Temporal characteristics of columnar aerosol optical properties and radiative forcing (2011–2015) measured at AERONET’s Pretoria_CSIR_DPSS site in South Africa

Accepted Manuscript Temporal characteristics of columnar aerosol optical properties and radiative forcing (2011–2015) measured at AERONET's Pretoria CSIR_DPSS site in South Africa K. Raghavendra Kumar, Na Kang, V. Sivakumar, Derek Griffith PII:

S1352-2310(17)30437-5

DOI:

10.1016/j.atmosenv.2017.06.048

Reference:

AEA 15410

To appear in:

Atmospheric Environment

Received Date: 26 March 2017 Revised Date:

24 June 2017

Accepted Date: 28 June 2017

Please cite this article as: Kumar, K.R., Kang, N., Sivakumar, V., Griffith, D., Temporal characteristics of columnar aerosol optical properties and radiative forcing (2011–2015) measured at AERONET's Pretoria CSIR_DPSS site in South Africa, Atmospheric Environment (2017), doi: 10.1016/ j.atmosenv.2017.06.048. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Graphical Abstract

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Temporal characteristics of columnar aerosol optical properties and radiative forcing (2011–2015) measured at AERONET’s Pretoria CSIR_DPSS site in South Africa

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Discipline of Physics, School of Chemistry and Physics, University of KwaZulu-Natal, Durban 4000, Kwazulu-Natal, South Africa. c

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Optronic Sensor Systems, Council for Scientific and Industrial Research (CSIR)–DPSS, Pretoria 0001, Gauteng, South Africa.

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Collaborative Innovation Centre on Forecast and Evaluation of Meteorological Disasters, Key Laboratory of Meteorological Disasters, Ministry of Education (KLME), Joint International Laboratory on Climate and Environment Change (ILCEC), Key Laboratory for Aerosol-CloudPrecipitation of China Meteorological Administration, School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing 210044, Jiangsu, China.

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K. Raghavendra Kumar , Na Kang , V. Sivakumar , Derek Griffith

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*Corresponding author

Tel: +86-25-58731592 Fax: +86-25-58699771 Email: [email protected], [email protected] 1

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ABSTRACT

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northwest of South Africa (SA) possessed large aerosol loading and still remained unexplored as

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none of the authors have been extensively studied. The characteristics of aerosol optical,

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physical, and radiative properties, as well as their relationships presented in this paper, were

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derived from the direct sun and sky radiances measured at Pretoria during August 2011–

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December 2015 using the AERONET’s (CE-318) automatic sun/sky radiometer. The annual

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mean AOD440, AE440-870, and SSA-T440 estimated at Pretoria during the study period were 0.23 ±

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0.13, 1.50 ± 0.26, and 0.91 ± 0.04, respectively. The mean AOD440 (AE440-870) for the study

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period appeared higher during the spring and summer seasons (summer), suggest dominance of

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fine mode particles attributed to biomass burning activities and seasonal influence of

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meteorology. Analysis of frequency occurrences of AOD and AE also indicate that this region is

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richly populated with fine mode particles. Further, the AOD-AE relationship was studied at

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Pretoria and the result concluded that the mixed type aerosols contributed more among the others

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followed by the urban/industrial-biomass burning and clean continental (background) aerosols.

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The high summertime SSA-T440 and fine mode radius of AVSD could be associated with the

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hygroscopic growth of water-soluble aerosols under high water vapor (absorbing aerosols). The

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positive (negative) values of aerosol radiative forcing (ARF) were observed in all the months, an

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indication of significant heating (cooling) within the atmosphere (top of the atmosphere (TOA)

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and bottom of the atmosphere (BOA)) were due to strong absorption (scattering) of radiation.

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Further, the efficiency derived between ARF and AOD440 indicated that ARF is a strong function

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of AOD at the BOA noted with a high degree of correlation coefficient (r = 0.93).

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Keywords: Sunphotometer; AOD; SSA; Radiative forcing; Biomass burning.

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Ground-based observations of the spectral aerosol optical depths (AODs) revealed that the

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1. Introduction Atmospheric aerosol particles are emitted from various natural and anthropogenic

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sources, which play an important role in the aerosol-climate-cloud interactions (Haywood and

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Shine, 1997). They affect weather and climate both directly (by scattering and absorbing both

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solar and terrestrial radiations) and indirectly (by modifying cloud albedo and droplet size

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distribution; thereby, changing the radiative properties and lifetime of clouds) (Rosenfeld, 2000).

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On the other hand, aerosols are an important component of climate models and contribute a large

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uncertainty to the radiative forcing of the earth-atmosphere system, due to their large spatial and

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temporal variations (IPCC, 2013). Understanding the impact of aerosols on radiative transfer in

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the atmosphere requires accurate knowledge of their columnar optical and microphysical

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properties such as size distribution, chemical composition, and optical properties which

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demonstrates the effects of aerosols on climate change (Dubovik et al., 2002; Alam et al., 2011;

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Che et al., 2015; Koo et al., 2016; Adesina et al., 2017).

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The comprehensive ground-based remote sensing networks such as the AErosol RObotic

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NETwork (AERONET; e.g, Holben et al., 1998) and the SKYradiometer NETwork (SKYNET;

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e.g., Kim et al., 2004) have been widely established and procure continuous datasets in various

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parts of the globe. Apart from these, the other regional observation network of stations, for

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example, the European Aerosol Research LIdar NETwork (EARLINET; e.g., Boselli et al.,

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2012), and China Aerosol Remote Sensing NETwork (CARSNET; e.g., Che et al., 2009) were

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also established. These networks have provided various parameters at multiple wavelengths to

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monitor column-integrated aerosol optical properties. Several previous studies found that these

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ground-based measurements of aerosol optical properties showed large sensitivity at the selected

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wavelengths (Eck et al., 2003, 2005; Alam et al., 2011, 2014; Queface et al., 2011; Kumar et al.,

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2013; Zhu et al., 2014; Bhaskar et al., 2015; Wang et al., 2015; Wu et al., 2015; Che et al., 2015;

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Kang et al., 2016; Yu et al., 2016a, b; Koo et al., 2016; Patel et al., 2017; Mor et al., 2017). A few earlier researchers had reported the optical and radiative properties of aerosols

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retrieved by these networks over certain regions of South Africa (SA) (Sivakumar et al., 2010;

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Queface et al., 2011; Kumar et al., 2013; Hersey et al., 2015; Adesina et al., 2014, 2017). These

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are still very limited in the context with the long-term observations, especially in the climatically

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important urban and industrial regions, particularly over Pretoria in the northwest of SA.

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However, only selected optical parameters such as aerosol optical depth (AOD) and Ångström

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exponent (AE) have been examined in these regions with a limited amount of data and none of

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them mentioned above have not been extensively studied (except Queface et al. (2011). Recently,

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Adesina et al. (2017) examined the aerosol optical and microphysical properties, and associated

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model derived aerosol radiative forcing (ARF) using the AERONET’s sunphotometer data

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measured at Skukuza (SA) during 1999–2010. More details on the previous investigation of

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aerosol optical properties conducted by several authors over different regions of SA can be found

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elsewhere (Adesina et al., 2017) and hence not repeated. The significant impact on ARF with the

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detailed analysis of aerosol size and absorption characteristics is yet to be investigated. In this

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regard, characterization of aerosols over this metropolitan region (Pretoria) has received great

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scientific interest and need for studying long-term atmospheric aerosol properties. In this view,

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the sunphotometer installed in Pretoria by the Council for Scientific and Industrial Research

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(CSIR) started measuring aerosol properties since August 2011. Currently, this station is active

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and is part of the AERONET network of stations named as ‘Pretoria_CSIR_DPSS’ (25.75°S,

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28.28°E, 1449 m above sea level) (http://aeronet.gsfc.nasa.gov/).

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The present investigation focused on examining the multi-year (August 2011–December

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2015) analyses of column-integrated aerosol optical properties and radiative forcing, for the first

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time at Pretoria. The objectives of this study are to: (i) investigate the long-term temporal

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distributions of aerosol optical, physical and radiative properties on monthly and seasonal scales,

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(ii) find out distinct aerosol source regions with the aid of air mass trajectories derived from the

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Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model, (iii) identify and

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study the impact of a variety of aerosol types originated from different sources, (iv) study the

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relationship between optical and physical properties, and (v) arrive at a comprehensive

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understanding of ARF and its efficiency (ARFE) over Pretoria in northwest SA.

