Impact of the Dust Aerosol Model on the VIIRS Aerosol Optical Depth (AOD) Product across China

The Visible Infrared Imaging Radiometer Suite (VIIRS) has been observing aerosol optical depth (AOD), which is a critical parameter in air pollution and climate change, for more than 7 years since 2012. Due to limited and uneven distribution of the Aerosol Robotic Network (AERONET) station in China, the independent data from the Campaign on Atmospheric Aerosol Research Network of China (CARE-China) was used to evaluate the National Oceanic and Atmospheric Administration (NOAA) VIIRS AOD products in six typical sites and analyze the influence of the aerosol model selection process in five subregions, particularly for dust. Compared with ground-based observations, the performance of all retrievals (except the Shapotou (SPT) site) is similar to other previous studies on a global scale. However, the results illustrate that the AOD retrievals with the dust model showed poor consistency with a regression equation as y = 0.312x + 0.086, while the retrievals obtained from the other models perform much better with a regression equation as y = 0.783x + 0.119. The poor AOD retrieval with the dust model was also verified by a comparison with the Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol product. The results show they have a lower correlation coefficient (R) and a higher mean relative error (MRE) when the aerosol model used in the retrieval is identified as dust. According to the Ultraviolet Aerosol Index (UVAI), the frequency of dust type over southern China is inconsistent with the actual atmospheric condition. In addition, a comparison of ground-based Ångström exponent (α) values yields an unexpected result that the dust model percentage exceed 40% when α < 1.0, and the mean α shows a high value of ~0.75. Meanwhile, the α peak value (~1.1) of the “dust” model determined by a satellite retravel algorithm indicate there is some problem in the dust model selection process. This mismatching of the aerosol model may partly explain the low accuracy at the SPT and the systemic biases in regional and global validations.

Automated Aerosol Classification from Spectral UV Measurements Using Machine Learning Clustering

In this study, we present an aerosol classification technique based on measurements of a double monochromator Brewer spectrophotometer during the period 1998–2017 in Thessaloniki, Greece. A machine learning clustering procedure was applied based on the Mahalanobis distance metric. The classification process utilizes the UV Single Scattering Albedo (SSA) at 340 nm and the Extinction Angstrom Exponent (EAE) at 320–360 nm that are obtained from the spectrophotometer. The analysis is supported by measurements from a CIMEL sunphotometer that were deployed in order to establish the training dataset of Brewer measurements. By applying the Mahalanobis distance algorithm to the Brewer timeseries, we automatically assigned measurements in one of the following clusters: Fine Non Absorbing Mixtures (FNA): 64.7%, Black Carbon Mixtures (BC): 17.4%, Dust Mixtures (DUST): 8.1%, and Mixed: 9.8%. We examined the clustering potential of the algorithm by reclassifying the training dataset and comparing it with the original one and also by using manually classified cases. The typing score of the Mahalanobis algorithm is high for all predominant clusters FNA: 77.0%, BC: 63.9%, and DUST: 80.3% when compared with the training dataset. We obtained high scores as well FNA: 100.0%, BC: 66.7%, and DUST: 83.3% when comparing it with the manually classified dataset. The flags obtained here were applied in the timeseries of the Aerosol Optical Depth (AOD) at 340 nm of the Brewer and the CIMEL in order to compare between the two and also stress the future impact of the proposed clustering technique in climatological studies of the station.

Can MERRA-2 Reanalysis Data Reproduce the Three-Dimensional Evolution Characteristics of a Typical Dust Process in East Asia? A Case Study of the Dust Event in May 2017

This study used the MERRA-2 reanalysis dataset and ground-based and satellite observational data to comprehensively analyze a typical dust storm event in east Asia on 2–7 May 2017 which engulfed most of China as well as ocean and Japan, and explore the accuracy and comprehensiveness of the MERRA-2 dataset in the analysis of dust processes. The results of comparison show that the description of the spatiotemporal distribution and evolution of the dust aerosols in the dust event using the MERRA-2 data is consistent with the data of AERONET, National Urban Air Quality Real-time Publishing Platform and Hamawari-8. Gobi Deserts was the most influential source area of this dust event with the highest emissions reaching 1.9 × 106 μg/m3. The vertical motion of the atmosphere can lift dust from the source area above 500 hPa. There were low-pressure troughs at 500 and 850 hPa and the winds behind and in front of the trough led to the high-altitude, long-distance transport of dust. Dust gradually affected the northwest China, north China, northeast China, and even the ocean and Japan on 2–7 May. This study demonstrates that although there is some uncertainty about the source of dust emission in the MERRA-2 model, the data accurately simulated the evolution of the dust event and analyze it comprehensively, while the accuracy of simulating the long-term evolution of dust requires further evaluation.

Bounding Global Aerosol Radiative Forcing of Climate Change

Aerosols interact with radiation and clouds. Substantial progress made over the past 40 years in observing, understanding, and modeling these processes helped quantify the imbalance in the Earth's radiation budget caused by anthropogenic aerosols, called aerosol radiative forcing, but uncertainties remain large. This review provides a new range of aerosol radiative forcing over the industrial era based on multiple, traceable, and arguable lines of evidence, including modeling approaches, theoretical considerations, and observations. Improved understanding of aerosol absorption and the causes of trends in surface radiative fluxes constrain the forcing from aerosol-radiation interactions. A robust theoretical foundation and convincing evidence constrain the forcing caused by aerosol-driven increases in liquid cloud droplet number concentration. However, the influence of anthropogenic aerosols on cloud liquid water content and cloud fraction is less clear, and the influence on mixed-phase and ice clouds remains poorly constrained. Observed changes in surface temperature and radiative fluxes provide additional constraints. These multiple lines of evidence lead to a 68% confidence interval for the total aerosol effective radiative forcing of -1.6 to -0.6 W m-2, or -2.0 to -0.4 W m-2 with a 90% likelihood. Those intervals are of similar width to the last Intergovernmental Panel on Climate Change assessment but shifted toward more negative values. The uncertainty will narrow in the future by continuing to critically combine multiple lines of evidence, especially those addressing industrial-era changes in aerosol sources and aerosol effects on liquid cloud amount and on ice clouds.

Spatiotemporal Characteristics of the Association between AOD and PM over the California Central Valley

Many air pollution health effects studies rely on exposure estimates of particulate matter (PM) concentrations derived from remote sensing observations of aerosol optical depth (AOD). Simple but robust calibration models between AOD and PM are therefore important for generating reliable PM exposures. We conduct an in-depth examination of the spatial and temporal characteristics of the AOD-PM2.5 relationship by leveraging data from the Distributed Regional Aerosol Gridded Observation Networks (DRAGON) field campaign where eight NASA Aerosol Robotic Network (AERONET) sites were co-located with EPA Air Quality System (AQS) monitoring sites in California’s Central Valley from November 2012 to April 2013. With this spatiotemporally rich data we found that linear calibration models (R2 = 0.35, RMSE = 10.38 μg/m3) were significantly improved when spatial (R2 = 0.45, RMSE = 9.54 μg/m3), temporal (R2 = 0.62, RMSE = 8.30 μg/m3), and spatiotemporal (R2 = 0.65, RMSE = 7.58 μg/m3) functions were included. As a use-case we applied the best spatiotemporal model to convert space-borne MultiAngle Imaging Spectroradiometer (MISR) AOD observations to predict PM2.5 over the region (R2 = 0.60, RMSE = 8.42 μg/m3). Our results imply that simple AERONET AOD-PM2.5 calibrations are robust and can be reliably applied to space-borne AOD observations, resulting in PM2.5 prediction surfaces for use in downstream applications.

Spatiotemporal Imputation of MAIAC AOD Using Deep Learning with Downscaling

Aerosols have adverse health effects and play a significant role in the climate as well. The Multiangle Implementation of Atmospheric Correction (MAIAC) provides Aerosol Optical Depth (AOD) at high temporal (daily) and spatial (1 km) resolution, making it particularly useful to infer and characterize spatiotemporal variability of aerosols at a fine spatial scale for exposure assessment and health studies. However, clouds and conditions of high surface reflectance result in a significant proportion of missing MAIAC AOD. To fill these gaps, we present an imputation approach using deep learning with downscaling. Using a baseline autoencoder, we leverage residual connections in deep neural networks to boost learning and parameter sharing to reduce overfitting, and conduct bagging to reduce error variance in the imputations. Downscaled through a similar auto-encoder based deep residual network, Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2) GMI Replay Simulation (M2GMI) data were introduced to the network as an important gap-filling feature that varies in space to be used for missingness imputations. Imputing weekly MAIAC AOD from 2000 to 2016 over California, a state with considerable geographic heterogeneity, our full (non-full) residual network achieved mean R2 = 0.94 (0.86) [RMSE = 0.007 (0.01)] in an independent test, showing considerably better performance than a regular neural network or non-linear generalized additive model (mean R2 = 0.78-0.81; mean RMSE = 0.013-0.015). The adjusted imputed as well as combined imputed and observed MAIAC AOD showed strong correlation with Aerosol Robotic Network (AERONET) AOD (R = 0.83; R2 = 0.69, RMSE = 0.04). Our results show that we can generate reliable imputations of missing AOD through a deep learning approach, having important downstream air quality modeling applications.

