of the viewing geometry and the aerosol type among the three retrieval algorithms. MAIAC performed well over bright and vegetated land surfaces, showing the highest retrieval accuracy over dense vegetation and particularly well in retrieving smoke AOD, yet it underestimated dust AOD. In conclusion, MAIAC's ability to provide AOD at high spatial resolution appears promising over South Asia, thus having advantage over contemporary aerosol retrieval algorithms for epidemiological and climatological studies. Capsule In comparison with MODIS DT and DB AOD, and AERONET AOD, MAIAC shows improved accuracy and lower bias over South Asia, as well as with greater spatial coverage. Graphical abstract Distribution of Aqua and Terra MODIS MAIAC, DT and DB AOD bias with respect to AERONET over South Asia. Unlabelled Image Highlights • Comprehensive evaluation of MAIAC AOD vs AERONET AOD and DT/DB AOD over South Asia • High-resolution MAIAC is capable to distinguish aerosol sources and fine feature. • MAIAC retrieval accuracy is higher than DT/DB with more accuracy over dark surface. • MAIAC has less sensitivity to variation in aerosol types across the seasons. • MAIAC best retrieve AOD for smoke events while underestimate AOD for dust. [ABSTRACT FROM AUTHOR]

Abstract Satellite-based particulate matter (PM) models provide spatially and temporally resolved estimations, allowing greater spatial-temporal coverage compared to sparse ground monitoring stations. The spatio-temporal resolution of these models can be improved using aerosol optical depth (AOD) products from various satellite platforms with different overpass times which can capture possible changes in diurnal aerosol concentrations. Israel is characterized by diverse geo-climatic regions and it is subjected to frequent dust storms events. Our goal was to estimate PM 10 and PM 2.5 concentrations in Israel on daily and intra-daily (mean PM around the Aqua and Terra overpasses) temporal resolutions and to assess the differences between these time windows. A hybrid modeling approach that consists of three stages was used enabling spatially continuous PM estimations at 1 × 1 km spatial resolution. The model was calibrated on a daily basis applying a mixed modeling approach using MODIS-based MAIAC AOD and various spatial and temporal predictors. It was found that in certain urban areas the measured and estimated PM concentrations during the satellite overpass (Terra or Aqua) were higher than the mean daily PM. The models performed well showing cross-validated R2 that ranged between 0.82 and 0.92. Mean estimated PM for the study period (2005–2015) during days with no dust events showed different spatial patterns for the daily and intra-daily estimations and revealed areas in Israel that are affected by high PM concentrations (mainly industrial or dense urban areas). Estimations from these models are useful for epidemiological research and might contribute to environmental regulatory purposes by focusing the efforts of PM pollution reduction at the identified polluted areas. Highlights • Daily and intra-daily PM 10 and PM 2.5 concentrations were estimated in Israel. • The model used satellite-based MAIAC AOD data and various spatio-temporal predictors. • Intra-daily PM concentrations were higher than daily concentrations in urban areas. • The models performed well with cross-validated R2 that ranged between 0.82 and 0.92. [ABSTRACT FROM AUTHOR]

Estimates of exposure to PM 2.5 are often derived from geographic characteristics based on land-use regression or from a limited number of fixed ground monitors. Remote sensing advances have integrated these approaches with satellite-based measures of aerosol optical depth (AOD), which is spatially and temporally resolved, allowing greater coverage for PM 2.5 estimations. Israel is situated in a complex geo-climatic region with contrasting geographic and weather patterns, including both dark and bright surfaces within a relatively small area. Our goal was to examine the use of MODIS-based MAIAC data in Israel, and to explore the reliability of predicted PM 2.5 and PM 10 at a high spatiotemporal resolution. We applied a three stage process, including a daily calibration method based on a mixed effects model, to predict ground PM 2.5 and PM 10 over Israel. We later constructed daily predictions across Israel for 2003–2013 using spatial and temporal smoothing, to estimate AOD when satellite data were missing. Good model performance was achieved, with out-of-sample cross validation R 2 values of 0.79 and 0.72 for PM 10 and PM 2.5 , respectively. Model predictions had little bias, with cross-validated slopes (predicted vs. observed) of 0.99 for both the PM 2.5 and PM 10 models. To our knowledge, this is the first study that utilizes high resolution 1 km MAIAC AOD retrievals for PM prediction while accounting for geo-climate complexities, such as experienced in Israel. This novel model allowed the reconstruction of long- and short-term spatially resolved exposure to PM 2.5 and PM 10 in Israel, which could be used in the future for epidemiological studies. [ABSTRACT FROM AUTHOR]

Exposure to particles with an aerodynamic diameter smaller than 2.5 μm (PM 2.5 ) adversely impacts human health. In many geographical regions where ground PM 2.5 monitoring is spatially sparse and unsuitable for environmental health inference, satellite remote sensing can potentially be used for estimating human exposure to PM 2.5 . However, retrieval of the aerosol optical depth (AOD) using the Dark Target (DT) algorithm is uncertain in many regions worldwide (e.g. western USA, the Middle East and central Asia) due to low signal-to-noise ratio as a result of high surface reflectivity in the spectral bands used by the algorithm. In this study we use the Deep Blue (DB) algorithm as well as a combined DB-DT algorithm for AOD retrievals. The AOD products are used to predict ground PM 2.5 using mixed effects models and the daily calibration approach. Models for the two study areas (Israel and San Joaquin Valley, Central California) were developed independently and then compared to each other. Using the AOD DB within a mixed effects model considerably improved PM 2.5 prediction in high reflectance regions, revealing in both study areas enhanced model performance (in terms of both R 2 and the root mean square prediction error), significant increase in the spatiotemporal availability of the AOD product, and improved PM 2.5 prediction relative to using AOD DT retrievals. [ABSTRACT FROM AUTHOR]