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2. Data, instrument, and methodology

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2.1. Site description

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Pretoria, situated approximately 55 km in the northwest of South Africa, is located in a

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transitional belt between the plateau of Highveld to the south and the lower-lying Bushveld to

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the north. It lies at an altitude of about 1339 m (4393 ft) above sea level in a warm, sheltered,

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fertile valley, surrounded by the hills of the Magaliesberg range. The urban-industrial city has a

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humid subtropical climate with long duration of hot rainy summers, and a short period of cold

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and dry winters. The major industries in the region include the manufacture of motorcycles,

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chemicals, pharmaceuticals, engineering products, construction materials, steel industries, oil

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refineries, cement factories, and power plants. The detailed description of the site and aerosol

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sources can be found elsewhere (Adesina et al., 2014; Kumar et al., 2014a, b).

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2.2. Meteorology

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The hourly (average, maximum and minimum) surface meteorological data such as wind

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speed (WS in m s-1), wind direction (WD in degree), air temperature (AT in °C), relative 5

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humidity (RH in %), and total precipitation (TP in mm) were recorded from the automatic

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weather station (AWS) installed on the CSIR campus provided by the South African Weather

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Services (SAWS). The four constituting seasons followed in the present work include summer

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(December–February; DJF), autumn (March–May; MAM), winter (June–August; JJA), and

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spring (September–November; SON). Cold winters in Pretoria were typically characterized by

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dry and severe, with a minimum (June) recorded annual averaged AT of about 12.51 ± 0.92 ºC.

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Summer is the hottest and rainy season observed with an annual averaged AT going maximum

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up to 24.0 ± 1.23 ºC (February) and noticed an annual maximum TP of ~678 mm in December

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during the study period (Fig. 1i). The autumn and spring seasons were characterized by fairly

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moderate ATs (18–21 °C), with TP of > 600 mm in each season. RH almost varied inversely

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with AT showed a maximum of 64% in summer (January) and a minimum during late winter

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with 37% in August. The annual TP varies between 1 and 700 mm (Fig. 1i) and concentrated

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mainly during the summertime followed by the spring and autumn seasons. The surface wind

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speed found over the measurement site was observed to be low with ~1.9 ms-1 during autumn

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prevailing with calm winds and direction of the winds was variable. Whereas, the surface wind

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flow slowly changes its direction to northerly and southeasterly during the winter season where

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high wind speeds were found with a mean value of ~2.4 ms-1 (Fig. 1ii). During summer and

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spring seasons, most of the winds were blown from the northwest and easterly/southeast

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directions, expected to transport dust and smoke particles resulted in high aerosol loading as

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described in the results and discussion section.

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2.3. Air mass trajectories

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The backward air mass cluster trajectories have been computed by using the HYSPLIT

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model of NOAA, USA (http://ready.arl.noaa.gov/HYSPLIT.php; Draxler and Rolph, 2003) to 6

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determine the aerosol sources and their transport pathways reaching the measurement site. The

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meteorological files for running the model were extracted from NCEP/NCAR reanalysis data to

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retrieve 7-day (168 h) air mass back trajectories arriving Pretoria at 1500 m above ground level

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(AGL) during the study period. It is evident that the trajectory clusters are shown in winter (JJA)

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and spring (SON) seasons carried polluted air masses from the arid/semi-arid regions in the

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northwest of SA and smoke particles produced from biomass burning resulted in high AOD440

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(Fig. 2). Whereas, the trajectory clusters during summer and autumn seasons originated from the

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far southern Indian and Atlantic Oceans brings marine air masses attributed to low AOD440

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resulting in high precipitation (see Fig. 1) that recorded over the study region. However, the air

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masses transported from neighboring regions together with local meteorology modulations

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affected the aerosol characteristics over the measurement site.

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2.4. Instrument and uncertainties

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Ground based networks are invaluable for understanding and validating satellite derived

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products. The AERONET is one of the commonly used ground-based networks following its

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worldwide distribution. It was established by NASA and uses Cimel (CE-318) sun/sky

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radiometers that take measurements of direct sun and diffuse sky radiances within the spectral

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ranges 340-1640 nm and 440-1020 nm, respectively (Holben et al., 1998). A detailed description

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of this instrument and data retrieval is provided by Holben et al. (1998) and followed by several

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authors (Singh et al., 2004; Alam et al., 2011, 2012; Queface et al., 2011; Wang et al., 2015; Yu

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et al., 2016a; Kang et al., 2016; Mor et al., 2017; Patel et al., 2017; Adesina et al., 2017 and

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references therein). Other optical parameters that can be retrieved from the standard AERONET

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inversion products are the fine mode fraction of AOD at 500 nm (FMF500), which is defined as

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the ratio of fine-mode AOD to the total AOD. Detailed properties of the aerosols (optical,

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physical and radiative) served as useful inputs in calculating broadband solar fluxes within the

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spectral range from 0.3 to 4.0 µm (Garcia et al., 2012). The AERONET data were downloaded at http://aeronet.gsfc.nasa.gov/ for the period

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during August 2011–December 2015, and level 2.0 (cloud-screened and quality-assured) all

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points format data measured at Pretoria are used in this study to obtain daily, monthly, and

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seasonal mean values. However, the observations were not continuous because of instrument

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calibration and maintenance. Overall, to carry out the characterization of aerosols, we have used

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1217 daily mean values of AOD and other optical properties, 757 daily values to study the

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microphysical properties having the AVSD and ASY, and 294 daily mean values of radiative

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forcing, The estimated uncertainty in AOD retrieval under cloud-free conditions is <±0.01 for

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longer wavelengths (>440 nm) and <±0.02 for shorter wavelengths, which is less than the ±5%

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uncertainty for the retrieval of sky radiance measurements (Eck et al., 2003, 2005; Dubovik et

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al., 2000, 2006; Singh et al., 2004). The single scattering albedo (SSA) and refractive index (RI)

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of almucantar retrievals were available only when AOD440 ≥ 0.4 to avoid the large inversion

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errors from the limited aerosol information content when AOD440 < 0.4 (Dubovik et al., 2000,

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2002; Smirnov et al., 2000; Eck et al., 2003; Singh et al., 2004; Alam et al., 2011, 2014; Yu et

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al., 2016a; Adesina et al., 2017). This resulted in the fewness of the data due to unavailability of

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SSA and RI values for the most part of the year. The SSA was expected to have an uncertainty of

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0.03–0.05 depending on the aerosol type and loading (AOD440 ≥ 0.4) for solar zenith angles >

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50° (Dubovik et al. 2000; Singh et al., 2004; Alam et al., 2012). The detailed retrieval accuracy,

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calibration, and uncertainties of standard CE-318 sun/sky radiometer can be found elsewhere

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(Dubovik et al., 2002; Singh et al., 2004; Alam et al., 2011, 2012; Olcese et al., 2014; Wang et

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al., 2015; Xia et al., 2016; Yu et al., 2016a, b; Adesina et al., 2014, 2017; Patel et al., 2017).

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2.5. Data analysis and methods

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2.5.1. Criteria followed in data quality In using level 2.0 inversion data, the number of available observations of SSA and

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complex RI is quite limited, since these variables are only considered reliable when AOD440 ≥

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0.4 (Dubovik et al., 2000; Singh et al., 2004). Thus, we don’t have sufficient amount of data and

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information on SSA and complex RI for other conditions (moderate-to-low AOD), except when

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the region is severely experienced with biomass burning (local and regional transport) resulting

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in high AOD. Whereas, the other inversion products of almucantar scan radiances such as

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aerosol volume size distribution (AVSD) and asymmetry parameter (ASY) are provided for all

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AOD levels. This result in a decrease of data count (SSA and RI) which will be used in the

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further analysis and affects the spectral and temporal behavior of the parameter; in turn have an

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impact on ARF. To solve this problem, we have used the level 2.0 data following the same

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criteria used by the AERONET team (Dubovik et al., 2006), and applied less threshold to AOD

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with AOD440 ≥ 0.15 instead of 0.4. This kind of approach has been adopted by previous authors

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using the AERONET data where the aerosol loading represents regional background condition

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(e.g., Mallet et al., 2013; Mateos et al., 2014). When this condition was applied to filter the

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SSA440 in the inversion product, 294 daily data points have been passed through the screening.