A Robust Deep Learning Approach for Spatiotemporal Estimation of Satellite AOD and PM2.5

Accurate estimation of fine particulate matter with diameter ≤2.5 μm (PM2.5) at a high spatiotemporal resolution is crucial for the evaluation of its health effects. Previous studies face multiple challenges including limited ground measurements and availability of spatiotemporal covariates. Although the multiangle implementation of atmospheric correction (MAIAC) retrieves satellite aerosol optical depth (AOD) at a high spatiotemporal resolution, massive non-random missingness considerably limits its application in PM2.5 estimation. Here, a deep learning approach, i.e., bootstrap aggregating (bagging) of autoencoder-based residual deep networks, was developed to make robust imputation of MAIAC AOD and further estimate PM2.5 at a high spatial (1 km) and temporal (daily) resolution. The base model consisted of autoencoder-based residual networks where residual connections were introduced to improve learning performance. Bagging of residual networks was used to generate ensemble predictions for better accuracy and uncertainty estimates. As a case study, the proposed approach was applied to impute daily satellite AOD and subsequently estimate daily PM2.5 in the Jing-Jin-Ji metropolitan region of China in 2015. The presented approach achieved competitive performance in AOD imputation (mean test R2: 0.96; mean test RMSE: 0.06) and PM2.5 estimation (test R2: 0.90; test RMSE: 22.3 μg/m3). In the additional independent tests using ground AERONET AOD and PM2.5 measurements at the monitoring station of the U.S. Embassy in Beijing, this approach achieved high R2 (0.82–0.97). Compared with the state-of-the-art machine learning method, XGBoost, the proposed approach generated more reasonable spatial variation for predicted PM2.5 surfaces. Publically available covariates used included meteorology, MERRA2 PBLH and AOD, coordinates, and elevation. Other covariates such as cloud fractions or land-use were not used due to unavailability. The results of validation and independent testing demonstrate the usefulness of the proposed approach in exposure assessment of PM2.5 using satellite AOD having massive missing values.

Study of Chemical and Optical Properties of Biomass Burning Aerosols during Long-Range Transport Events toward the Arctic in Summer 2017

Biomass burning related aerosol episodes are becoming a serious threat to the radiative balance of the Arctic region. Since early July 2017 intense wildfires were recorded between August and September in Canada and Greenland, covering an area up to 4674 km2 in size. This paper describes the impact of these biomass burning (BB) events measured over Svalbard, using an ensemble of ground-based, columnar, and vertically-resolved techniques. BB influenced the aerosol chemistry via nitrates and oxalates, which exhibited an increase in their concentrations in all of size fractions, indicating the BB origin of particles. The absorption coefficient data (530 nm) at ground reached values up to 0.6 Mm–1, highlighting the impact of these BB events when compared to average Arctic background values, which do not exceed 0.05 Mm–1. The absorption behavior is fundamental as implies a subsequent atmospheric heating. At the same time, the AERONET Aerosol Optical Depth (AOD) data showed high values at stations located close to or in Canada (AOD over 2.0). Similarly, increased values of AODs were then observed in Svalbard, e.g., in Hornsund (daily average AODs exceeded 0.14 and reached hourly values up to 0.5). Elevated values of AODs were then registered in Sodankylä and Andenes (daily average AODs exceeding 0.150) a few days after the Svalbard observation of the event highlighting the BB columnar magnitude, which is crucial for the radiative impact. All the reported data suggest to rank the summer 2017 plume of aerosols as one of the biggest atmosphere related environmental problems over Svalbard region in last 10 years.

Extracting Taklimakan Dust Parameters from AIRS with Artificial Neural Network Method

Two back-propagation artificial neural network retrieval models have been developed for obtaining the dust aerosol optical depth (AOD) and dust-top height (DTH), respectively, from Atmospheric InfraRed Sounder (AIRS) brightness temperature (BT) measurements over Taklimakan Desert area. China Aerosol Remote Sensing Network (CARSNET) measurements at Tazhong station were used for dust AOD validation. Results show that the correlation coefficient of dust AODs between AIRS and CARSNET reaches 0.88 with a deviation of −0.21, which is the same correlation coefficient as the AIRS dust AOD and the Moderate-Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) product. In the AIRS DTH retrieval model, there is an option to include the collocated MODIS deep blue (DB) AOD as additional input for daytime retrieval; the independent dust heights from Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) are used for AIRS DTH validation, and results show that the DTHs derived from the combined AIRS BT measurements and MODIS DB AOD product have better accuracy than those from AIRS BT measurements alone. The correlation coefficient of DTHs between AIRS and independent CALIOP dust heights is 0.79 with a standard deviation of 0.41 km when MODIS DB AOD product is included in the retrieval model. A series of case studies from different seasons were examined to demonstrate the feasibility of retrieving dust parameters from AIRS and potential applications. The method and approaches can be applied to process measurements from advanced infrared (IR) sounder and high-resolution imager onboard the same platform.

Long-term validation of MODIS C6 and C6.1 Dark Target aerosol products over China using CARSNET and AERONET

This study provided a comprehensive evaluation of the Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 006 (C6) and 061 (C6.1) Dark Target (DT) 10 km aerosol optical depth (AOD) over China during 2002-2014. Considering that sparse Aerosol Robotic Network (AERONET) sites are available in China, 18 sites from China Aerosol Remote Sensing Network (CARSNET) were also used to conduct this validation. The results showed that C6.1 DT outperform C6 with 59.03% of the retrievals falling within the expected error (EE) compared to C6 (54.94%). Meanwhile, C6.1 DT achieved a reduced RMSE of 0.171, a higher R of 0.901 and a bias closer to 0 relative to C6 (RMSE: 0.185; R: 0.890). When the validation was conducted over different underlying surfaces, C6 DT overestimated AOD by 19.8%, with only 45.01% of the retrievals within the EE over urban sites, whereas C6.1 showed clear improvements, with 11.8% more data falling within the EE. Hardly any improvement was observed in C6.1 over forest, cropland, and grassland sites. The C6.1 DT exhibited more significant improvements over Beijing area and northern China than southern China. The highest retrieval accuracy of 61.05% among the four Beijing sites was achieved at Beijing_CARSNET, but the improvements were lower than other Beijing sites. The extent of the improvements was positively correlated with the percentage of urban pixels over the sites in Beijing and northern China in terms of the retrieval accuracy. Moreover, C6.1 DT had a little effect on improvements over southern China and showed reduced collocation over coastal cities.

The climatology of aerosol optical thickness and radiative effects in Southeast Asia from 18-years of ground-based observations

The present study utilizes 18 years of long-term (2001-2018) data collected from six active AERONET sites over the Indo-Gangetic Plain (IGP) and the North China Plain (NCP) areas in Southeast Asia. The annual mean (±SD) aerosol optical thickness at 440 nm (AOT₄₄₀) was found high at XiangHe (0.92 ± 0.69) and Taihu (0.90 ± 0.51) followed by Beijing (0.81 ± 0.69), Lahore (0.81 ± 0.43), and Kanpur (0.73 ± 0.35) and low at Karachi (0.52 ± 0.23). Seasonally, high AOT₄₄₀ with corresponding high Ångström exponent (ANG_440-870) noticed during JJA for all sites, except Kanpur, suggesting the dominance of fine-mode particles, generally associated with large anthropogenic emissions. Climatologically, an increasing (decreasing) trend was observed over IGP (NCP) sites, with the highest (lowest) percentage of departures in AOT₄₄₀ found over Beijing (Karachi). We further identified major aerosol types which showed the dominance of biomass burning, urban-industrial followed by the mixed type of aerosols. In addition, single scattering albedo (SSA), asymmetry parameter (ASP), volume size distribution (VSD), and complex aerosol refractive index (RI) showed significant temporal and spectral changes, illustrating the complexity of aerosol types. At last, the annual mean direct aerosol radiative forcing at the top, bottom, and within the atmosphere for all sites were found in the range from -17.36 ± 3.75 to -45.17 ± 4.87 W m^-2, -64.6 ± 4.86 to -93.7 ± 10.27 W m^-2, and 40.5 ± 6.43 to 68.25 ± 7.26 W m^-2, respectively, with an averaged atmospheric heating rate of 0.9-2.3 K day^-1. A large amount of anthropogenic aerosols showed a significant effect of heating (cooling) on the atmosphere (surface) results obviously, due to an increased rate of atmospheric heating. Therefore, the thermodynamic effects of anthropogenic aerosols on the atmospheric circulation and its structure should be taken into consideration for future study over the experimental sites.

