Monthly Mean Aerosol Optical Depth Research Articles

A study on optical properties, classification, and transport of aerosols during the smog period over South Asia using remote sensing.

Over the past few years, South Asian region has experienced frequent and thick smog events because of rapid population growth and enhanced anthropogenic activities, particularly in the Indo-Gangetic Plain (IGP). Therefore, the present study investigates aerosol properties such as aerosol optical depth (AOD) (500nm), Angstrom exponent (AE) (440-870nm), single scattering albedo (SSA), fine-mode fraction (FMF), absorption aerosol optical depth (AAOD), and absorption aerosol exponent (AAE) over selected AERONET sites namely Bhola (2012-2021), Dhaka (2012-2021), Jaipur (2011-2021), Kanpur (2011-2021), Karachi (2011-2021), Lahore (2011-2021), and Pokhara (2011-2021) in the IGP during the smog period (October, November, and December). Additionally, different aerosol types were categorized using AERONET direct sun (AOD, AE) and inversion products (VSD, SSA, RI, FMF, and ASY). The monthly mean AOD, AE, and FMF varied from ⁓0.33 to 1.07, ⁓0.3 to 1.4, and 0.6-0.9µm over all selected AERONET sites during the smog period. Moreover, the outcomes revealed the dominance of biomass-burning and urban/ industrial aerosols over Lahore, Karachi, Dhaka, and Bhola during the smog period. Contrary to this, dust and mixed aerosols were abundant over Jaipur and Karachi, respectively. Furthermore, HYSPLIT cluster analysis is used to trace the transmission paths and potential sources of aerosols over selected sites.

Retrieval and validation of long-term aerosol optical depth from AVHRR data over China

ABSTRACT Advanced Very High Resolution Radiometer (AVHRR) onboard National Oceanic and Atmospheric Administration (NOAA) satellites can provide over 40 years of global remote sensing observations, which can be used to retrieve long-term aerosol optical depth (AOD). This is of great significance to the study of global climate change. In this paper, we proposed an algorithm to jointly calculate AOD and land surface properties from AVHRR observations. With assumptions that AOD doesn’t vary in adjacent space and earth surface property doesn’t vary in two days, the algorithm considered non-Lambertian surface reflection based on the shape of bidirectional reflectance distribution function (BRDF shape) and obtained AOD and surface property by optimal estimation (OE) method. The algorithm has been applied to NOAA-7, 9, 11, 14, 16, 18, and 19 satellites and AVHRR-retrieved AOD with 5 × 10 km over China (15°–60° N, 70°–140°E) has been obtained from 1982 to 2016. Comparisons of AVHRR-retrieved AOD against AErosol RObotic NETwork (AERONET) (in and around China) and China Aerosol Remote Sensing Network (CARSNET) AOD show good consistency with 62.62% points within the uncertainty of Δτ = ± (0.05 + 0.25τ) and root-mean-square error (RMSE) of 0.26. Further comparison of the monthly mean AOD of multiple AOD datasets in the ‘Beijing’, ‘Dalanzadgad’, ‘NCU_Taiwan’ and ‘Kanpur’ stations shows that the results of the algorithm are stable. The yearly averaged AOD data also has similar agreements with MERRA-2 (The Modern-Era Retrospective analysis for Research and Applications, Version 2) and AVHRRDB data (AVHRR ‘Deep Blue’ aerosol data set). The multi-year mean correlation coefficient is 0.70 and 0.61 and the percentages within the uncertainty are 80.01% and 67.29% compared with MERRA-2 AOD and AVHRRDB AOD respectively.

Features of the Extreme Fire Season of 2021 in Yakutia (Eastern Siberia) and Heavy Air Pollution Caused by Biomass Burning