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2.5.2. Aerosol radiative products from inversion algorithm

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The SSA and ASY are important inputs for the radiative transfer codes used in the

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quantification of impact of aerosols on climate radiative effect. The AERONET inversion

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algorithm also calculates broadband solar radiation and estimated the ARF using the DIScrete

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Ordinate Radiative Transfer (DISORT) module provided with the retrieved inputs are AOD,

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SSA, ASY, and complex RI (Garcia et al., 2012; Che et al., 2015; Adesina et al., 2017). The ARF 9

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is defined as the effect of total aerosols (both natural and anthropogenic) on the radiative fluxes

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because of the scattering and absorption of solar radiation by aerosols and is used to quantify the

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impact of aerosols on the climate. The ARF (measured in W m-2) at the top of the atmosphere

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(TOA at 100 km) and bottom of the atmosphere/surface (BOA at 1 km) is calculated as the

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difference in net flux with (WA) and without (WOA) aerosol because of the instantaneous

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change of the aerosol content in the atmosphere.

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(

TOA TOA BOA BOA ∆F = ∆FWA − ∆FWOA − ∆FWA − ∆FWOA

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where ∆F denotes the net radiation (downward radiation F↓ minus upward radiation F↑).

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The ARFBOA denotes the combined effects of scattering and absorption of solar radiation

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by air-suspended particles on the net flux at the BOA; ARFTOA denotes the reflection of solar

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radiation to space by aerosols, and ARFATM denotes the absorption of solar radiation within the

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atmosphere (Mateos et al., 2014; Wu et al., 2015; Tiwari et al., 2016; Xia et al., 2016). Negative

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and positive values of ARF correspond to an aerosol cooling and warming effects, respectively.

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A correction term was proposed to reduce the uncertainties associated with the estimation of

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ARF and has been used by other researchers (Garcia et al., 2012; Che et al., 2015; Wu et al.,

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2015; Xia et al., 2016; Adesina et al., 2017) so that instead of the BOA values provided by the

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AERONET, (1–A)×(BOA) has been used in this work, where A is the averaged surface albedo.

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The surface albedo, required for calculating the ARF, was obtained from the 8-day MODIS land

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products (MCD43B3, downloaded from ftp://e4ft101.cr.usgs.gov/MOTA/MCD43B3.005) used

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in the AERONET inversion algorithm. Note that instantaneous ARF for the solar zenith angles

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between 50° and 80° is presented in this work.

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In addition to the ARF values, the aerosol radiative forcing efficiency (ARFE) values

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were also obtained from the AERONET inversion algorithm. Since ARF increases as the AOD 10

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increases, the definition of ARFE is crucial. It is defined as a quantity independent of the aerosol

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load that represents the rate at which the atmosphere is forced per unit of AOD. Hence,

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instantaneous ARFE has been computed as the ratio of instantaneous ARF to the corresponding

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AOD500 (Che et al., 2015) and the results are summarized in the following sections.

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3. Results and discussion

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3.1. Frequency distributions in optical properties

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The relative frequency histograms of all the daily averaged AOD500, Ångström exponent

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(AE470-870), and precipitable water vapor content (PWC) along with the cumulative frequency

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(CF), the total number of daily data (N), and annual mean (±standard deviation) during the study

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period is shown in Fig 3. The bin interval in the present study was set to 0.1 for AOD and 0.2 for

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AE470-660 and PWC; and we considered all AOD, AE470-660, and PWC values in the range 0–1.1,

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0–2.6, and 0–3.2, respectively. It is evident from Fig 3a that a unimodal AOD distribution of

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frequencies (significantly skewed towards lower values) was observed during the study period

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signifying dominance of a particular aerosol type, similar to the investigation by Kumar et al.

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(2014) found over different environments. With an annual mean AOD440 of 0.23±0.13, the

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strongest mode was observed in the bin interval 0.1-0.2 which showed generally a less polluted

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environment. AE470-870, with left skewness, also showed a single peak distribution of frequencies

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similar to investigations by Bi et al. (2011) and Adesina et al. (2014, 2017) over Northwest of

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China and South Africa, respectively. The occurrence of strongest mode at relatively higher size

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bins (1.4-1.6) supported by relatively higher annual mean AE470-870 of 1.50±0.26 (Fig. 3b),

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implies that the anthropogenic fine mode particles (smoke particles produced from biomass

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burning) contributed more relative to coarse mode particles during the study period. On the other

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hand, the PWC showed the widest unimodal annual heterogeneity (Fig. 3c). With an annual mean

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of 1.32±0.40, PWC peaked at relatively lower interval size bins of 0.6-0.8 and at subsequent

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interval bins.

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3.1.1. Spectral variations of AOD

The spectral variation of AOD in different seasons is demonstrated in Fig. 3d. It is

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obvious that the AOD is strongly dependent on wavelength, with higher AOD values at shorter

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wavelengths and lower values at longer wavelengths followed and consistent with the well

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established Mie scattering theory of particles (Kumar et al., 2009). The spectral AOD distribution

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pattern showed highly skewed towards the longer wavelengths indicating the predominance of

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fine mode aerosols; whereas, the flatter spectral distribution showed the higher contribution of

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coarse mode particles (Eck et al., 2005; Kumar et al., 2009; Tiwari et al., 2016; Adesina et al.,

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2017). Several studies have been carried out to investigate this fact, which established that the

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fine mode particles have a much greater effect on AOD in the visible region (Schuster et al.,

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2006; Kumar et al., 2009; Kaskaoutis et al., 2009; Xia et al., 2016; Tiwari et al., 2016; Mor et al.,

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2017). AOD is highly skewed with steepest spectral distribution was observed in all the seasons,

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particularly in spring. At smaller wavelengths, a prominent peak was observed during spring

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followed by the summer, autumn, and winter seasons. However, AOD at longer wavelengths has

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nearly same value for all seasons, with a little higher AOD was observed in spring and winter

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seasons due to the abundance of coarser particles, as it is clearly evidenced from the air mass

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back trajectories. Larger AOD spectral pattern was observed during spring followed by the

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summer seasons suggested that the AOD was contributed primarily by fine mode aerosols due to

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faster production mechanism by secondary aerosols (or gas-to-particle conversion) (Kaskaoutis et

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al., 2009; Kumar et al., 2009, 2014a; Tiwari et al., 2016). A relatively lower spectral AOD

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pattern was exhibited in winter which may be the result of weak generation mechanism of

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aerosols and induced local meteorological phenomena. A detailed discussion on the seasonal

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variability and effect of meteorology on AOD is presented in the following sections.

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3.2. Temporal variations of direct sun observations Fig. 4 and Table 1 shows the respective multiyear monthly and seasonal mean variations

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obtained from the daily values of AOD440, AE440-870, PWC, and FMF500 measured at Pretoria. The

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box-whisker plots shown in Figs. 4 (a-d) represents the mean (solid circle), the median (line

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inside the box), upper/lower quartiles (the box edges), and the data range excluding outliers (the

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whisker caps). The annual mean AOD440 at CSIR_DPSS site in Pretoria during the measurement

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period 2011–2015 is about 0.23 ± 0.13, which is higher than those observed at other stations in

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SA, with Skukuza during 1998–2008 (AOD500=0.21) and Cape Town during 2008–2013

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(AOD550=0.06) investigated by Queface et al. (2011) and Nyeki et al. (2015), respectively (see

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Table 2). Further, it is examined that the annual mean AOD500 reported at other urban regions

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such as Karachi in Pakistan (Alam et al., 2011), Wuhan in Central China (Wang et al., 2015),

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Beijing in North China (Xia et al., 2016), Shanghai in East China (Cheng et al., 2015), Delhi in

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North India (Tiwari et al., 2016), and Jodhpur in the Northwest of India (Bhaskar et al., 2015)

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showed higher than that of our results (see Table 2). However, the mean AOD pattern showed the

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highest values observed in spring and the lowest ones during the winter season. In addition to this

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regular pattern, the summer months (February) recorded a local maximum of 0.31, a behavior

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that will be elaborated in the next section. However, the median values are slightly lower than the

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monthly mean, shifting the distribution to the right. Additionally, the high variability in standard

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deviation generally coincides with high AOD values during spring and summer seasons, and vice

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versa.

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The seasonal mean AODs at 440 nm were found to be 0.24 ± 0.15, 0.20 ± 0.11, 0.20 ±

319

0.12, and 0.27 ± 0.17 during summer, autumn, winter, and spring seasons, respectively (Table 1)

320

which could be related to the variable aerosol sources. The high AOD during spring (SON)

321

attributed to the smoke particles (fine mode) produced from biomass burning and forest fires

322

from local and regional sources. This kind of anthropogenic activities over SA is most frequent

323

and is a strong seasonal phenomenon resembles the previous results over Pretoria and other

324

regions of SA (Piketh et al., 1999; Sivakumar et al, 2010; Queface et al., 2011; Kumar et al.,

325

2013, 2014b; Adesina et al., 2014, 2017; Nyeki et al., 2015; Hersey et al., 2015). However, the

326

predominance of coarse mode aerosols attributed to dust emissions (low AE440-870 and FMF500)

327

relative to the fine mode is evident during spring and is further supported by the results discussed

328

previously obtained from the HYSPLIT model. Further, the higher AODs during summer months

329

corroborate well with the high AE440-870 and FMF500 represents the abundance of fine mode

330

aerosols. But in February, AE is relatively low (Fig. 4b) with the corresponding low FMF500 (Fig.