Three-wavelength polarization Scheimpflug lidar system developed for remote sensing of atmospheric aerosols

Multiple-wavelength polarization lidar techniques have been of great interest for the studies of aerosol backscattering color ratio, Ångström exponent, particle size distribution, hygroscopic growth, etc. Conventional lidar techniques are mainly based on the time-of-flight principle. In this paper, a three-wavelength polarization Scheimpflug lidar (SLidar) system, based on the Scheimpflug imaging principle, has been developed for studying optical properties of atmospheric aerosols. The SLidar system utilizes low-cost, compact, multimode laser diodes as light sources and two complementary metal oxide semiconductor (CMOS) sensors as detectors. The depolarization ratio was measured at the 808 nm band by successively detecting atmospheric backscattering signals from two orthogonally polarized laser beams with a polarization CMOS camera, while the 520 nm and the 405 nm backscattering signals were recorded by a second CMOS camera based on the time-multiplexing scheme. Atmospheric remote measurements were carried out in May and July 2019 on a near-horizontal path. The aerosol extinction coefficient, linear volume depolarization ratio, and the Ångström exponent have been retrieved and evaluated to study aerosol properties during different atmospheric conditions, which were in good agreement with optical properties reported by previous studies.

A Climatological Satellite Assessment of Absorbing Carbonaceous Aerosols on a Global Scale

A global climatology of absorbing carbonaceous aerosols (ACA) for the period 2005–2015 is obtained by using satellite MODIS (Moderate Resolution Imaging Spectroradiometer)-Aqua and OMI (Ozone Monitoring Instrument)-Aura aerosol optical properties and by applying an algorithm. The algorithm determines the frequency of presence of ACA (black and brown carbon) over the globe at 1° × 1° pixel level and on a daily basis. The results of the algorithm indicate high frequencies of ACA (up to 19 days/month) over world regions with extended biomass burning, such as the tropical forests of southern and central Africa, South America and equatorial Asia, over savannas, cropland areas or boreal forests, as well as over urban and rural areas with intense anthropogenic activities, such as the eastern coast of China or the Indo-Gangetic plain. A clear seasonality of the frequency of occurrence of ACA is evident, with increased values during June–October over southern Africa, during July–November over South America, August–November over Indonesia, November–March over central Africa and November–April over southeastern Asia. The estimated seasonality of ACA is in line with the known annual patterns of worldwide biomass-burning emissions, while other features such as the export of carbonaceous aerosols from southern Africa to the southeastern Atlantic Ocean are also successfully reproduced by the algorithm. The results indicate a noticeable interannual variability and tendencies of ACA over specific world regions during 2005–2015, such as statistically significant increasing frequency of occurrence over southern Africa and eastern Asia.

Observational study of aerosol-induced impact on planetary boundary layer based on lidar and sunphotometer in Beijing

Atmospheric aerosols have been found to influence the development of planetary boundary layer (PBL) and hence to aggravate haze pollution in megacities. PBL height (PBLH) determines the vertical extent to which the most pollutant effectively disperses and is a key argument in pollution study. In this study, we quantitatively evaluate aerosol radiation effect on PBL, as well as assessment of surface cooling effect and atmosphere heating effect. All the data are measured at a site of Beijing from 2014 to 2017, of which PBLH is retrieved from micro pulse lidar and aerosol optical depth (AOD) from sunphotometer. Case study shows qualitatively that relative high aerosol load reduces PBLH, and in turn causes a high surface PM_2.5 concentration. We preliminarily reveal the influential mechanism of aerosol on PBL. The influence of aerosol on the radiation flux of PBL is analyzed, with the correlation coefficient (R) of 0.938 between AOD and radiative forcing of BOA (RF_BOA) and R = 0.43 between RF_BOA and PBLH. Also, AOD is found to negatively correlate with PBLH (R = -0.41). With the increase of AOD, the cooling effect of surface is enhanced, and further impede the development of PBL. Due to aerosol-induced reduction of PBLH, near surface PM_2.5 concentration surges and presents an exponential growth following AOD. Then, it is speculated and testified that the relationship between SSA (single scatting albedo) and PBLH would be determined by the location of absorbing aerosol within PBL. The upper PBL absorbing aerosol may decrease PBLH, while the lower absorbing aerosol appear to enhance PBLH. The study probably can provide effective observational evidence for understanding the effect of aerosol on PBL and be a reference of air pollution mitigation in Beijing and its surrounding areas.

Dust Properties and Radiative Impacts at a Suburban Site during 2004–2017 in the North China Plain

Aerosols and their radiative effects are of primary interest in climate research because of their vital influence on climate change. Dust aerosols are an important aerosol type in the North China Plain (NCP), mainly as a result of long-range transport, showing substantial spatiotemporal variations. By using measurements from the Aerosol Robotic Network (AERONET) between September 2004 and May 2017, and the space-borne Moderate Resolution Imaging Spectroradiometer (MODIS) and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) aerosol products, we investigated the properties of dust aerosols and their radiative effects at Xianghe (XH)—a suburban site in the NCP. Dust events occurred most frequently during spring (a total of 105 days) relative to the other three seasons (a total of 41 days) during the periods concerned. The dust aerosol optical depth (AOD) at 675 nm was at a maximum in spring (0.60 ± 0.44), followed (in decreasing order) by those in autumn (0.58 ± 0.39), summer (0.54 ± 0.15), and winter (0.53 ± 0.23). Cooling effects of dust aerosol radiative forcing (RF) at the bottom and top of the atmosphere tended to be strongest in spring (−96.72 ± 45.69 and −41.87 ± 19.66 Wm−2) compared to that in summer (−57.08 ± 18.54 and −25.54 ± 4.45 Wm−2), autumn (−72.01 ± 27.27 and −32.54 ± 15.18 Wm−2), and winter (−79.57 ± 32.96 and −37.05 ± 17.06 Wm−2). The back-trajectory analysis indicated that dust air mass at 500 m that arrived at XH generally originated from the Gobi and other deserts of northern China and Mongolia (59.8%), and followed by northwest China and Kazakhstan (37.2%); few dust cases came from northeast China (3.0%). A single-peaked structure with the maximum occurring at ~2 km was illustrated by all dust events and those sorted by their sources in three directions. Three typical dust events were specifically discussed to better reveal how long-range transport impacted the dust properties and radiative effects over the NCP. The results presented here are expected to improve our understanding of the physical properties of dust aerosols over the NCP and their major transport path and significant impacts on the regional solar radiation budget.

Contrasting Aerosol Optical Characteristics and Source Regions During Summer and Winter Pollution Episodes in Nanjing, China

Two episodes with heavy air pollution in Nanjing, China, one in the summer and another one in the winter of 2017, were selected to study aerosol properties using sun photometer and ground-based measurements, together with source region analysis. The aerosol properties, the meteorological conditions, and the source regions during these two episodes were very different. The episodes were selected based on the air quality index (AQI), which reached a maximum value of 193 during the summer episode (26 May–3 June) and 304 during the winter episode (21–31 December). The particulate matter (PM) concentrations during the winter episode reached maximum values for PM2.5/10 of 254 g m−3 and 345 g m−3, much higher than those during the summer (73 and 185 g m−3). In contrast, the value of aerosol optical depth (AOD) at 500 nm was higher during the summer episode (2.52 0.19) than during that in the winter (1.38 0.18). A high AOD value does not necessarily correspond to a high PM concentration but is also affected by factors, such as wind, Planetary Boundary Layer Height (PBLH), and relative humidity. The mean value of the Ångström Exponent (AE) varied from 0.91–1.42, suggesting that the aerosol is a mixture of invaded dust and black carbon. The absorption was stronger during the summer than during the winter, with a minimum value of the single scattering albedo (SSA) at 440 nm of 0.86 on 28 May. Low values of asymmetry factor (ASY) (0.65 at 440 nm and 0.58 at 1020 nm) suggest a large number of anthropogenic aerosols, which are absorbing fine-mode particles. The Imaginary part of the Refractive Index (IRI) was higher during the summer than during the winter, indicating there was absorbing aerosol during the summer. These differences in aerosol properties during the summer and winter episodes are discussed in terms of meteorological conditions and transport. The extreme values of PM and AOD were reached during both episodes in conditions with stable atmospheric stratification and low surface wind speed, which are conducive for the accumulation of pollutants. Potential source contribution function (PSCF) and concentration weighted trajectory (CWT) analysis show that fine mode absorbing aerosols dominate during the summer season, mainly due to emissions of local and near-by sources. In the winter, part of the air masses was arriving from arid/semi-arid regions (Shaanxi, Ningxia, Gansu, and Inner Mongolia provinces) covering long distances and transporting coarse particles to the study area, which increased the scattering characteristics of aerosols.