Yakutia (Eastern Siberia) is one of the most fire-prone regions of Russia, which is frequently affected by large-scale wildfires despite a relatively short warm period, which usually lasts from May to September. In 2021, Yakutia experienced the worst fire season over the last four decades. In this study, we investigate features of the extreme fire season, factors that promote extreme fire weather, and heavy air pollution caused by biomass burning in the region utilizing multiple satellite and ground-based observations along with reanalysis data and forward-trajectory modelling. The results demonstrate that the total number of hotspots (HS) in 2021 amounted to ~150,000, which is almost twice as much as the previous record year (2020). One of the main features of the 2021 fire season was the period of extensive growth of the number of HS, which occurred from 24 July to 12 August. High fire danger during the fire season was promoted by positive anomalies in monthly air temperature (August) and negative anomalies in monthly precipitation (May–July). August of 2021 in central Yakutia was the second most hot August (14.9 °C) during a 43-year NCEP-DOE Reanalysis record (1979–2021). In addition, the intensification of wildfires during August 2021 was associated with persistent high-pressure systems, which promoted dry weather conditions in the region by blocking the transport of moist air masses from the western part of Russia. The low wind speeds, observed in the center of a high-pressure system, led to the accumulation of wildfire emissions in the atmosphere, which significantly affect air quality in the region. The monthly mean aerosol optical depth values in July 2021 were 0.82 (MODIS MAIAC) and 1.37 (AERONET) which were 14.9 and 18.7 times higher than respective values of 2007 (the year with minimal wildfires in the Asian part of Russia and Yakutia). Based on aerosol index observations and forward trajectories, we demonstrate that smoke plumes originated from the study area were transported over long distances reaching the Ural Mountains in the west, Mongolia in the south, the North Pole in the north, and Alaska in the east, traveling the distances of ~2000–7000 km. Maximum spatial extent of the smoke plumes reached ~10–12 mln. km2.

An Evaluation of Two Decades of Aerosol Optical Depth Retrievals from MODIS over Australia

We present an evaluation of Aerosol Optical Depth (AOD) retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS) over Australia covering the period 2001–2020. We focus on retrievals from the Deep Blue (DB) and Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithms, showing how these compare to one another in time and space. We further employ speciated AOD estimates from Copernicus Atmospheric Monitoring Service (CAMS) reanalyses to help diagnose aerosol types and hence sources. Considering Australia as a whole, monthly mean AODs show similar temporal behaviour, with a well-defined seasonal peak in the Austral summer. However, excepting periods of intense biomass burning activity, MAIAC values are systematically higher than their DB counterparts by, on average, 50%. Decomposing into seasonal maps, the patterns of behaviour show distinct differences, with DB showing a larger dynamic range in AOD, with markedly higher AODs (ΔAOD∼0.1) in northern and southeastern regions during Austral winter and summer. This is counter-balanced by typically smaller DB values across the Australian interior. Site level comparisons with all available level 2 AOD data from Australian Aerosol Robotic Network (AERONET) sites operational during the study period show that MAIAC tends to marginally outperform DB in terms of correlation (RMAIAC = 0.71, RDB = 0.65) and root-mean-square error (RMSEMAIAC = 0.065, RMSEDB = 0.072). To probe this behaviour further, we classify the sites according to the predominant surface type within a 25 km radius. This analysis shows that MAIAC’s advantage is retained across all surface types for R and all but one for RMSE. For this surface type (Bare, comprising just 1.2% of Australia) the performance of both algorithms is relatively poor, (RMAIAC = 0.403, RDB = 0.332).

Analysis of aerosol cloud interactions with a consistent signal of meteorology and other influencing parameters

Quantifying the impact of aerosols on cloud micro/macro physical properties and estimating the signature of Aerosol Cloud Interactions (ACI) is one of the challenging tasks in atmospheric sciences. The Moderate Resolution Imaging Spectroradiometer and the European Centre for Medium-Range Weather Forecasts ERA-5 reanalysis data are employed to systematically study the ACI over the monsoon region in Pakistan. Based on the monsoon occurrence and rainfall intensity, the whole region is divided into three sub-regions labeled as highly intensive (R1), moderately intensive (R2) and weak (R3) monsoon region. The results indicate that the monthly mean Aerosol Optical Depth (AOD) peaks in the summer monsoon months (Jun, Jul, Aug, Sep). The well-known Twomey effect whereby the Cloud Droplet Radius (CDR) decreases with increasing AOD holds only over R3; the opposite effects (Anti-Twomey effect) are found over R1 and R2, all passing the test of statistical significance (p < 0.05). The multi-year AOD is found to be positively correlated with Cloud Liquid Water Path (CLWP) and Cloud Optical Depth (COD) over R1 and R2, suggesting that thicker clouds containing more water droplets are formed in polluted atmosphere. Over R3, decreases in CLWP and COD are found with increasing AOD only when AOD is less than~0.325. The analysis of ACI over R1 and R2 during the winter months shows similar but stronger responses of CDR, CLWP and COD to the variation in AOD. The weaker responses during the summer monsoon season may attributed to the occurrence of high level cloud and unstable atmospheric condition. Further investigation of the influences of Relative Humidity and pressure vertical velocity on the CDR-AOD relationships shows that although the magnitude of the CDR-AOD correlations change with meteorological conditions, the sign of correlations remain unchanged with meteorological conditions.