331

4d) likely to be associated with the contribution of coarse mode particles from dust sources (high

332

AOD) and mixing of dust with anthropogenic emissions. These coarse particles could be

333

eliminated due to rainout or washout processes as the study region experiences 83% of its total

334

precipitation in summer and spring seasons (Fig. 1i), which is consistent with the results reported

335

by Adesina et al. (2014) investigated for the same site with one year data retrieved during 2012.

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Additionally, meteorological factors play a crucial role in modulating aerosol loading

337

(Kumar et al., 2013). The high AOD in summer is attributed to enhanced AT and RH followed

338

by high PWC (Fig. 4c), which increases gas-to-particle conversion and hygroscopic growth of

339

aerosols leading to increased production of secondary coarse aerosols that are, in turn, removed

340

faster by wet deposition processes (Eck et al., 2005; Alam et al., 2011). Also, the increased solar 14

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radiation in summer favors the photochemical processes leading to the production of secondary

342

aerosols of anthropogenic origin (Kumar et al., 2009, 2013; Cheng et al., 2015; Kang et al., 2016;

343

Xia et al., 2016). Although, stronger deposition of aerosols due to enhanced precipitation in

344

summer (Fig. 1i), the stable weather conditions, aerosol hygroscopic growth, secondary aerosol

345

formation, photochemical processes, and pollutants from burning and dust cause aerosol

346

accumulation and then enhance loading (Che et al., 2015; Patel et al., 2017). Further, the

347

anticyclone effect raised in the southern Atlantic and Indian Oceans favors the occurrence of

348

lower AOD during autumn and winter seasons. Additionally, the lower AOD and AE during the

349

winter could be attributed to weak aerosol generation mechanism and diffusion of pollutants due

350

to strong winds (Fig. 1ii).

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Over the entire study period, the annual mean AE440-870 was noticed with a magnitude of

352

1.50 ± 0.26 (Fig. 3b). As it can be seen, this parameter appears more or less similar in all the

353

months throughout the year, with the mean values ranging between 1.41 (August) and 1.62

354

(December). The AE was found to be higher (lower) of 1.58 ± 0.28 (1.41 ± 0.25) in summer

355

(winter) followed by autumn with 1.52 ± 0.26 indicates the dominance of fine mode particles

356

relative to the coarse mode and vice versa. Lower AE440-870 values during winter signify

357

dominance of coarse mode particles (mainly sea salt aerosols) likely transported from the marine

358

environments, which is evident from the HYSPLIT model as previously discussed. The

359

investigations carried out by Olcese et al. (2014) over central Argentina at Cordoba-CETT

360

AERONET station and Bennouna et al. (2013) over AERONET’s Palencia site in north central

361

Spain followed a similar pattern of aerosol optical parameters for the period during 1999–2010

362

and 2003–2011, respectively.

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364

3.3. Identification of aerosol types and their contribution An investigation of major aerosol types found over Pretoria was carried out via the

366

relationship between AOD440 versus AE440-870. For more details about the relation between the

367

parameters, the readers are advised to refer section S1 and Fig. S1 in SM. This method has been

368

widely used in a number of studies (Kaskaoutis et al., 2009; Kumar et al., 2014a, 2017; Bibi et

369

al., 2016; Yu et al., 2016a; Patel et al., 2017) over different environments and is based on the

370

sensitivity of the two wavelength dependent parameters to different microphysical aerosol

371

properties. Fig. 5 shows the contour density maps to investigate and identify basic aerosol types

372

from AOD440 and AE440-870 at Pretoria for different seasons. Further, the details of the procedure

373

for constructing the graphs can be found elsewhere (Kaskaoutis et al., 2009; Kumar et al., 2014a,

374

2017; Bibi et al., 2016).

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A close examination of contour density plots revealed that the areas of maximum density

376

representing different aerosol types depending on the season. In all the seasons, it is evident that

377

the mixed type (MT) aerosols contributed larger with 47.6% in MAM and SON than all other

378

types of aerosols. Followed to this, the maximum density area was observed for the pair

379

(AOD440, AE470-870) = (>0.3, >1.0) during DJF and SON seasons indicative of moderate to high

380

turbid conditions under the influence of mixed aerosol field, with larger fraction of urban-

381

industrial and biomass burning (BU) anthropogenic fine aerosols contributing 26.94% and

382

24.35%, respectively. MAM and JJA presents the maximum density area with AOD440 = <0.15

383

and AE470-870 = >1.0 indicative of relatively low turbid (back ground) conditions under the

384

influence of clean continental (CC) type aerosols with a mixture of coarse and fine mode

385

particles. During JJA, the region bounded between AOD440 = <0.15 and AE470-870 = >1.0

386

represents more abundance of clean marine (CM) aerosol type among all other seasons

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contributing 6.13% to the total. The coarse mode desert dust (DD) type aerosols originated from

388

long-range transport were completely disappeared at Pretoria during the study period.

389

3.4. Temporal distributions of inversion products

390

3.4.1. Aerosol volume size distribution

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The monthly and seasonal mean values of AVSD during the period 2011–2015 measured

392

at Pretoria are shown in Fig. 6 and their statistics are tabulated in Table 1. The vertical bars

393

represent the standard deviation of mean which indicates the variability in size distribution within

394

a month. The size distributions were found to be bimodal logarithm structure with significant

395

variability in volume concentration and peak radius, which reflects the mixture of aerosol types

396

with the contribution of each mode varying with the month. A number of studies have showed

397

the suitability of bimodal lognormal function model for AVSDs (Dubovik et al., 2002, 2005; Eck

398

et al., 2003; Alam et al., 2011, 2014; Wang et al., 2015; Tiwari et al., 2016; Yu et al., 2016b; Bibi

399

et al., 2016; Mor et al., 2017; Patel et al., 2017; Adesina et al., 2014, 2017). It is evident that the

400

AVSDs showed a bimodal structure with distinct fine and coarse modes at radii of 0.15 µm

401

(except in February at 0.19 µm) and 3.86 µm (except in the months of SON at 5.06 µm),

402

respectively in all the months (Fig. 6a). Although the fine and coarse mode radii of AVSDs do

403

not change significantly in all the months, the fine-to-coarse fraction exhibits pronounced

404

variations which affect the large variability in AE.

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The seasonal variability in AVSDs (Fig. 6b) exhibited large similarities and is

406

characteristics of seasonally varying aerosol sources and types. As seen from Fig. 6b, the fine

407

modes reached the maximum peak at a radius of 0.15 µm (except in winter with an early peak at

408

a radius of 0.11 µm), whereas the coarse modes at a radius of 3.86 µm (except in spring at 5.06

409

µm) in all seasons. The higher volume concentration of 0.033 µm3 µm-2 in the fine mode during 17

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spring likely related to the frequent anthropogenic biomass burning and forest fires activities. The

411

noticeable peak in the coarse mode was found during this season attributed to the presence of

412

long-range transported mineral dust aerosols and/or sea salt aerosols at Pretoria, which coincides

413

with the lower values of AE440-870 (Fig. 4b) and back trajectories computed from the HYSPLIT

414

model (Fig. 2). Similar patterns of the AVSD for desert dust aerosols were obtained by Dubovik

415

et al. (2002). It should further be noted that the higher volume concentration in the coarse mode

416

was evident in winter (0.025 µm3 µm-2) than in summer (0.017 µm3 µm-2) and autumn (0.018

417

µm3 µm-2) seasons illustrates coagulation and hygroscopic growth of anthropogenic fine aerosols

418

(Table 1). Also, the low volume concentration of coarse mode particles in summer could be due

419

to the efficient removal of coarse particles by high precipitation over the region. It is noted that

420

Singh et al. (2004) and Alam et al. (2011) reported an increase in volume concentration of coarse

421

mode by 40-60% during the summer season. An interesting feature in summer is that the fine

422

mode radius is the largest indicating hygroscopic growth of fine mode particles as a result of the

423

higher RH and PWC leading to enhanced scattering by large size aerosols. Overall, the bimodal

424

patterns of the AVSD resulted from a number of factors including the mixing of air masses with

425

different aerosol pollutants, nucleation of fine aerosol particles, and hygroscopic growth of

426

particles in the atmosphere (Singh et al., 2004). Similar patterns of the AVSDs were examined by

427

Queface et al. (2011) and Adesina et al. (2014, 2017) reported the bimodal distribution with a

428

mixture of coarse particles over Skukuza and Pretoria.