Five-year observation of aerosol optical properties and its radiative effects to planetary boundary layer during air pollution episodes in North China: Intercomparison of a plain site and a mountainous site in Beijing

The aerosol microphysical, optical and radiative properties of the whole column and upper planetary boundary layer (PBL) were investigated during 2013 to 2018 based on long-term sun-photometer observations at a surface site (~106 m a.s.l.) and a mountainous site (~1225 m a.s.l.) in Beijing. Raman-Mie lidar data combined with radiosonde data were used to explore the aerosol radiative effects to PBL during dust and haze episodes. The results showed size distribution exhibited mostly bimodal pattern for the whole column and the upper PBL throughout the year, except in July for the upper PBL, when a trimodal distribution occurred due to the coagulation and hygroscopic growth of fine particles. The seasonal mean values of aerosol optical depth at 440 nm for the upper PBL were 0.31 ± 0.34, 0.30 ± 0.37, 0.17 ± 0.30 and 0.14 ± 0.09 in spring, summer, autumn and winter, respectively. The single-scattering albedo at 440 nm of the upper PBL varied oppositely to that of the whole column, with the monthly mean value between 0.91 and 0.96, indicating weakly to slightly strong absorptive ability at visible spectrum. The monthly mean direct aerosol radiative forcing at the Earth's surface and the top of the atmosphere varied from -40 ± 7 to -105 ± 25 and from -18 ± 4 to -49 ± 17 W m^-2, respectively, and the maximum atmospheric heating was found in summer (~66 ± 12 W m^-2). From a radiative point of view, during dust episode, the presence of mineral dust heated the lower atmosphere, thus promoting vertical turbulence, causing more air pollutants being transported to the upper air by the increasing PBLH. In contrast, during haze episode, a large quantity of absorbing aerosols (such as black carbon) had a cooling effect on the surface and a heating effect on the upper atmosphere, which favored the stabilization of PBL and occurrence of inversion layer, contributing to the depression of the PBLH.

Evaluation of Four Atmospheric Correction Algorithms for GOCI Images over the Yellow Sea

Atmospheric correction (AC) for coastal waters is an important issue in ocean color remote sensing. AC performance is fundamental in retrieving reliable water-leaving radiances and then bio-optical parameters. Unlike polar-orbiting satellites, geostationary ocean color sensors allow high-frequency (15–60 min) monitoring of ocean color over the same area. The first geostationary ocean color sensor, i.e., the Geostationary Ocean Color Imager (GOCI), was launched in 2010. Using GOCI data acquired over the Yellow Sea in summer 2017 at three principal overpass times (02:16, 03:16, 04:16 UTC) with ±1 and ±3 h match-up times, this study compared four GOCI AC algorithms: (1) the standard near infrared (NIR) algorithm of NASA (NASA-STD), (2) the Korea Ocean Satellite Center (KOSC) standard algorithm for GOCI (KOSC-STD), (3) the diffuse attenuation coefficient at 490 nm Kd (490)-based NIR correction algorithm (Kd-based), and (4) the Management Unit of the North Sea Mathematical Models (MUMM). The GOCI-estimated remote sensing reflectance (Rrs), aerosol parameters [aerosol optical thickness (AOT), Angström Exponent (AE)], and chlorophyll-a (Chla) were validated using in situ data. For Rrs, AOT, AE, and Chla, GOCI-retrieved results performed well within the ±1 h temporal window, but the number of match-ups was extended within the ±3 h match-up window. For ±3 h GOCI-derived Rrs, all algorithms had an absolute percentage difference (APD) at 490 and 555 nm of <40%, while other bands showed larger differences (APD > 60%). Compared with in situ values, the APD of the Rrs(490)/Rrs(555) band ratio was <20% for all ACs. For AOT and AE, the APD was >40% and >200%, respectively. Of the four algorithms, the KOSC-STD algorithm demonstrated satisfactory performance in deriving Rrs for the region of interest (Rrs APD: 22.23%–73.95%) in the visible bands. The Kd-based algorithm worked well obtaining Ocean Color 3 GOCI Chla because Rrs(443) is more accurate than the KOSC-STD. The poorest Rrs retrievals were achieved using the NASA-STD and the MUMM algorithms. Statistical analysis indicated that all methods had optimal performance at 04:16 UTC.

Analysis of a Haze Event over Nanjing, China Based on Multi-Source Data

We analyzed a June 2018 Nanjing, China haze event using ground-based and spaceborne sensors, combined with sounding and HYSPLIT backward trajectory data, with the ground-based and spaceborne sensor data exhibiting good consistency. Water vapor content showed significant positive correlation with AOD (aerosol optical depth), and AOD measured in urban and industrial areas was much higher compared to other similar zones. The afternoon convection caused the aerosol uplift during the haze event. Higher aerosol concentration was detected below 2 km. Due to the summer afternoon convective movement, pollutants at high altitude were dominated by small particles, while the overall pollutant mix was dominated by mixed aerosols. During a stable period over June 11–18, a single, near-surface inversion layer, and occasional two inversion layers, stopped pollutant dispersal, with only very stable ocean air mass transport in the southeast direction available. The Air Quality Index drop which took place during June 28–30 was caused by two inversion layers, combined with the immigration of pollutants from inland air masses.

Discrimination of aerosol types over the Tehran city using 5 years (2011-2015) of MODIS collection 6 aerosol products

PURPOSE

Tehran, Iran, is an interesting location for aerosol studies because it is affected by anthropogenic pollution and desert dust aerosols. The aim of this study was to discriminate the aerosol types using satellite data over the city.

METHOD

The study was performed using Level-2 daily Aerosol Optical Depth (AOD) and Ångström Exponent (AE) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments on board the Terra and Aqua satellites for the years 2011 to 2015. As the Deep Blue (DB) AE retrievals are more reliable than the Dark Target (DT) AE retrievals, the study was performed using DB data.

RESULTS

The number of granules with successful retrievals (at least in two pixels with AODs >0.2 over Tehran with high quality assurance) was 200, which indicates that aerosols could be observed in 5.47% (200 from 3652 of Terra and Aqua granules) of the overpasses during the study period. The maximum and minimum values of AOD occurred during May (0.32 ± 0.27) and August (0.18 ± 0.07), respectively. Based on the AOD vs. AE data, aerosols were classified into three different categories: urban/industry (UI), Desert Dust (DD) and Mixed (Mix). To improve the accuracy of the aerosol classification, the analysis was limited to retrievals with AOD values larger than 0.2. The DD, UI and Mix types had 48.5%, 30.5% and 21% contribution in the aerosol days, respectively.

CONCLUSIONS

The maximum DD frequency was observed in the spring and summer seasons, while the UI type had its maximum during the cold season. The AOD of the DD type (over Tehran) correlated well with the AOD observations done at the Aerosol Robotic Network (AERONET) site in Zanjan (300 km northwest from Tehran). For the UI type, no relationship with the AERONET AOD was detected. This gives confidence in our aerosol typing as the contribution of dust in the aerosol load is mainly from long-range transport, whereas the urban aerosols originate from local sources. Back trajectories ending in Tehran show that the northeast and west trajectories are two main transport routes for the dust to the study area.

Himawari-8/AHI and MODIS Aerosol Optical Depths in China: Evaluation and Comparison

The geostationary earth orbit satellite—Himawari-8 loaded with the Advanced Himawari Imager (AHI) has greatly enhanced our capacity of dynamic monitoring in Asia–Pacific area. The Himawari-8/AHI hourly aerosol product is a promising complementary source to the MODerate resolution Imaging Spectroradiometer (MODIS) daily aerosol product for near real-time air pollution observations. However, a comprehensive evaluation of AHI aerosol optical depth (AOD) is still limited, and the difference in performances of AHI and MODIS remains uncertain. In this study, we evaluated the Himawari-8/AHI Level 3 Version 3.0 and MODIS Collection 6.1 Deep Blue AOD products over China against AOD measurements from AErosol RObotic NETwork (AERONET) sites in a spatiotemporal comparison of the products from February 2018 to January 2019. Results showed that AHI AOD achieved a moderate agreement with AERONET with a correlation coefficient of 0.75 and a root-mean-square-error of 0.26, which was slightly inferior to MODIS. The retrieval accuracy was spatially and temporally varied in AHI AOD, with higher accuracies for XiangHe and Lulin sites as well as in the morning and during the summer. The dependency analysis further revealed that the bias in AHI AOD was strongly dependent on aerosol loading and influenced by the Ångström Exponent and NDVI while those for MODIS appeared to be independent of all variables. Fortunately, the biases in AHI AOD could be rectified using a random forest model that contained the appropriate variables to produce sufficiently accurate results with cross-validation R of 0.92 and RMSE of 0.15. With these adjustments, AHI AOD will continue to have great potential in characterizing precise dynamic aerosol variations and air quality at a fine temporal resolution.