Characterizing the long‐term dynamics of aerosol optical depth in the Yangtze River Middle‐Reach urban agglomeration, China

AbstractAlong with the rapid urbanization and economic development, the Yangtze River Middle‐Reach urban agglomeration (YRMR) has suffered from increasingly serious environmental problems. Characterizing the long‐term dynamics of aerosol optical depth (AOD) in YRMR is of important significance for scientific control of aerosol pollutants over this region. Previously, numerous researches have analysed the temporal and spatial characteristics of AOD in different regions through statistical values of AOD time series or using trend analysis methods based on linear regression models. It is challenging to approximate nonlinear trends and identify change points of AOD time series. This paper proposed a new methodology for investigating the dynamics of AOD over the YRMR region on a monthly basis by using the Terra MODIS Collection 6.1 Level 2 aerosol products (MOD04_L2) during 2001–2017. A three‐dimensional spatio‐temporal interpolation method was used to generate gap‐filled monthly AOD maps, and then a generic Bayesian time series decomposition algorithm was applied to analyse the trends and change points of monthly AOD time series. The results showed that the areas with high AOD values were mainly located in the core cities and the connecting belts. Strong positive AOD anomalies had been observed during 2006–2011, whereas the positive anomalies were typically intermingled with negative anomalies and it indicated that the evolution patterns of AOD for different regions were different and influenced by fluctuations. Trend analysis of the monthly mean AOD values indicated that AOD in YRMR increased first, then fluctuated, and finally decreased. Although it was difficult to separate the increasing and fluctuating periods from each other due to the fluctuation, the decreasing trend after 2011 was significant, and a significant drop of AOD had been observed since 2014. Long‐term AOD variations were closely related to national policies, and the results could provide useful references for regional environmental assessment and decision‐making.

Dust Aerosol Retrieval Over the Oceans With the MODIS/VIIRS Dark Target Algorithm: 2. Nonspherical Dust Model

AbstractThe Dark‐target (DT) aerosol algorithm retrieves spectral Aerosol Optical Depth (AOD) and other aerosol properties from Moderate‐resolution Imaging Spectrometer (MODIS) reflectance observations. Over the ocean, the DT algorithm is known to contain scattering‐angle‐dependent biases in its retrievals of AOD, Angstrom Exponent (AE), and Fine Mode Fraction (FMF) for dust aerosols. Following a two‐step strategy to improve the DT retrieval of dust over ocean, for which the first step is to identify dusty pixels (reported in “Part 1”), in this “Part 2,” we report on construction of a new dust model lookup table (LUT) and the strategy for applying it within the existing DT algorithm. In particular, we evaluate different characterizations of dust optical properties from a variety of frameworks and databases, and compare them with the current DT retrieval assumptions. Substituting the standard operational LUT with a spheroid dust model with identified dusty pixels shows significant improvement when compared with collocated AERONET‐identified dusty pixels. Specifically, the application of the new dust model to dusty pixels reduces their AOD bias from 0.06 to 0.02 while improving the fraction of retrievals within expected error from 64% to 82%. At the same time, the overall bias in AE is reduced from 0.13 to 0.06, and the scattering‐angle‐dependent AE bias is largely eliminated. In testing on two full months of data (April and July), the new retrieval will reduce the monthly mean AOD by up to 0.1 and 0.2 in the north Atlantic and Arabian seas, respectively. The average AE and FMF are also reduced in these dust heavy regions.