429

3.4.2. Single scattering albedo

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The annual mean monthly variations of SSA-T at 440 nm (SSA-T440) measured at

431

Pretoria site during 2011–2015 is shown in Fig. 7a and its seasonal values are presented in Table

432

1. Theoretically, SSA increases with increasing wavelength for dust aerosols and decreases for 18

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biomass burning and urban-industrial aerosols, while it exhibits almost neutral spectral

434

dependence in the case of the aerosol mixture and sulfate particles (Dubovik et al., 2002; Eck et

435

al., 2003; Singh et al., 2004; Bergstrom et al., 2007). As reported by Dubovik et al. (2002), the

436

mean value of SSA at 870 nm is larger than 0.96 for dust; but variable between 0.85 and 0.96 for

437

urban-industrial locations. The annual mean of SSA-T440 is 0.91 ± 0.04, which is close to the

438

SSA found in other urban regions of Indo-Gangetic Plain (IGP) (Singh et al., 2004; Alam et al.,

439

2011; Bibi et al., 2016), lower to that noticed at Beijing (Gong et al., 2014; Xia et al., 2016),

440

Wuhan (Wang et al., 2015), and Shanghai (Cheng et al., 2015), and higher than that of rural and

441

background locations of SA (Adesina et al., 2017) and China (Che et al., 2015) (Table 2).

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The observed maximum monthly and seasonal values of SSA-T440 in summer could be

443

attributed to the abundance of anthropogenic aerosols and the high PWC resulted in the

444

hygroscopic growth of aerosols in the urban atmosphere (Dubovik et al., 2002; Singh et al.,

445

2004). However, the lower values observed in winter were strongly affected by a large amount of

446

black and organic carbon aerosols generated from combustion of fossil fuel and biomass burning.

447

The SSA-T440 (>0.9) during the late spring (November) could imply that urban-industrial aerosol

448

tends to contribute more to aerosol loading than biomass burning aerosol. An earlier study by

449

Singh et al. (2004) over Kanpur also found a similar variation in SSA suggested that the increase

450

in SSA during summer may also be attributed to the hygroscopic growth of water-soluble

451

aerosols under high PWC besides the long-range transport of dust. In contrast, during winter the

452

urban aerosols of the absorbing type were more dominant relative to the long-range transported

453

dust in Pretoria consequently leading to low SSA.

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456

3.4.3. Asymmetry parameter

The monthly pattern of ASY-T at 440 nm retrieved from AERONET’s Pretoria site

458

during 2011–2015 showed a high value with 0.71 ± 0.03 in February and very low value of 0.66

459

in October (Fig. 7b) attributed to more absorbing aerosols from biomass burning. The seasonal

460

averaged ASY-T values were found to be 0.70 ± 0.03, 0.69 ± 0.03, 0.67 ± 0.02, and 0.68 ± 0.03

461

during summer, autumn, winter, and spring seasons, respectively (Table 1). The higher values of

462

ASY-T440 noted in summer expressing the predominance of coarse mode particles in which fine

463

particles were present to some extent. However, the decrease in ASY-T values during winter

464

attributed to plenty of absorbing aerosols emitted from biomass burning suggesting a relative

465

abundance of fine mode particles. This is in good agreement and consistent with the previous

466

investigations reported by Adesina et al. (2014) over the same region. The greater decrease in

467

ASY-T was observed in spring indicating that the region is highly accumulated with the

468

anthropogenic absorbing aerosol pollutants throughout the study period. A similar variation in

469

ASY values which depend on the aerosol type as well as on seasonal variability was also

470

documented well by the previous authors over different urban regions (Singh et al., 2004; Alam

471

et al., 2011; Bibi et al., 2016; Xia et al., 2016; Yu et al., 2016a; Mor et al., 2017).

472

3.4.4. Real and imaginary parts refractive index

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The optical properties of aerosols are defined in terms of the refractive index (RI)

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obtained by combining real (R) and imaginary (I) parts of RI. Higher the value of imaginary part

475

of RI (IRI) indicates a higher absorption and increase in the real part of RI (RRI) represents

476

highly scattering aerosols (Singh et al., 2004). The monthly averaged RRI values at 440 nm

477

varied between 1.48 and 1.39 (Fig. 7c) representing higher RRI values in June with lower values

478

in February. The observed RRI was maximum during the winter (1.47 ± 0.06) followed by spring 20

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(1.44 ± 0.04), and minimum in the summer (1.42 ± 0.06) season (Table 1). The high RRI values

480

in winter and spring seasons infer more scattering type particles (mainly dust and sea salt

481

particles); as lower RRI in summer suggests the dominance of anthropogenic aerosols likely

482

related to enhance RH. Recently, Alam et al. (2014), Xia et al. (2016), and Yu et al. (2016b) have

483

also reported greater values of RRI for dust aerosols when compared to the anthropogenic

484

aerosols.

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The monthly mean values of IRI at 440 nm shown in Fig. 7d presented maximum during

486

the months of spring (September) with 0.022 and minimum of 0.004 during the summer period

487

(February). The highest IRI value of 0.018 ± 0.01 at 440 nm was observed during the winter

488

suggests the dominance of anthropogenic absorbing (black/organic carbon) aerosols. However,

489

low IRI in summer (0.007 ± 0.01) indicates the dominance of coarse dust particles, similar to that

490

noticed in the case of SSA-T440. Our measured values are analogous to the results of Eck et al.

491

(2003) and Adesina et al. (2014, 2017) over different regions of SA, and in other urban

492

environments (Singh et al., 2004; Alam et al., 2012, 2014; Bibi et al., 2016; Yu et al., 2016b).

493

3.5. Relationship of SSA-T with AOD440, AE440-870, and FMF500

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The relationship between SSA-T440 and AE440-870 as a function of FMF500 for different

495

AOD440 bin sizes are studied and is shown in Fig. 8. It is evident that the SSA-T440 for particles

496

with AE > 1.0 (fine mode) was greater than that of particles with AE ≤ 1.0 (coarse mode) when

497

AOD was less than 0.2. This represents higher scattering (absorption) due to abundant fine

498

(coarse) mode particles. Further, the SSA-T increased with increasing AOD for the particles with

499

AE > 1.0 suggesting a larger scattering ability of fine mode particles. When AOD is between 0.4

500

and 0.6, fine mode particles with AE > 1.0 (FMF500 > 0.8) become the main aerosol type in this

501

region and most of the particles have SSA-T greater than 0.9. The SSA-T was greater than 0.9 for

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all the particles when the AOD bin size varies from 0.6 to 1.0 and continued to increase with AE

503

(FMF500 > 0.9) for higher AOD. This could imply that the higher aerosol concentration is due to

504

the presence of anthropogenic fine aerosols (sulfates and nitrates). To confirm this, the detailed

505

study on the chemical composition of atmospheric aerosols should be conducted and analyzed in

506

future over this region. Gong et al. (2014) also demonstrated the strong scattering ability of

507

aerosol particles from anthropogenic emissions increases with AOD and AE, except for those due

508

to the strong dust outbreak events. Recently, similar studies were conducted over an urban region

509

in Central China (Wuhan) inferred that the anthropogenic aerosols have more scattering capacity

510

which increases with AOD (Wang et al., 2015). On the contrary, negative and positive

511

correlations of SSA with AE and AOD reported by Masoumi et al. (2013) at Zanjan (Iran)

512

indicates the role played by low absorbing and large size dust particles towards increasing AOD.

513

3.6. Aerosol radiative forcing and its efficiency

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By using radiative transfer module (Gracia et al., 2012), data retrieved from inversion

515

algorithm and a spherical fraction of particles, ARF (describing the direct effect of atmospheric

516

aerosols on solar radiation) was obtained. The ARF at BOA is mostly a function of AOD, while

517

ARF at TOA depends strongly on SSA and the surface albedo (Bergstorm et al., 2007). Fig. 9

518

illustrates the monthly mean variations in ARF at the BOA (ARFBOA), the TOA (ARFTOA), and

519

within the atmosphere (ARFATM) recorded over Pretoria during the period 2011–2015. The

520

corresponding seasonal mean values estimated from the monthly values are given in Table 1. The

521

annual mean ARF respectively at the TOA, BOA, and ATM retrieved over Pretoria was found to

522

be -10.91 ± 6.12, -30.73 ± 13.90, and +19.82 ± 8.66 W m-2. These ARF values were somewhat

523

lower compared to those found over urban sites in IGP, also affected by dust during the summer

524

and spring seasons (Singh et al., 2004; Alam et al., 2011; Bibi et al., 2016).