Exploring aerosols near clouds with high-spatial-resolution aircraft remote sensing during SEAC4RS

Since aerosols are important to our climate system, we seek to observe the variability of aerosol properties within cloud systems. When applied to the satellite-borne Moderate-resolution Imaging Spectroradiometer (MODIS), the Dark Target (DT) retrieval algorithm provides global aerosol optical depth (AOD at 0.55 μm) in cloud-free scenes. Since MODIS' resolution (500 m pixels, 3 km or 10 km product) is too coarse for studying near-cloud aerosol, we ported the DT algorithm to the high-resolution (~50 m pixels) enhanced-MODIS Airborne Simulator (eMAS), which flew on the high-altitude ER-2 during the Studies of Emissions, Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC4RS) Airborne Science Campaign over the U.S. in 2013. We find that even with aggressive cloud screening, the ~0.5 km eMAS retrievals show enhanced AOD, especially within 6 km of a detected cloud. To determine the cause of the enhanced AOD, we analyze additional eMAS products (cloud retrievals and degraded-resolution AOD), co-registered Cloud Physics Lidar (CPL) profiles, MODIS aerosol retrievals, and ground-based Aerosol Robotic Network (AERONET) observations. We also define spatial metrics to indicate local cloud distributions near each retrieval, and then separate into near-cloud and far-from-cloud environments. The comparisons show that low cloud masking is robust, and unscreened thin cirrus would have only a small impact on retrieved AOD. Some of the enhancement is consistent with clear-cloud transition zone microphysics such as aerosol swelling. However, 3D radiation interaction between clouds and the surrounding clear air appears to be the primary cause of the high AOD near clouds.

Analysis of water vapor effects on aerosol properties and direct radiative forcing in China

The effects of column water vapor (CWV) on aerosol optical properties, radiative effects and classification are studied by using aerosol and CWV data from eight Aerosol Robotic Network (AERONET) sites in China: Beijing, XiangHe, Shouxian, Taihu, Hong_Kong, Zhongshan_Univ, SACOL, and Mt_WLG, which represents 5 distinct aerosol climatologies in China. Contrast in correlations between aerosol optical depth (AOD) and CWV is found. High correlation coefficient (R) ranging from 0.63-0.94 is observed at Beijing and XiangHe (North China Plain), SACOL (Northwest China) and Mt_WLG (the Tibetan Plateau). R values at stations in the Middle-East China (Shouxian and Taihu) are within 0.32-0.45. AOD shows poor correlation to CWV in Southeast China (R at Hong_Kong and Zhongshan_Univ of 0.15 and 0.27). At most sites, the asymmetry (ASYM) of fine-mode aerosol increases with CWV with R larger than ~0.4. Aerosol direct radiative forcing efficiency (ADRFE) at the bottom of the atmosphere (BOA) is affected by CWV, with R >~0.5 over the north and Middle-East China sites. The statistic results show that an increase of CWV by 0.1 cm could result in enhancements of ADREF at the BOA by about 1.1-2.8 W m^-2 at all the sites except Mt_WLG. The aerosol classification shows that the mix-small aerosol type is always dominated under the high CWV air. The clusters of back-trajectories with relative humidity (RH) from Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model indicate that the air mass with high RH is often from south and east directions. The influence of CWV on aerosol properties is mainly shown in the properties of fine mode aerosol, which needs to be considered in the study of aerosol radiative forcing and climate effects.

Dual-wavelength Mie-scattering Scheimpflug lidar system developed for the studies of the aerosol extinction coefficient and the Ångström exponent

A dual-wavelength Scheimpflug lidar system, utilizing a 4-W 808-nm and 1-W 407-nm multimode laser diodes as light sources and two CMOS sensors as detectors, is developed for the studies of the aerosol extinction coefficient and the Ångström exponent. The system performance has been successfully validated by a two-week continuous measurement campaign on a near horizontal path in May 2018 at Dalian, which is a coastal city in Northern China. The aerosol extinction coefficients retrieved by the Fernald method show good correlations with particle concentrations and relative humidities (RHs). It has been found that the enhancement factor of the backscattering coefficient at the short wavelength due to hygroscopic growth is larger than that at the long wavelength for the aerosol particles off the coast of the Yellow Sea. The Ångström exponent obtains from the aerosol extinction coefficients at the two wavelengths, varies between 0 and 2, and is found to relate with the mass concentration fraction of fine mode particles, specifically PM2.5 particles. Moreover, the Ångström exponent has a positive correlation with the RH, implying a bimodal or multimodal size distribution of aerosol particles in the measurement season.

Long-Term Ground-Based Measurements of Aerosol Optical Depth over Kuwait City

We analyze ten years (2008–2017) of ground-based observations of the Aerosol Optical Depth (AOD) in the atmosphere of Kuwait City, in Middle East. The measurements were conducted with a CIMEL sun-sky photometer, at various wavelengths. The daily average AOD at 500 nm (AOD500) is 0.45, while the mean Ångström coefficient (AE), calculated from the pair of wavelengths 440 and 870 nm, is 0.61. The observed high AOD500 values (0.75–2.91), are due to regional sand and dust storm events, which are affecting Kuwait with a mean annual frequency of almost 20 days/year. The long-term record analysis of AOD500 and AE, shows a downward and upward tendency respectively, something which could be attributed to the continuous expansion and industrialization of the main city of Kuwait, in combination with the simultaneous increase of soil moisture over the area. By utilizing back trajectories of air masses for up to 4 days, we assessed the influence of various regions to the aerosol load over Kuwait. The high aerosol loads during spring, are attributed to the dominance of coarse particles from Saudi Arabia (AOD500 0.56–0.74), a source area that contributes the 56% to the mean annual AOD500. Other dust sources affecting significantly Kuwait originated from the regions of Iraq and Iran with contribution of 21%.

Elevated Black Carbon Concentrations and Atmospheric Pollution around Singrauli Coal-Fired Thermal Power Plants (India) Using Ground and Satellite Data

The tropospheric NO₂ concentration from OMI AURA always shows high concentrations of NO₂ at a few locations in India, one of the high concentrations of NO₂ hotspots is associated with the locations of seven coal-fired Thermal Power plants (TPPs) in Singrauli. Emissions from TPPs are among the major sources of black carbon (BC) soot in the atmosphere. Knowledge of BC emissions from TPPs is important in characterizing regional carbonaceous particulate emissions, understanding the fog/haze/smog formation, evaluating regional climate forcing, modeling aerosol optical parameters and concentrations of black carbon, and evaluating human health. Furthermore, elevated BC concentrations, over the Indo-Gangetic Plain (IGP) and the Himalayan foothills, have emerged as an important subject to estimate the effects of deposition and atmospheric warming of BC on the accelerated melting of snow and glaciers in the Himalaya. For the first time, this study reports BC concentrations and aerosol optical parameters near dense coal-fired power plants and open cast coal mining adjacent to the east IGP. In-situ measurements were carried out in Singrauli (located in south-east IGP) at a fixed site about 10 km from power plants and in transit measurements in close proximity to the plants, for few days in the month of January and March 2013. At the fixed site, BC concentration up to the 95 μgm⁻³ is observed with strong diurnal variations. BC concentration shows two maxima peaks during early morning and evening hours. High BC concentrations are observed in close proximity to the coal-fired TPPs (>200 μgm⁻³), compared to the outside domain of our study region. Co-located ground-based sunphotometer measurements of aerosol optical depth (AOD) show strong spatial variability at the fixed site, with AOD in the range 0.38–0.58, and the highest AOD in the range 0.7–0.95 near the TPPs in transit measurements (similar to the peak of BC concentrations). Additionally, the Angstrom exponent was found to be in the range 0.4–1.0 (maximum in the morning time) and highest in the proximity of TPPs (~1.0), suggesting abundance of fine particulates, whereas there was low Angstrom exponent over the surrounding coal mining areas. Low Angstrom exponent is characterized by dust from the unpaved roads and nearby coal mining areas. MODIS derived daily AOD shows a good match with the MICROTOPS AOD. The CALIPSO derived subtypes of the aerosol plot shows that the aerosols over Singrauli region are mainly dust, polluted dust, and elevated smoke. The preliminary study for few days provides information about the BC concentrations and aerosol optical properties from Singrauli (one of the NO₂ hotspot locations in India). This preliminary study suggests that long-term continuous monitoring of BC is needed to understand the BC concentrations and aerosol optical properties for better quantification and the estimation of the emission to evaluate radiative forcing in the region.