Improving the Estimation of Daily Aerosol Optical Depth and Aerosol Radiative Effect Using an Optimized Artificial Neural Network

Aerosols can absorb and scatter surface solar radiation (SSR), which is called the aerosol radiative forcing effect (ARF). Great efforts have been made for the estimation of the aerosol optical depth (AOD), SSR and ARF using meteorological measurements and satellite observations. However, the accuracy, and spatial and temporal resolutions of these existing AOD, SSR and ARF models should be improved to meet the application requirements, due to the uncertainties and gaps of input parameters. In this study, an optimized back propagation (BP) artificial neural network (Genetic_BP) was developed for improving the estimation of the AOD values. The retrieved AOD values using the Genetic_BP model and meteorological measurements at China Meteorological Administration (CMA) stations were used to calculate SSR and bottom of the atmosphere (BOA) ARF (ARFB) using Yang’s Hybrid model (YHM). The result show that the Genetic_BP could be used for estimating AOD values with high accuracy (R = 0.866 for CASNET (China Aerosol Remote Sensing Network) stations and R = 0.865 for AERONET (Aerosol Robotic Network) stations). The estimated SSR also showed a good agreement with SSR measurements at 96 CMA radiation stations, with RMSE, MAE, R and R2 of 29.27%, 23.77%, 0.948, and 0.899, respectively. The estimated ARFB values are also highly correlated with the AERONET ARFB ones with RMSE, MAE, R and R2 of −35.47%, −25.33%, 0.843, and 0.711, respectively. Finally, the spatial and temporal variations of AOD, SSR, and ARFB values over Mainland China were investigated. Both AOD and SSR values are generally higher in summer than in other seasons. The ARFB are generally stronger in spring and summer than in other seasons. The ranges for the monthly mean AOD, SSR and ARFB values over Mainland China are 0.183–0.333, 10.218–24.196 MJ m−2day−1 and −2.986 to −1.244 MJ m−2day−1, respectively. The Qinghai-Tibetan Plateau has always been an area with the highest SSR, the lowest AOD and the weakest ARFB. In contrast, the Sichuan Basin has always been an area with low SSR, high AOD, and strong ARFB. The newly proposed AOD model may be of vital importance for improving the accuracy and computational efficiency of AOD, SSR and ARFB estimations for solar energy applications, ecological modeling, and energy policy.

Evaluation of the MODIS C6 Aerosol Optical Depth Products over Chongqing, China

The Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 (C6) aerosol optical depth (AOD) products from the 10/3 km Dark Target (DT) and Deep Blue (DB) algorithms are firstly evaluated using ground observed AODs by the sun photometer in Chongqing, a mountainous mega-city in southwest China. The validation results show that MODIS AODs from 10/3 km DT algorithm are comparable with those of the sun photometer, although there are slight overestimations. However, the DB algorithm substantially underestimates MODIS AODs when comparing with those of the sun photometer. Error analyses imply that the bias of surface reflectance estimation is the main error source for both algorithms. The cloud screening scheme of the DT algorithm is more effective than the DB algorithm. The cloud vicinity effect should be considered in the quality control processes for both of the algorithms. A sensitivity test suggests that in complex terrain area, like Chongqing, the collocation method in the validation of satellite products should be carefully selected according to local circumstances. When comparing the monthly mean AODs of MODIS products with sun photometer observations, it shows that the Terra MODIS AOD products are valid to represent the mean statuses in summer and autumn, but the monthly mean of Aqua MODIS AODs are limited in Chongqing.

Aerosol climatology over the Mexico City basin: Characterization of optical properties

Climatology of Aerosol Optical Depth (AOD), Single Scattering Albedo (SSA), and aerosol particle-size distribution were analyzed using a 15-year (1999–2014) dataset from AErosol RObotic NETwork (AERONET) observations over the Mexico City (MC) basin. The atmosphere over this site is dominated by two main aerosol types, represented by urban/industrial pollution and biomass-burning particles. Due to the specific meteorological conditions within the basin, seasons are usually classified into three as follows: Dry Winter (DW) (November–February); Dry Spring (DS) (March–April), and the RAiny season (RA) (May–October), which are mentioned throughout this article. Using a CIMEL sun photometer, we conducted continuous observations over the MC urban area from January 1999 to December 2014. Aerosol Optical Depth (AOD), Ångström exponent (α440–870), Single Scattering Albedo (SSA), and aerosol particle-size distribution were derived from the observational data. The overall mean AOD500 during the 1999–2014 period was 0.34±0.07. The monthly mean AOD reached a maximal value of 0.49 in May and a minimal value of 0.27 in February and March. The average α440–870 value for the period studied was 1.50±0.16. The monthly average of α440–870 reached a minimal value of 1.32 in August and a maximal value of 1.61 in May. Average SSA at 440nm was 0.89 throughout the observation period, indicating that aerosols over Mexico City are composed mainly of absorptive particles. Concentrations of fine- and coarse-mode aerosols over MC were highest in DS season compared with other seasons, especially for particles with radii measuring between 0.1 and 0.2μm. Results from the Spectral De-convolution Algorithm (SDA) show that fine-mode aerosols dominated AOD variability in MC. In the final part of this article, we present a classification of aerosols in MC by using the graphical method proposed by Gobbi et al. (2007), which is based on the combined analysis of α and its spectral curvature δα.