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The ARFTOA values were smaller than the values of ARFBOA due to the higher rate of

526

absorption of aerosol particles at the surface, reducing the solar energy available to be

527

backscattered to TOA (Xin et al., 2014; Che et al., 2015; Wu et al., 2015; Yu et al., 2016a; Patel

528

et al., 2017; Mor et al., 2017). During summer (February) higher values of ARF at the TOA (Fig.

529

9a) illustrate backscattered of radiation coincides with high SSA due to coarse mode dust

530

particles (Yu et al., 2016b). Both the ARF values at the TOA and BOA were negative in all the

531

months, particularly more negative during summer and spring months, indicating the significant

532

decrease of solar radiation reaching the ground (more scattering effect) and a net cooling due to

533

aerosol particles (Adesina et al., 2017). Overall, the net atmospheric forcing (the difference

534

between TOA and surface) was found to be positive in all the months and seasons indicate

535

resultant atmospheric heating due to strong absorption of radiation by aerosols produced from

536

anthropogenic activities (such as biomass burning) (Alam et al., 2011; Adesina et al., 2014; Che

537

et al., 2015; Patel et al., 2017; Mor et al., 2017). This is particularly pronounced during late

538

winter and spring seasons (August–October) suggests higher aerosol absorption due to biomass

539

burning and forest fire activities, which is a seasonal phenomenon over different regions in SA

540

(Queface et al., 2011; Adesina et al., 2014; Hersey et al., 2015).

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As pointed out by Garcia et al. (2012), ARF may provide the total radiative effect of

542

atmospheric aerosols but its efficiency may denote the rate at which the atmosphere is forced per

543

unit of AOD, since it is not dependent on AOD. The ARFE is appropriate in making a consistent

544

comparison of the radiative effects of solar radiation due to aerosol particles. Figs. 9d, 9e shows

545

the box-whisker plots of monthly mean changes in ARFEBOA and ARFETOA measured at Pretoria

546

and the corresponding seasonal mean values are listed in Table 1. The ARFE at the TOA showed

547

negative values during all the months with the highest value of -59.42 ± 15.68 W m-2 τ-1 during

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August and a minimum value of -92.33 ± 23.68 W m-2 τ-1 in January. Much higher negative

549

values of ARFE were observed at the BOA with a maximum value of 182.48 ± 38.21 W m-2 τ-1

550

during February and the minimum value in June with 268.36 ± 48.36 W m-2 τ-1. The lower AOD

551

in June leads to the higher value of forcing efficiency at the BOA. The negative magnitudes of

552

ARFEBOA values were higher in later winter and early spring (August and September) and lower

553

in the summer period at Pretoria; whereas the ARFETOA presents quite opposite pattern to that of

554

ARFEBOA. The higher values of negative ARFETOA and ARFEBOA values were probably caused

555

by an increase of backscattered and absorption of solar radiation by the atmospheric aerosols,

556

respectively.

557

3.7. Relationship of ARF with optical properties

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Fig. 10 shows the relationship between the daily averaged ARF (at the TOA, BOA, and

559

ATM) and the most important aerosol optical and physical properties such as AOD440, AE440-870,

560

SSA-T440, and ASY-T440 observed at Pretoria during the study period. The obtained ARF values

561

are highly dependent on the aerosol load and type, increasing as AOD increases. Fig. 10a shows

562

the linear fits of the ARF with the AOD440 retrieved at Pretoria. The ARFE, which is obtained

563

from the slope of these linear fits, was found to be comparable with the mean values of ARFE at

564

TOA, BOA, and in the ATM given in Fig. 9. The relationship between ARF and AOD at TOA,

565

BOA, and within the ATM showed significant linear correlations. The magnitudes of ARF

566

increased as the AOD increases represented with high correlation coefficients ranging between

567

0.80 and 0.93. The negative correlations at the TOA and BOA represent cooling of the surface,

568

with an overall positive correlation of forcing in the ARFATM corresponds to significant warming

569

in the atmosphere. Similar is the case observed in the AE440-870 at Pretoria during the study

570

period. In Pretoria, the ARF at TOA, BOA, and in the ATM peaked at an AE value of 1.5, which

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is higher than that reported over Beijing urban city (Gong et al., 2014; Xin et al., 2014). Due to

572

the differences in the aerosol sources, the strong absorption region of ARF is significantly

573

different in the AE. It is evident that (particularly during winter and spring) ARF in Pretoria was

574

much affected by the impact of locally produced mineral dust and sea salt particles when the AE

575

values were less than 0.7, as the remaining was under the influence of anthropogenic aerosol

576

emissions. However, positive and negative differences appeared in the ATM and BOA that was

577

dependent on the components and types of aerosols due to the differences in the aerosol sources.

578

The large warming effect in the atmosphere than that of cooling in the BOA is due to smoke and

579

soot particles led to positive ARF in the atmosphere-surface system. The observed large

580

differences in optical and radiative characteristics were due to anthropogenic (sulfate and nitrate)

581

and black/organic carbon aerosols.

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ARF also has a relationship with other important physical parameters such as SSA-T and

583

ASY-T retrieved at 440 nm. The presence of radiation throughout the atmosphere makes aerosols

584

with lower SSA due to strong absorption of radiation. This could lead to higher ARF and less

585

radiation at the surface. From Figs. 10c, 10d, it illustrates that there is also an increasing trend in

586

the ARF at the TOA, BOA, and in the ATM with decreasing daily mean values of SSA-T440 and

587

ASY-T440 implying more absorption of aerosols, and the aerosol warming of the atmosphere-

588

surface system shifted to cooling. In Pretoria, the ARF at the TOA, BOA, and in the ATM

589

peaked at the SSA-T440 and ASY-T440 values of 0.86 and 0.68, respectively, signifies that the

590

warming in the atmosphere-surface system was attributed to strong absorption due to smoke and

591

soot aerosols. As previously mentioned, the negative TOA forcing was larger in summer and

592

autumn than in spring and winter seasons. Additionally, higher amounts of scattered sulfate and

593

nitrate aerosols were noted in summer and autumn seasons at Pretoria caused by severe regional

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atmospheric pollution emitted through the region's rapid and large industrialization. However,

595

due to atmosphere-surface warming and cooling, a considerable amount of solar radiation

596

trapped inside the atmosphere is a significant source of heating, particularly within the lower

597

atmosphere (Li et al., 2010). Such trapping can increase atmospheric stability and influence

598

regional climate and environment (Xin et al., 2014).

599

4. Summary and conclusions

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594

Column-integrated aerosol optical and radiative properties were retrieved from the Cimel

601

(CE-318) automatic sun/sky radiometer installed at Pretoria (CSIR_DPSS) in northwest South

602

Africa were studied from the observations made during August 2011–December 2015. The

603

annual mean AOD440, AE440-870, and SSA-T440 estimated at Pretoria during the study period were

604

found to be 0.23 ± 0.13, 1.50 ± 0.26, and 0.91 ± 0.04, respectively. On the seasonal basis, high

605

AOD440 (low AE of 1.49 ± 0.26) observed during the spring season with 0.27 ± 0.17 indicate a

606

significant abundance of coarse mode relative to fine mode particles; while the high AOD and

607

AE of 0.24 ± 0.15 and 1.58 ± 0.28 during the summer season, respectively suggest a dominant

608

contribution from the anthropogenic sources. The HYSPLIT model derived trajectories indicated

609

that air masses mostly coming from desert or arid regions i.e., northwest and west of South

610

Africa during winter and spring seasons. Further, the air masses originated from the urban and

611

industrialized regions of northeast/easterly SA in summer and autumn seasons apparently

612

explains the relative contribution of transported anthropogenic aerosols towards the observation

613

site. The major aerosol types found during the entire study period were made of 4.66%, 25.79%,

614

23.99%, and 45.56% for the clean marine (CM), clean continental (CC), Urban/industrial and

615

biomass burning (BU), and mixed type (MT) aerosols, respectively. The retrieved aerosol optical

616

and physical properties showed strong seasonal variation and large differences in Pretoria

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affirming an overall dominance of fine mode relative to the coarse mode in the aerosol mixture

618

derived from multiple sources. Further, the values of ARF were also estimated using the radiative

619

transfer code retrieved from the measurements. The mean ARF retrieved from the sunphotometer

620

was found to be in the range from -6.12 ± 1.35 to -19.32 ± 5.64 W m-2 at the TOA and 19.68 ±

621

4.78 to 52.21 ± 14.92 W m-2 at the BOA; whereas, the resultant atmospheric forcing varied

622

between 9.92 ± 1.52 and 42.81 ± 11.68 W m-2. Large negative ARF was estimated at the BOA,

623

with relatively small values observed at the TOA. This caused a strong cooling effect on the

624

surface, but the resulting ARF showed warming within the atmosphere, potentially affecting the

625

regional climate and atmospheric environment.