Impact of snow-darkening by deposition of light-absorbing aerosols on snow cover in the Himalaya-Tibetan-Plateau and influence on the Asian Summer monsoon: A possible mechanism for the Blanford Hypothesis

The impact of snow darkening by deposition of light absorbing aerosols (LAAs) on snow cover over the Himalaya-Tibetan-Plateau (HTP) and influence on the Asian summer monsoon are investigated using the NASA Goddard Earth Observing System Model Version 5 (GEOS-5). We find that during April-May-June, deposition of LAAs on snow leads to a reduction in surface albedo, initiating a sequence of feedback processes, starting with increased net surface solar radiation, rapid snowmelt in HTP and warming of the surface and upper troposphere, followed by enhanced low-level southwesterlies and increased dust loading over the Himalayas-Indo-Gangetic Plain. The warming is amplified by increased dust aerosol heating, and subsequently amplified by latent heating from enhanced precipitation over the Himalaya foothills and northern India, via the Elevated Heat Pump (EHP) effect during June-July-August. The reduced snow cover in the HTP anchors the enhanced heating over the Tibetan Plateau and its southern slopes, in conjunction with an enhancement of the Tibetan Anticyclone, and the development of an anomalous Rossby wavetrain over East Asia, leading to weakening of the subtropical westerly jet, and northward displacement and intensification of the Mei-Yu rainbelt. Our results suggest that atmosphere-land heating induced by LAAs, particularly desert dust play a fundamental role in physical processes underpinning the snow-monsoon relationship proposed by Blanford more than a century ago.

Development of a Regression Model for Estimating Daily Radiative Forcing Due to Atmospheric Aerosols from Moderate Resolution Imaging Spectrometers (MODIS) Data in the Indo Gangetic Plain (IGP)

The assessment of direct radiative forcing due to atmospheric aerosols (ADRF) in the Indo Gangetic Plain (IGP), which is a food basket of south Asia, is important for measuring the effect of atmospheric aerosols on the terrestrial ecosystem and for assessing the effect of aerosols on crop production in the region. Existing comprehensive analytical models to estimate ADRF require a large number of input parameters and high processing time. In this context, here, we develop a simple model to estimate daily ADRF at any location on the surface of the IGP through multiple regressions of AErosol RObotic NETwork (AERONET) aerosol optical depth (AOD) and atmospheric water vapour using data from 2002 to 2015 at 10 stations in the IGP. The goodness of fit of the model is indicated by an adjusted R2 value of 0.834. The Jackknife method of deleting one group (station data) was employed to cross validate and study the stability of the regression model. It was found to be robust with an adjusted R2 fluctuating between 0.813 and 0.842. In order to use the year-round ADRF model for locations beyond the AERONET stations in the IGP, AOD, and atmospheric water vapour products from MODIS Aqua and Terra were compared against AERONET station data and they were found to be similar. Using MODIS Aqua and Terra products as input, the year-round ADRF regression was evaluated at the IGP AERONET stations and found to perform well with Pearson correlation coefficients of 0.66 and 0.65, respectively. Using ADRF regression model with MODIS inputs allows for the estimation of ADRF across the IGP for assessing the aerosol impact on ecosystem and crop production.

On the minimization of adjacency effects in SeaWiFS primary data products from coastal areas

The minimization of adjacency effects (AE) in SeaWiFS primary products at the Aqua Alta Oceanographic Tower (AAOT) was investigated using sample images concurrent with in situ measurements. The validation exercise was performed with the NASA SeaDAS processing scheme ingesting original SeaWiFS data and alternatively SeaWiFS top-of-atmosphere data corrected for AE, and additionally including and excluding the default turbid water (TW) correction algorithm. Results show overestimates of the TW contributions partially compensating for AE. The analysis also suggests that intra-annual biases observed in SeaWiFS radiometric products at the AAOT may result from a misinterpretation of the NIR atmospheric signal as water contribution in data acquired in winter, and from uncompensated AE in data acquired in summer.

Atmospheric Dust, Early Cases, and Localized Meningitis Epidemics in the African Meningitis Belt: An Analysis Using High Spatial Resolution Data

BACKGROUND

Bacterial meningitis causes a high burden of disease in the African meningitis belt, with regular seasonal hyperendemicity and sporadic short, but intense, localized epidemics during the late dry season occurring at a small spatial scale [i.e., below the district level, in individual health centers (HCs)]. In addition, epidemic waves with larger geographic extent occur every 7-10 y. Although atmospheric dust load is thought to be an essential factor for hyperendemicity, its role for localized epidemics remains hypothetic.

OBJECTIVES

Our goal was to evaluate the association of localized meningitis epidemics in HC catchment areas with the dust load and the occurrence of cases in the same population early in the dry season.

METHODS

We compiled weekly reported cases of suspected bacterial meningitis at the HC resolution for 14 districts of Burkina Faso for the period 2004-2014. Using logistic regression, we evaluated the association of epidemic HC-weeks with atmospheric dust [approximated by the aerosol optical thickness (AOT) satellite product] and with the observation of early meningitis cases during October-December.

RESULTS

Although AOT was strongly associated with epidemic HC-weeks in crude analyses across all HC-weeks during the meningitis season [odds ratio (OR) [Formula: see text]; 95% CI: 4.90, 9.50], the association was no longer apparent when controlling for calendar week (OR [Formula: see text]; 95% CI: 0.60, 1.50). The number of early meningitis cases reported during October-December was associated with epidemic HC-weeks in the same HC catchment area during January-May of the following year (OR for each additional early case [Formula: see text]; 95% CI: 1.06, 1.21).

CONCLUSIONS

Spatial variations of atmospheric dust load do not seem to be a factor in the occurrence of localized meningitis epidemics, and the factor triggering them remains to be identified. The pathophysiological mechanism linking early cases to localized epidemics is not understood, but their occurrence and number of early cases could be an indicator for epidemic risk. https://doi.org/10.1289/EHP2752.

Aerosol columnar characteristics and their heterogeneous nature over Varanasi, in the central Ganges valley

The Indo-Gangetic Basin (IGB) experiences one of the highest aerosol loading over the globe with pronounced inter-/intra-seasonal variability. Four-year (January 2011-December 2014) continuous MICROTOPS-II sun-photometer measurements at Varanasi, central Ganges valley, provide an opportunity to investigate the aerosol physical and optical properties and their variability. A large variation in aerosol optical depth (AOD: from 0.23 to 1.89, mean of 0.82 ± 0.31) and Ångström exponent (AE: from 0.19 to 1.44, mean of 0.96 ± 0.27) is observed, indicating a highly turbid atmospheric environment with significant heterogeneity in aerosol sources, types and optical properties. The highest seasonal means of both AOD and AE are observed in the post-monsoon (October-November) season (0.95 ± 0.31 for AOD and 1.16 ± 0.14 for AE) followed by winter (December, January, February; 0.97 ± 0.34 for AOD and 1.09 ± 0.20 for AE) and are mainly attributed to the accumulation of aerosols from urban and biomass/crop residue burning emissions within a shallow boundary layer. In contrast, during the pre-monsoon and monsoon seasons, the aerosols are mostly coming from natural origin (desert and mineral dust) mixed with pollution in several cases. The spectral dependence of AE, the aerosol "curvature" effect and other graphical techniques are used for the identification of the aerosol types and their mixing processes in the atmosphere. Furthermore, the aerosol source-apportionment assessment using the weighted potential source contribution function (WPSCF) analysis reveals the different aerosol types, emission sources and transport pathways.

Investigation on spatiotemporal distribution of aerosol optical properties over two oceanic regions surrounding Indian subcontinent during summer monsoon season

Columnar spectral aerosol optical depths (AODs) and total suspended particulate matter (TSPM) concentrations were collected on board the Oceanographic Research Vessel (ORV) of Sagar Kanya (SK) during 7-21 June 2014 (SK-313) and 31 July-14 August 2015 (SK-323) over the Arabian Sea (AS) and Bay of Bengal (BoB), respectively, for the two successive years during summer monsoon season. AOD measured at 500 nm (AOD₅₀₀) varied significantly from 0.08 to 0.66 (0.07 to 0.60), with a mean of 0.48 ± 0.13 (0.34 ± 0.13) over the BoB (AS) during SK-313 (SK-323). It simply implies that aerosol load was higher over BoB, not variability as the standard deviations of AOD over both oceans are identical (0.13). Daily AOD₅₀₀ ranged between 0.15 and 0.60 accounted for 70-75% of the total occurrences over two oceanic regions. Mean Ångström exponent (α or alpha) and Ångström turbidity coefficient (β or beta) were found to be 0.43 ± 0.17 (0.39 ± 0.19) and 0.37 ± 0.15 (0.27 ± 0.13), respectively, which are higher over the AS during SK-323 (SK-313) that indicate predominance of coarse-relative to fine-mode particles. On the other hand, the spectral curvature and second derivative of alpha (α') also showed significant contribution of coarse-mode particles over fine during the two campaigns. Further, column aerosol size distribution (CSD) derived from the King's inversion also exhibited bimodal distribution with a predominant peak observed in the coarse mode (~1.0 μm) compared to the fine mode at a geometric mean radius at ~0.1 μm over two oceans. The observed data showed that the two marine regions are significantly influenced by various types of aerosols with a predominance of mixed type (MT) of aerosols. From the morphological study, it is inferred that the particles are a flake, spherical, irregular, and in flower and aggregated shapes conducted for the TSPM samples collected during SK-323 over the AS. Finally, the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model is used to study the impact of long-distance transported aerosols and identify their sources.