Reducing Multi-sensor Monthly Mean Aerosol Optical Depth Uncertainty Part II: Optimal Locations for Potential Ground Observation Deployments.

Surface remote sensing of aerosol properties provides "ground truth" for satellite and model validation, and is an important component of aerosol observation system. Due to the different characteristics of background aerosol variability, information obtained at different locations usually have different spatial representativeness, implying that the location should be carefully chosen so that its measurement could be extended to a greater area. In this study, we present an objective observation array design technique that automatically determines the optimal locations with the highest spatial representativeness based on the Ensemble Kalman Filter (EnKF) theory. The ensemble is constructed using aerosol optical depth (AOD) products from five satellite sensors. The optimal locations are solved sequentially by minimizing the total analysis error variance, which means that observations at these locations will reduce the background error variance to the largest extent. The location determined by the algorithm is further verified to have larger spatial representativeness than some other arbitrary location. In addition to the existing active AERONET sites, the 40 selected optimal locations are mostly concentrated on regions with both high AOD inhomogeneity and its spatial representativeness, namely the Sahel, South Africa, East Asia and North Pacific Islands. These places should be the focuses of establishing future AERONET sites in order to further reduce the uncertainty in the monthly mean AOD. Observations at these locations contribute to approximately 50% of the total background uncertainty reduction.

Comparison of PMCAMx aerosol optical depth predictions over Europe with AERONET and MODIS measurements

Abstract. The ability of chemical transport model (CTM) PMCAMx to reproduce aerosol optical depth (AOD) measurements by the Aerosol Robotic Network (AERONET) and the Moderate Resolution Imaging Spectroradiometer (MODIS) over Europe during the photochemically active period of May 2008 (EUCAARI campaign) is evaluated. Periods with high dust or sea-salt levels are excluded, so the analysis focuses on the ability of the model to simulate the mostly secondary aerosol and its interactions with water. PMCAMx reproduces the monthly mean MODIS and AERONET AOD values over the Iberian Peninsula, the British Isles, central Europe, and Russia with a fractional bias of less than 15 % and a fractional error of less than 30 %. However, the model overestimates the AOD over northern Europe, most probably due to an overestimation of organic aerosol and sulfates. At the other end, PMCAMx underestimates the monthly mean MODIS AOD over the Balkans, the Mediterranean, and the South Atlantic. These errors appear to be related to an underestimation of sulfates. Sensitivity tests indicate that the evaluation results of the monthly mean AODs are quite sensitive to the relative humidity (RH) fields used by PMCAMx, but are not sensitive to the simulated size distribution and the black carbon mixing state. The screening of the satellite retrievals for periods with high dust (or coarse particles in general) concentrations as well as the combination of the MODIS and AERONET datasets lead to more robust conclusions about the ability of the model to simulate the secondary aerosol components that dominate the AOD during this period.

Retrieval and Validation of Atmospheric Aerosol Optical Depth From AVHRR Over China