626

Acknowledgments

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This work was supported by the National Natural Science Foundation of China (Grant No.

628

91644224), the Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological

629

Administration, NUIST (Grant No. KDW1404), the Natural Science Foundation of Jiangsu

630

Province (Grant No. BK20140996), and the National Research Foundation (NRF-South Africa)

631

bi-lateral research grant (UID: 78682). The authors are grateful to the PIs of AERONET site at

632

Pretoria_CSIR_DPSS and his assistants for the upkeep of the instrument and availability of the

633

online data. We also acknowledge the South Africa Weather Service (SAWS) for providing the

634

meteorological data used in this publication. Thanks are also due to the NOAA ARL for

635

computing backward trajectories using the HYSPLIT model. The authors would like to

636

acknowledge Prof. Alfred Weidensohler, the European Executive Editor of Journal and the two

637

anonymous reviewers for their helpful comments and constructive suggestions towards the

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improvement of an earlier version of the manuscript.

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Appendix A. Supplementary data

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Supplementary

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http://dx.doi.org/10.1016/j.envpol.2017.xx.xxx.

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SON 0.27 ± 0.17 1.49 ± 0.26 1.29 ± 0.48 0.76 ± 0.13 0.033 ± 0.007 0.026 ± 0.006 0.89 ± 0.03 0.68 ± 0.03 1.44 ± 0.04 0.014 ± 0.01 -40.01 ± 23.30 -11.51 ± 6.53 -240.32 ± 44.0 -68.64 ± 19.26

SC

Seasons MAM JJA 0.20 ± 0.11 0.20 ± 0.12 1.52 ± 0.26 1.41 ± 0.25 1.27 ± 0.48 0.69 ± 0.30 0.76 ± 0.12 0.74 ± 0.12 0.023 ± 0.003 0.023 ± 0.007 0.018 ± 0.004 0.025 ± 0.003 0.93 ± 0.03 0.86 ± 0.04 0.69 ± 0.03 0.67 ± 0.02 1.43 ± 0.05 1.47 ± 0.06 0.008 ± 0.01 0.018 ± 0.01 -25.67 ± 10.95 -33.50 ± 18.05 -10.40 ± 5.72 -8.63 ± 5.22 -215.18 ± 43.9 -262.11 ± 42.1 -81.22 ± 16.87 -63.72 ± 17.61

TE D

AOD440 AE440-870 PWC FMF500 Vol-f Vol-c SSA-T440 ASY-T440 RRI440 IRI440 ARFBOA ARFTOA ARFEBOA ARFETOA

DJF 0.24 ± 0.15 1.58 ± 0.28 2.02 ± 0.35 0.78 ± 0.14 0.028 ± 0.012 0.018 ± 0.008 0.94 ± 0.05 0.70 ± 0.03 1.42 ± 0.06 0.007 ± 0.01 -26.48 ± 13.65 -13.20 ± 8.65 -188.44 ± 50.1 -86.64 ± 18.65

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Table 1. Seasonal and annual mean variations of aerosol optical, physical, and radiative properties observed at CSIR_DPSS site in Pretoria during August 2011–December 2015. The value next to the mean corresponds to the standard deviation. The respective units for PWC, Vol, ARF, and ARFE are cm, µm3 µm-2, W m-2, and W m-2 τ-1. The remaining quantities are dimensionless.

ANN

0.23 ± 0.13 1.50 ± 0.26 1.32 ± 0.40 0.72 ± 0.19 0.027 ± 0.008 0.021 ± 0.006 0.91 ± 0.04 0.69 ± 0.03 1.44 ± 0.06 0.012 ± 0.01 -30.73 ± 13.90 -10.91 ± 6.12 -225.06 ± 44.7 -75.51 ± 17.6

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Period AOD500 AE440-870 2007-2012 0.64 0.88 2014-2015 0.43 0.69 2004-2012 0.66 0.71 2013 0.72 0.83 2007-2013 1.15 1.21 2004-2011 0.41 1.28 0.72 1.21 2010-2012 2009-2013 0.59 0.94 1999-2010 0.10 1.20 2012 0.22* 1.60 1.40 1999-2010 0.25* 2011-2015 0.23* 1.50

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Type Urban Rural, Arid Urban Semi-urban Urban Urban Urban Rural Urban Urban Rural Urban

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Site Lahore Desalpar Jodhpur Rohtak Wuhan Beijing Shanghai Shenyang Argentina Pretoria Skukuza Pretoria

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Citation Bibi et al. (2016) Patel et al. (2017) Bhaskar et al. (2015) Mor et al. (2017) Wang et al. (2015) Gong et al. (2014) Cheng et al. (2015) Che et al. (2015) Olcese et al. (2014) Adesina et al. (2014) Adesina et al. (2017) Present Study

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Table 2. Statistical comparison of mean AOD, AE, SSA-T, and ASY-T derived from automatic sun/sky radiometer at different environments. The AOD values denoted with asterisk (*) are given at 440 nm.

SSA-T440 0.89 0.92 0.89 0.91 0.90 0.89 0.91 0.86 0.88 0.91 0.93 0.91

ASY-T440 0.71 0.74 0.71 0.68 0.69 0.71 0.69

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>= 6.5 6.0 - 6.5 5.5 - 6.0 5.0 - 5.5 4.5 - 5.0 4.0 - 4.5 3.5 - 4.0 3.0 - 3.5 2.5 - 3.0 2.0 - 2.5 1.5 - 2.0 1.0 - 1.5 0.5 - 1.0 0.0 - 0.5

SE SSW

S

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Fig. 1. (i) Annual variations in monthly and seasonal mean values of AT, RH, WS, and TP. The vertical bars represent the standard deviation. (ii) Wind rose plot showing wind direction observed at the Pretoria for four seasons along with the wind speed during the study period. The wind speed is indicated with the color scale. Each circle represents percentage of occurrences of WS (2% each for all the seasons). The four seasons considered in this study are (a) summer (DJF), (b) autumn (MAM), (c) winter (JJA), and (d) spring (SON).

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Fig. 2. Seven days cluster backward trajectories arriving from different regions at 1500 m AGL over Pretoria during 2011–2015.

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80 70 60 50 40 30 20 10

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Fig. 3. (a-c) Frequency of occurrence (in %) along with the cumulative frequency are represented for different optical properties. (d) Seasonal and annual mean spectral variations of AOD observed during the study period measured at Pretoria. The annual mean and standard deviation values of optical parameters and count (N) are also given inside the panels.

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Relative frequency (%)

0 0 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 AOD440 110 20 (c) PWC = 1.32 + 0.40 (N = 1217) 100 18 90 16

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Cumulative frequency (%) AOD

40

2.6 2.4 2.2 2.0 1.8 1.6 1.4 1.2 1.0 0.8 0.6 0.4 0.2 0.0

1.1 (b) AE440-870 = 1.50 + 0.26

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Fig. 4. Box-whisker plot showing month-to-month variations of (a) AOD440, (b) AE440-870, (c) PWC, and (d) FMF at AOD500. The vertical lines represent the standard deviation from the mean. Each box represents 25th and 75th percentiles and the whiskers represent the 5th and 95th percentiles. The solid circle inside each box represents the mean value and the horizontal line represents the median value. The solid diamonds above and below the boxes indicate the maximum and minimum values, respectively. Whereas, the crosses represent 1st and 99th percentile values of the dataset. The annual mean (± standard deviation) values for each parameter observed during the study period are also given in the respective panels.