An Evaluation of MODIS-Retrieved Aerosol Optical Depth over AERONET Sites in Alaska

The air quality monitoring network in Alaska is currently limited to ground-based observations in urban areas and national parks, leaving a large proportion of the state unmonitored. The use of Moderate Resolution Imaging Spectroradiometer MODIS aerosol optical depth (AOD) to estimate ground-level particulate pollution concentrations has been successfully demonstrated around the world and could potentially be used in Alaska. In this work, MODIS AOD measurements at 550 nm were validated against AOD derived from two ground-based Aerosol Robotic Network (AERONET) sunphotometers in Alaska, located at Utqiagvik (previously known as Barrow) and Bonanza Creek, to determine if MODIS AOD from the Terra and Aqua satellites could be used to estimate ground-level particulate pollution concentrations. The MODIS AOD was obtained from MODIS collection 6 using the dark target Land and Ocean algorithms from years 2000 to 2014. MODIS data could only be obtained between the months of April and October; therefore, it was only evaluated for those months. Individual and combined Terra and Aqua MODIS data were considered. The results showed that MODIS collection 6 products at 10-km resolution for Terra and Aqua combined are not valid over land but are valid over the ocean. Note that the individual Terra and Aqua MODIS collection 6 AOD products at 10-km resolution are valid over land individually but not when combined. Results also suggest the MODIS collection 6 AOD products at 3-km resolution are valid over land and ocean and perform better over land than the 10-km product. These findings indicate that MODIS collection 6 AOD products can be used quantitatively in air quality applications in Alaska during the summer months.

Using Multi-Angle Imaging SpectroRadiometer Aerosol Mixture Properties for Air Quality Assessment in Mongolia

Ulaanbaatar (UB), the capital city of Mongolia, has extremely poor wintertime air quality with fine particulate matter concentrations frequently exceeding 500 μg/m3, over 20 times the daily maximum guideline set by the World Health Organization. Intensive use of sulfur-rich coal for heating and cooking coupled with an atmospheric inversion amplified by the mid-continental Siberian anticyclone drive these high levels of air pollution. Ground-based air quality monitoring in Mongolia is sparse, making use of satellite observations of aerosol optical depth (AOD) instrumental for characterizing air pollution in the region. We harnessed data from the Multi-angle Imaging SpectroRadiometer (MISR) Version 23 (V23) aerosol product, which provides total column AOD and component-particle optical properties for 74 different aerosol mixtures at 4.4 km spatial resolution globally. To test the performance of the V23 product over Mongolia, we compared values of MISR AOD with spatially and temporally matched AOD from the Dalanzadgad AERONET site and find good agreement (correlation r = 0.845, and root-mean-square deviation RMSD = 0.071). Over UB, exploratory principal component analysis indicates that the 74 MISR AOD mixture profiles consisted primarily of small, spherical, non-absorbing aerosols in the wintertime, and contributions from medium and large dust particles in the summertime. Comparing several machine learning methods for relating the 74 MISR mixtures to ground-level pollutants, including particulate matter with aerodynamic diameters smaller than 2.5 μm ( PM 2.5 ) and 10 μm ( PM 10 ), as well as sulfur dioxide ( SO 2 ), a proxy for sulfate particles, we find that Support Vector Machine regression consistently has the highest predictive performance with median test R 2 for PM 2.5 , PM 10 , and SO 2 equal to 0.461, 0.063, and 0.508, respectively. These results indicate that the high-dimensional MISR AOD mixture set can provide reliable predictions of air pollution and can distinguish dominant particle types in the UB region.

Radiative response of biomass-burning aerosols over an urban atmosphere in northern peninsular Southeast Asia

A large concentration of finer particulate matter (PM_2.5), the primary air-quality concern in northern peninsular Southeast Asia (PSEA), is believed to be closely related to large amounts of biomass burning (BB) particularly in the dry season. In order to quantitatively estimate the contributions of BB to aerosol radiative effects, we thoroughly investigated the physical, chemical, and optical properties of BB aerosols through the integration of ground-based measurements, satellite retrievals, and modelling tools during the Seven South East Asian Studies/Biomass-burning Aerosols & Stratocumulus Environment: Lifecycles & Interactions Experiment (7-SEAS/BASELInE) campaign in 2014. Clusters were made on the basis of measured BB tracers (Levoglucosan, nss-K⁺, and NO₃^-) to classify the degree of influence from BB over an urban atmosphere, viz., Chiang Mai (18.795°N, 98.957°E, 354m.s.l.), Thailand in northern PSEA. Cluster-wise contributions of BB to PM_2.5, organic carbon, and elemental carbon were found to be 54-79%, 42-79%, and 39-77%, respectively. Moreover, the cluster-wise aerosol optical index (aerosol optical depth at 500nm≈0.98-2.45), absorption (single scattering albedo ≈0.87-0.85; absorption aerosol optical depth ≈0.15-0.38 at 440nm; absorption Ångström exponent ≈1.43-1.57), and radiative impacts (atmospheric heating rate ≈1.4-3.6Kd^-1) displayed consistency with the degree of BB. PM_2.5 during Extreme BB (EBB) was ≈4 times higher than during Low BB (LBB), whereas this factor was ≈2.5 for the magnitude of radiative effects. Severe haze (visibility≈4km) due to substantial BB loadings (BB to PM_2.5≈79%) with favorable meteorology can significantly impact the local-to-regional air quality and the, daily life of local inhabitants as well as become a respiratory health threat. Additionally, such enhancements in atmospheric heating could potentially influence the regional hydrological cycle and crop productivity over Chiang Mai in northern PSEA.

Intra-annual variations of regional aerosol optical depth, vertical distribution, and particle types from multiple satellite and ground-based observational datasets

The climatic and health effects of aerosols are strongly dependent on the intra-annual variations in their loading and properties. While the seasonal variations of regional aerosol optical depth (AOD) have been extensively studied, understanding the temporal variations in aerosol vertical distribution and particle types is also important for an accurate estimate of aerosol climatic effects. In this paper, we combine the observations from four satellite-borne sensors and several ground-based networks to investigate the seasonal variations of aerosol column loading, vertical distribution, and particle types over three populous regions: the Eastern United States (EUS), Western Europe (WEU), and Eastern and Central China (ECC). In all three regions, column AOD, as well as AOD at heights above 800 m, peaks in summer/spring, probably due to accelerated formation of secondary aerosols and hygroscopic growth. In contrast, AOD below 800m peaks in winter over WEU and ECC regions because more aerosols are confined to lower heights due to the weaker vertical mixing. In the EUS region, AOD below 800m shows two maximums, one in summer and the other in winter. The temporal trends in low-level AOD are consistent with those in surface fine particle (PM2.5) concentrations. AOD due to fine particles (< 0.7 μm diameter) is much larger in spring/summer than in winter over all three regions. However, the coarse mode AOD (> 1.4 μm diameter), generally shows small variability, except that a peak occurs in spring in the ECC region due to the prevalence of airborne dust during this season. When aerosols are classified according to sources, the dominant type is associated with anthropogenic air pollution, which has a similar seasonal pattern as total AOD. Dust and sea-spray aerosols in the WEU region peak in summer and winter, respectively, but do not show an obvious seasonal pattern in the EUS region. Smoke aerosols, as well as absorbing aerosols, present an obvious unimodal distribution with a maximum occurring in summer over the EUS and WEU regions, whereas they follow a bimodal distribution with peaks in August and March (due to crop residue burning) over the ECC region.

Discerning the pre-monsoon urban atmosphere aerosol characteristic and its potential source type remotely sensed by AERONET over the Bengal Gangetic plain

In the present study, we evaluated the pre-monsoon urban atmosphere (UA) aerosol characteristics remotely sensed by Aerosol Robotic Network (AERONET) over the Bengal Gangetic plain (BGP) at Kolkata (KOL) and their implication in potential source types and spatiotemporal features. About 70% of the AERONET-sensed aerosol optical depth at 0.50 μ m, AOD_0.5 (Angstrom exponent, α at 0.44-0.87 μ m) during the pre-monsoon period (February to June) was greater than 0.50 (≤ 1); the pre-monsoon mean of AOD_0.5 (α) was 0.73 (0.83) which was found being slightly higher (lower) than nearby AERONET stations (Dhaka/Bhola) located over the eastern Ganges basin. The volume geometric mean radius for the fine mode (FM) (coarse mode, CM) UA aerosol from AERONET retrievals was estimated to be 0.14-0.17 (2.24-2.75) μ m. The spectral distribution of the monthly mean of UA aerosol single-scattering albedo (SSA) exhibited an increasing trend with an increase in wavelength throughout all wavelengths during April, unlike the rest of the pre-monsoon months. Investigation of aerosol types indicated the pre-dominance of dust during April and a mixture of urban/open burning with mixed desert dust during the rest of the pre-monsoon months. Potential aerosol source fields were identified over the Indo-Gangetic Plain (IGP), east coast, northwestern India, and oceanic regions; these were estimated at elevated layers of atmosphere during April and May but that at surface layers during February and June. Comparison of aerosol characteristics over the BGP (at Kolkata, KOL) with that at six other coincident AERONET sites over India revealed mean AOD at KOL being 11 to 91% higher than the rest of the AERONET stations, with the relative increase at KOL being the highest during March; this was attributed to persistent high values of both FM and CM AOD unlike the rest of the stations. The monthly mean of SSA was the lowest at KOL among AERONET stations, during February and March. Comparison of the AOD from the AERONET aerosol retrievals over the BGP UA with the coincident Moderate Resolution Imaging Spectroradiometer (MODIS) latest retrievals (C005 and C006) indicated a moderate correlation between the two retrievals; discrepancy in MODIS-retrieved relative distribution of FM and CM AOD was inferred compared to AERONET in the UA.