As Advanced Very High Resolution Radiometer (AVHRR) lacks a 2.1- $\mu\text{m}$ band, the widely used “dark target” algorithm cannot be used to retrieve aerosol optical depth (AOD) from AVHRR over land. Instead, a multiple regression algorithm has been developed to process a time series of AVHRR Level_1b measurements over China (15°–45° N, 75°–135° E) for AOD retrieval. As the apparent reflectance of AVHRR is closely related to AOD, which can be provided by Moderate Resolution Imaging Spectroradiometer (MODIS), spatially and temporally collocated Aqua/MODIS AOD and AVHRR Level_1b measurements from four years (January 2008–December 2011) were used to generate the regression coefficients. Angle information, normalized difference vegetation index, water vapor, and surface elevation are chosen in addition to the apparent reflectance as predictors for different surface types. By applying the regression coefficients to AVHRR, the AOD product from independent AVHRR Level_1b measurements (May 2003–December 2007) was generated. Validation with AErosol RObotic NETwork (AERONET) AOD and comparison with MODIS AOD products have been conducted to evaluate the uncertainty of the AVHRR AOD from May 2003 to December 2007. The distribution pattern of the seasonal mean AOD from AVHRR is consistent with that of the MODIS AOD. Taking regions with rapid economic development, for example, the regional monthly mean AOD for these two data sets agrees well, with a consistent tendency and high correlation coefficients. When compared with AERONET from four sites in China, the results are also encouraging. Results show that the multiple regression method offers the potential to generate an AOD climatology data record from a long-term AVHRR Level_1b data set over land.

Ground-based aerosol climatology of China: aerosol optical depths from the China Aerosol Remote Sensing Network (CARSNET) 2002–2013

Abstract. Long-term measurements of aerosol optical depths (AODs) at 440 nm and Ångström exponents (AE) between 440 and 870 nm made for CARSNET were compiled into a climatology of aerosol optical properties for China. Quality-assured monthly mean AODs are presented for 50 sites representing remote, rural, and urban areas. AODs were 0.14, 0.34, 0.42, 0.54, and 0.74 at remote stations, rural/desert regions, the Loess Plateau, central and eastern China, and urban sites, respectively, and the corresponding AE values were 0.97, 0.55, 0.82, 1.19, and 1.05. AODs increased from north to south, with low values (&lt; 0.20) over the Tibetan Plateau and northwestern China and high AODs (&gt; 0.60) in central and eastern China where industrial emissions and anthropogenic activities were likely sources. AODs were 0.20–0.40 in semi-arid and arid regions and some background areas in northern and northeastern China. AEs were &gt; 1.20 over the southern reaches of the Yangtze River and at clean sites in northeastern China. In the northwestern deserts and industrial parts of northeast China, AEs were lower (&lt; 0.80) compared with central and eastern regions. Dust events in spring, hygroscopic particle growth during summer, and biomass burning contribute the high AODs, especially in northern and eastern China. The AODs show decreasing trends from 2006 to 2009 but increased ~ 0.03 per year from 2009 to 2013.

Mesoscale modeling study of the interactions between aerosols and PBL meteorology during a haze episode in Jing–Jin–Ji (China) and its nearby surrounding region – Part 1: Aerosol distributions and meteorological features

Abstract. The urbanized region ofJing(Beijing)-Jin(Tianjin)-Ji (alias of Hebei province) and its nearby surrounding region (3JNS) is becoming China's most polluted area by haze, exceeding even the Yangtze and Pearl river deltas. Aside from pollutant emission, the meteorology of the planetary boundary layer (PBL) is the most important factor affecting haze pollution. Focusing on July 2008, the aerosol optical properties and PBL meteorology features closely related to haze formation were simulated in the 3JNS region using an online atmospheric chemical transport model. The relationship between regional PBL meteorology, PM2.5, and haze is discussed. Model results accurately simulated the aerosol optical depth (AOD), single scattering albedo (SSA) and asymmetry parameter (ASY), validated by comparison with observations from the MODerate Resolution Imaging Spectroradiometer (MODIS), the China Aerosol Remote Sensing NETwork (CARSNET) and the Aerosol Robotic NETwork (AERONET). Modeled PBL wind speeds showed reasonable agreement with those from the National Centers for Environmental Prediction (NCEP) Reanalysis 2. A monthly mean AOD value as high as 1.2 was found from both model and observations, with a daily mean larger than 2.0 during haze episodes in the 3JNS region. Modeled and observed SSA values of 0.90–0.96 and ASY values of 0.72–0.74 demonstrated the high scattering characteristic of summer aerosols in this region. PBL wind speeds from modeled and NCEP data both showed a reversing trend of PM2.5 variation, illustrating the importance of the "PBL window shadow" in haze formation. Turbulence diffusion and PBL height had opposite phases to surface PM2.5, indicating that lower PBL height and weaker PBL turbulence diffusion are essential to haze formation. It is noted that homogeneous air pressure does not occur at the surface, but at an 850–950 hPa height during the haze episode. The momentum transmitting downward of the cold air from above the PBL to the low PBL and surface lead to an increase in surface wind speeds and haze dispersal.