Constraining chemical transport PM2.5 modeling outputs using surface monitor measurements and satellite retrievals: application over the San Joaquin Valley

Advances in satellite retrieval of aerosol type can improve the accuracy of near-surface air quality characterization by providing broad regional context and decreasing metric uncertainties and errors. The frequent, spatially extensive and radiometrically consistent instantaneous constraints can be especially useful in areas away from ground monitors and progressively downwind of emission sources. We present a physical approach to constraining regional-scale estimates of PM2.5, its major chemical component species estimates, and related uncertainty estimates of chemical transport model (CTM; e.g., the Community Multi-scale Air Quality Model) outputs. This approach uses ground-based monitors where available, combined with aerosol optical depth and qualitative constraints on aerosol size, shape, and light-absorption properties from the Multi-angle Imaging SpectroRadiometer (MISR) on the NASA Earth Observing System's Terra satellite. The CTM complements these data by providing complete spatial and temporal coverage. Unlike widely used approaches that train statistical regression models, the technique developed here leverages CTM physical constraints such as the conservation of aerosol mass and meteorological consistency, independent of observations. The CTM also aids in identifying relationships between observed species concentrations and emission sources. Aerosol air mass types over populated regions of central California are characterized using satellite data acquired during the 2013 San Joaquin field deployment of the NASA Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) project. We investigate the optimal application of incorporating 275 m horizontal-resolution aerosol air-mass-type maps and total-column aerosol optical depth from the MISR Research Aerosol retrieval algorithm (RA) into regional-scale CTM output. The impact on surface PM2.5 fields progressively downwind of large single sources is evaluated using contemporaneous surface observations. Spatiotemporal R 2 and RMSE values for the model, constrained by both satellite and surface monitor measurements based on 10-fold cross-validation, are 0.79 and 0.33 for PM2.5, 0.88 and 0.65 for NO3 -, 0.78 and 0.23 for SO4 2-, and 1.01 for NH+, 0.73 and 0.23 for OC, and 0.31 and 0.65 for EC, respectively. Regional cross-validation temporal and spatiotemporal R2 results for the satellite-based PM2.5 improve by 30 % and 13 %, respectively, in comparison to unconstrained CTM simulations and provide finer spatial resolution. SO4 2- cross-validation values showed the largest spatial and spatiotemporal R2 improvement, with a 43 % increase. Assessing this physical technique in a well- instrumented region opens the possibility of applying it globally, especially over areas where surface air quality measurements are scarce or entirely absent.

Comparison of AOD from CALIPSO, MODIS, and Sun Photometer under Different Conditions over Central China

Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) provides three-dimensional information on aerosol optical properties across the globe. However, the performance of CALIPSO aerosol optical depth (AOD) products under different air quality conditions remains unclear. In this research, three years of CALIPSO level 2 AOD data (November 2013 to December 2017) were employed to compare with the Moderate Resolution Imaging Spectroradiometer (MODIS) level 2 columnar AOD products and ground-based sun photometer measurements for the same time period. To investigate the effect of air quality on AODs retrieved from CALIPSO, the AODs obtained from CALIPSO, MODIS, and sun photometer were inter-compared under different air quality conditions over Wuhan and Dengfeng. The average absolute bias of AOD between CALIPSO and sun photometer was 0.22 ± 0.21, 0.11 ± 0.07, and 0.14 ± 0.13 under clean, moderate, and polluted weather, respectively. The result indicates that the CALIPSO AOD were more reliable under moderate and polluted days. Moreover, the deviation of AOD between CALIPSO and sun photometer was largest (0.23 ± 0.21) in the autumn season, and lowest (0.13 ± 0.12) in the winter season. The results show that CALIPSO AOD products were more applicable to regions and seasons with high aerosol concentrations.

Observations of the Interaction and Transport of Fine Mode Aerosols with Cloud and/or Fog in Northeast Asia from Aerosol Robotic Network (AERONET) and Satellite Remote Sensing

Analysis of sun photometer measured and satellite retrieved aerosol optical depth (AOD) data has shown that major aerosol pollution events with very high fine mode AOD (>1.0 in mid-visible) in the China/Korea/Japan region are often observed to be associated with significant cloud cover. This makes remote sensing of these events difficult even for high temporal resolution sun photometer measurements. Possible physical mechanisms for these events that have high AOD include a combination of aerosol humidification, cloud processing, and meteorological co-variation with atmospheric stability and convergence. The new development of Aerosol Robotic network (AERONET) Version 3 Level 2 AOD with improved cloud screening algorithms now allow for unprecedented ability to monitor these extreme fine mode pollution events. Further, the Spectral Deconvolution Algorithm (SDA) applied to Level 1 data (L1; no cloud screening) provides an even more comprehensive assessment of fine mode AOD than L2 in current and previous data versions. Studying the 2012 winter-summer period, comparisons of AERONET L1 SDA daily average fine mode AOD data showed that Moderate Resolution Imaging Spectroradiometer (MODIS) satellite remote sensing of AOD often did not retrieve and/or identify some of the highest fine mode AOD events in this region. Also, compared to models that include data assimilation of satellite retrieved AOD, the L1 SDA fine mode AOD was significantly higher in magnitude, particularly for the highest AOD events that were often associated with significant cloudiness.

Lidar Measurements of Dust Aerosols during Three Field Campaigns in 2010, 2011 and 2012 over Northwestern China

Ground-based measurements were carried out during field campaigns in April–June of 2010, 2011 and 2012 over northwestern China at Minqin, the Semi-Arid Climate and Environment Observatory of Lanzhou University (SACOL) and Dunhuang. In this study, three dust cases were examined, and the statistical results of dust occurrence, along with physical and optical properties, were analyzed. The results show that both lofted dust layers and near-surface dust layers were characterized by extinction coefficients of 0.25–1.05 km−1 and high particle depolarization ratios (PDRs) of 0.25–0.40 at 527 nm wavelength. During the three campaigns, the frequencies of dust occurrence retrieved from the lidar observations were all higher than 88%, and the highest frequency was in April. The vertical distributions revealed that the maximum height of dust layers typically reached 7.8–9 km or higher. The high intensity of dust layers mostly occurred within the planetary boundary layer (PBL). The monthly averaged PDRs decreased from April to June, which implies a dust load reduction. A comparison of the relationship between the aerosol optical depth at 500 nm (AOD500) and the Angstrom exponent at 440–870 nm (AE440–870) confirms that there is a more complex mixture of dust aerosols with other types of aerosols when the effects of human activities become significant.

Satellite Ocean Aerosol Retrieval (SOAR) algorithm extension to S-NPP VIIRS as part of the 'Deep Blue' aerosol project

The Suomi National Polar-Orbiting Partnership (S-NPP) satellite, launched in late 2011, carries the Visible Infrared Imaging Radiometer Suite (VIIRS) and several other instruments. VIIRS has similar characteristics to prior satellite sensors used for aerosol optical depth (AOD) retrieval, allowing the continuation of space-based aerosol data records. The Deep Blue algorithm has previously been applied to retrieve AOD from Sea-viewing Wide Field-of-view Sensor (SeaWiFS) and Moderate Resolution Imaging Spectro-radiometer (MODIS) measurements over land. The SeaWiFS Deep Blue data set also included a SeaWiFS Ocean Aerosol Retrieval (SOAR) algorithm to cover water surfaces. As part of NASA's VIIRS data processing, Deep Blue is being applied to VIIRS data over land, and SOAR has been adapted from SeaWiFS to VIIRS for use over water surfaces. This study describes SOAR as applied in version 1 of NASA's S-NPP VIIRS Deep Blue data product suite. Several advances have been made since the SeaWiFS application, as well as changes to make use of the broader spectral range of VIIRS. A preliminary validation against Maritime Aerosol Network (MAN) measurements suggests a typical uncertainty on retrieved 550nm AOD of order ±(0.03+10%), comparable to existing SeaWiFS/MODIS aerosol data products. Retrieved Ångström exponent and fine mode AOD fraction are also well-correlated with MAN data, with small biases and uncertainty similar to or better than SeaWiFS/MODIS products.