Variability in aerosol optical properties and radiative forcing over Gorongosa (18.97oS, 34.35oE) in Mozambique

This paper reports the observational results of aerosol optical, microphysical and radiative characteristics for the time measured over Gorongosa (18.97oS, 34.35oE, 30 m asl) in Mozambique using a ground-based AERONET sun-sky radiometer. In the present study, the data recorded during the period July–December, 2012 have been used and particular attention was paid to show how aerosol loading evolves during the biomass burning season (spring) including pre- and post-months. The results reveal that the monthly mean aerosol optical depth (AOD) at 500 nm was high (low) with 0.64 ± 0.34 (0.20 ± 0.06) in September (November), while the Angstrom Exponent (AE) (α 440–870) decreased, except September (1.56 ± 0.26) due to increase in the fine-mode aerosol concentration produced from biomass burning. The volume size distribution (VSD) has bimodal lognormal structure and has fine-mode (coarse) maximum at a radius of 0.15 µm (3.0 µm) in September (December). The single scattering albedo (SSA) decreases with wavelength from July to October and almost stable in November and December. The imaginary (Im) refractive index (RI) showed a strong evidence of black carbon aerosol origin during the biomass burning months. Aerosol radiative forcing (ARF) computed from SBDART model shows large negative values at the surface (−89.22 W m−2) and at the top (−22.36 W m−2), with a higher value of atmospheric forcing (+66.87 W m−2) resulting in average tropospheric heating rate of 1.88 K day−1 for the study period. Further, the comparison shows good agreement between the ARFs at the top and bottom of the atmosphere derived from AERONET to SBDART.

Insight into wintertime aerosol characteristics over Beijing

Aerosol particle pollution in northern China has crucial impact on regional and global climate. The monthly mean aerosol optical depth (AOD) at 550nm in the northern China had its minimum in winter. Surface measurements of aerosol microphysical and optical properties over the Beijing urban area from December 3, 2011 to January 1, 2012 are presented here. The aim of this study was to evaluate wintertime aerosol characteristics, which were impacted by wind and relative humidity. The following mean values were observed: scattering coefficient (293±283Mm−1, 1Mm−1=10−6m−1), absorption coefficient (136±125Mm−1), backscattering ratio (0.15±0.02), single scattering albedo (0.65±0.08), at 550nm, and scattering Angstrom exponent (2.01±0.22), with mean relative humidity of (47±24)%. Wintertime values of scattering and absorption coefficients had wind dependence, showing that high values occurred with calm winds while the dilution effect of strong winds was obtained for wind speed larger than 4ms−1. Based on air mass backward trajectories, wintertime haze episodes in Beijing were induced by local-pollution emissions, whereas clear periods were dominated by air masses from clean, continental, northwestern areas. Our study indicates that the main challenge to improve wintertime air quality in Beijing is to control local emissions of particulate pollution.

Aerosol optical and physical properties during winter monsoon pollution transport in an urban environment

We analysed aerosol optical and physical properties in an urban environment (Kolkata) during winter monsoon pollution transport from nearby and far-off regions. Prevailing meteorological conditions, viz. low temperature and wind speed, and a strong downdraft of air mass, indicated weak dispersion and inhibition of vertical mixing of aerosols. Spectral features of WinMon aerosol optical depth (AOD) showed larger variability (0.68-1.13) in monthly mean AOD at short-wavelength (SW) channels (0.34-0.5 μm) compared to that (0.28-0.37) at long-wavelength (LW) channels (0.87-1.02 μm), thereby indicating sensitivity of WinMon AOD to fine aerosol constituents and the predominant contribution from fine aerosol constituents to WinMon AOD. WinMon AOD at 0.5 μm (AOD 0. 5) and Angstrom parameter ( α) were 0.68-0.82 and 1.14-1.32, respectively, with their highest value in December. Consistent with inference from spectral features of AOD, surface aerosol loading was primarily constituted of fine aerosols (size 0.23-3 μm) which was 60-70 % of aerosol 10- μm (size 0.23-10 μm) concentration. Three distinct modes of aerosol distribution were obtained, with the highest WinMon concentration at a mass median diameter (MMD) of 0.3 μm during December, thereby indicating characteristics of primary contribution related to anthropogenic pollutants that were inferred to be mostly due to contribution from air mass originating in nearby region having predominant emissions from biofuel and fossil fuel combustion. A relatively higher contribution from aerosols in the upper atmospheric layers than at the surface to WinMon AOD was inferred during February compared to other months and was attributed to predominant contribution from open burning emissions arising from nearby and far-off regions. A comparison of ground-based measurements with Moderate Resolution Imaging Spectroradiometer (MODIS) data showed an underestimation of MODIS AOD and α values for most of the days. Discrepancy in relative distribution of fine and coarse mode of MODIS AOD was also inferred.

Aerosol climatology at Delhi in the western Indo‐Gangetic Plain: Microphysics, long‐term trends, and source strengths

AbstractWe present the climatology of aerosol microphysics, its trends, and impact of potential sources based on the long term measurements (for a period of 11.5 years from December 2001 to May 2012) of aerosol optical depths (AOD) in the spectral range 340–1020 nm from an urban center Delhi (28.6°N, 77.3°E, 238 m mean sea level) in the western Indo‐Gangetic Plain (IGP). The study is the first ever long‐term characterization of aerosols over the western IGP from the ground‐based measurements. AODs are known to affect the air quality, visibility, radiative balance, and cloud microphysics of the region and IGP is one of the highest populated and polluted regions of the world. Our measurements show consistently high AOD during the entire period of observation. The seasonal variations of spectral AODs and Angstrom parameters are generally consistent every year. The AODs show a weak but statistically significant (in 95% confidence level) decreasing trend approximately −0.02/year at 500 nm, possibly, modulated by the pre‐monsoon heavy dust loading during the first half of the observation period. The climatological monthly mean AOD at shorter wavelengths peaks twice, during June and November, while at longer wavelengths it shows only one peak in June. The annual variations of Angstrom exponent, α and its derivative, α′ suggest the prevalence of multi‐modal aerosol size distributions at Delhi. The coarse‐mode aerosols dominate during summer (March–June) and monsoon (July–September) seasons, whereas fine/accumulation mode enhances during post‐monsoon (October–November) and winter (December–February) seasons. Potential advection pathways have been identified using concentration weighted trajectory (CWT) analysis of the 5 day isentropic air mass back trajectories at the observation site and their seasonal variations are discussed.

Aerosol optical properties retrieved from Sun photometer measurements over Shanghai, China

Using a CIMEL Sun photometer, we conducted continuous observations over the urban area of Shanghai (31°14′N, 121°32′E) from 18 April 2007 to 31 January 2009. The aerosol optical depth (AOD), Angstrom wavelength exponent, single scattering albedo (ω0), and aerosol particle size distribution were derived from the observational data. The monthly mean AOD reached a maximum value of 1.20 in June and a minimum value of 0.43 in January. The monthly averaged Angstrom wavelength exponent reached a minimum value of 1.15 in April and a maximum value of 1.41 in October. The frequencies of the AOD and Angstrom wavelength exponent presented lognormal distributions. The averaged ω0 at 550 nm was 0.94 throughout the observation period, indicating that the aerosols over Shanghai are composed mainly of scattering particles. The concentrations of coarse mode and accumulation mode aerosols over Shanghai were highest in spring compared with other seasons, especially for those particles with radii between 1.0 and 2.0 μm. The median radius of monthly averaged accumulation mode aerosols increased with increasing AOD, and fine particles accounted for the majority of the aerosol volume concentration. The ratios of the monthly averaged volume concentration of accumulation mode and coarse mode aerosols (Vf/Vc) were over 0.6 for all months studied and reached up to 1.94 in August. The volumes of the two modes changed with AOD, but their correlations presented different sensitivities, that is, the volume concentration of accumulation mode aerosols was more sensitive to variations in AOD than that of coarse mode aerosols. The aerosol volume concentration decreased with increasing ω0, indicating that the higher the volume concentration of aerosols, the higher the absorption in particle extinction properties. The increase in absorption was caused primarily by secondary species coated on black carbon (BC) and primary organic carbon (POC) particles.