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PWC (cm)

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(d) FMF500 = 0.72 + 0.19

(c) PWC = 1.32 + 0.40 cm

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-0.1 5.0

JUN

AOD 440

0.7

FMF500

(a) AOD440 = 0.23 + 0.13

MAY

1.0

AE440-870

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18

1.4

BU (26.94%)

12

1.2

10

1

8

MT (45.21%) CM (3.65%)

0.4

6

2

DD (0%) 0.2

0.4

0.6

0.8

1

12 10

1.2

8

1

0.6

0

0.4

MT (47.62%)

DD (0%)

6 4 2 0

AOD (440 nm)

1.6

(c)

2

36 30 27 24

1.4

21

BU (17.27%)

1.2

18 15

1

12

MT (45.68%)

CM (6.13%)

0.8

9

EP

6 3

DD (0%)

0.2

0.4

AC C

0

1.8

33

TE D

1.8

0.6

0.8

AOD (440 nm)

0.2

0.4

0.6

0.8

CC (22.28%)

2.2

CC (30.92%)

2

0

1

AOD (440 nm)

2.2

0.2

14

BU (23.41%)

1.6

(d)

1.4

BU (24.35%)

1.2 1

MT (47.67%)

0.8

CM (5.7%)

0

0.4

16

0.2

0.2

0.6

18

1.4

0.8

4

20

M AN U

0.6

AE (440-870 nm)

14

1.6

(b)

RI PT

20

16

0.8

1.8

AE (440-870 nm)

AE (440-870 nm)

1.6

22

CM (3.17%)

1.8

2

AE (440-870 nm)

2

CC (25.79%)

CC (24.2%)

2.2

(a)

SC

2.2

0.6

0

0.4

30 28 26 24 22 20 18 16 14 12 10 8 6 4 2 0

DD (0%)

0.2 1

0

0.2

0.4

0.6

0.8

1

AOD (440 nm)

Fig. 5. Contour density maps to present different aerosol types and their contributions in different seasons from AOD versus AE relationship observed at Pretoria. CM–clean marine; CC–clean continental; BU–urban/industrial and biomass burning; DD–desert dust; MT–mixed type. The presentation of seasons in panels (a-d) is same as given in Fig. 2.

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Jan Apr Jul Oct

(a)

0.05

Feb May Aug Nov

Mar Jun Sep Dec

RI PT

0.06

dV/dlnr (µm3 µm-2)

0.04 0.03

SC

0.02 0.01

M AN U

0.00 0.05

DJF MAM JJA SON ANN

(b)

-2

dV/dlnr (µm µ m )

0.04

3

0.03

0.01 0.00

TE D

0.02

EP

0.1

1 Radius (µ µm)

10

AC C

Fig. 6. Monthly (a) and seasonal (b) variations of aerosol volume size distributions observed at AERONET’s Pretoria CSIR_DPSS site during 2011–2015.

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0.80 (b) ASY-T440 = 0.69 + 0.03

0.96 0.93 SSA-T440

0.90 0.87

0.81 0.78

0.74 0.72 0.70 0.68 0.66 0.64 0.62 0.60

M AN U

0.75 0.72 1.65

0.58 0.050

(d) IRI440 = 0.012 + 0.01

(c) RRI440 = 1.44 + 0.06

1.59 1.56 1.53 1.50 1.47

0.045 0.040 0.035 0.030 0.025 0.020 0.015 0.010 0.005 0.000 DEC

NOV

OCT

SEP

AUG

JUL

JUN

MAY

APR

MAR

FEB

-0.005 JAN

DEC

NOV

EP

JUN

MAY

APR

MAR

FEB

1.29

OCT

1.32

SEP

1.35

AUG

1.38

JUL

1.41

TE D

1.44

JAN

Fig. 7. The monthly variations of almucantar scan inversion products (a) SSA-T, (b) ASYT, (c) RRI, and (d) IRI derived at 440 nm observed over Pretoria during 2011-2015. The representation of box-whisker plots shown in all panels is same as in Fig. 4. The annual mean (± standard deviation) values for each parameter observed during the study period are also given in the respective panels.

AC C

RRI440

0.76

SC

0.84

1.62

0.78

ASY-T 440

(a) SSA-T440 = 0.91 + 0.04

IRI440

0.99

RI PT

1.02

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1 .02 0 .99

0 < AOD440 < 0.2 (r = 0.49)

0.2 < AOD440 < 0.4 (r = 0.58)

0.4 < AOD440 < 0.6 (r = 0.73)

0.6 < AOD440 < 1.0 (r = 0.29)

0 .96

0 .90

RI PT

SSA-T44 0

0 .93

0 .87 0 .84 0 .81 0 .78 0 .75

0 .99

SC

0 .72 1 .02

SSA-T4 40

0 .93 0 .90 0 .87 0 .84 0 .81 0 .78 0 .75 0 .72 0.6

0 .8

1.0

1 .2

1 .4

1.6

1 .8

M AN U

0 .96

2.0

2 .2 0 .6

0 .8

1.0

1 .2

AE4 40-8 70

1.4

1 .6

1 .8

2.0

2 .2

TE D

AE4 40-8 70

1.02 0.99

0 < AOD 440 < 1.0 (r = 0.51)

0.96 0.93

FMF500

0.87

1.0 0.9 0.8 0.8 0.7 0.6 0.5 0.4 0.4 0.3 0.2

SSA-T 440

EP

0.90

0.84

AC C

0.81 0.78 0.75

0.72 0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

2.2

AE440-870

Fig. 8. Density scatter plots of SSA-T440 with AE440-870 to show their relationship for different AOD440 bins as a function of FMF500 measured at Pretoria CSIR_DPSS site. The obtained Pearson’s coefficient (r) for the correlation through linear regression fit (solid line) is also shown in each panel. The magnitudes of solid circles which represent FMF are given with a color scale shown in the bottom panel.

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10

-2

-2

40

-1

(d) ARFETOA = -75.51 + 17.61 W m τ

(a) ARFTOA = -10.91 + 6.12 W m

20

-20

RI PT

-60 -80

-100 -120

TE D

-140 -160 120

-2

-100

100

-200 -250 -300 -350

-450

-2

-500 600

-1

(f) ARFEATM = 149.55 + 40.48 W m τ

500 400 300 200 100

DEC

NOV

OCT

SEP

AUG

JUL

JUN

APR

MAR

DEC

NOV

OCT

0 SEP

JUL

JUN

MAY

APR

MAR

JAN

0

FEB

20

AC C

40

AUG

60

-1

-400

EP

-2

80

-150

-2

(c) ARFATM = 19.82 + 8.66 W m

FEB

-2

-140 -50

-1

-1

-40

ARFBOA (W m )

-120

M AN U

-20

ARF ATM (W m )

-2

-100

(e) ARFEBOA = -225.06 + 44.71 W m τ

(b) ARFBOA = -30.73 + 13.90 W m

JAN

0

-2

-80

ARFEATM (W m τ )

-50 20

SC

-40

-60

-2

-30

-40

ARFEBOA (W m τ )

-20

MAY

-2

ARF TOA (W m )

-10

-2

-1

0

ARFETOA (W m τ )

0

Fig. 9. Same as in Fig. 4, but for ARF (a-c) and ARFE (d-f) at the TOA, BOA, and ATM retrieved from the AERONET measured at Pretoria. The annual mean (± standard deviation) values for each parameter observed during the study period are also given in the respective panels.

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120

(a)

90

(b) y = 17.01x - 2.01; r = 0.29 y = -33.11x + 15.49; r = -0.45 y = -16.12x + 13.49; r = -0.67

ATM; y = 87.04x + 4.77; r = 0.81 BOA; y = -128.07x - 6.46; r = -0.93 TOA; y = -41.03x - 1.69; r = -0.92

RI PT

150

ARF (W m-2)

60 30 0 -30

SC

-60 -90

-150 0.0 150

0.2

0.4

0.6 AOD440

0.8

(c) y = -84.76x + 97.54; r = -0.29 y = 27.38x - 57.55; r = 0.11 y = -57.38x + 39.99; r = -0.48 90 60 30

-30 -60 -90 -120

1.0

1.2 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 AE440-870 (d) y = -73.26x + 72.93; r = -0.14 y = 33.31x - 56.13; r = 0.05 y = -39.96x + 16.81; r = -0.19

TE D

0

EP

-150 0.65 0.70 0.75 0.80 0.85 0.90 0.95 1.00 1.05 0.580.600.620.640.660.680.700.720.740.760.780.80 SSA-T440 ASY-T440

Fig. 10. Relationships between ARF measured at TOA, BOA, and ATM with AOD440 (a), AE440-870 (b), SSA-T440 (c), and ASY-T440 (d) observed from the daily average level 2.0 inversion products. The regression coefficients and Pearson’s correlation coefficient (r) obtained from the linear regression analysis are also shown in all the panels.

AC C

ARF (W m -2)

120

M AN U

-120

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HIGHLIGHTS Aerosol optical and physical properties exhibited prominent seasonal variations in Pretoria. High AOD in summer and spring seasons attributed to abundance of anthropogenic aerosols.

RI PT

Low SSA-T in winter is due to large presence of absorbing type aerosols.

Negative ARFBOA results in cooling the surface due to strong absorption by anthropogenic aerosol particles.

AC C

EP

TE D

M AN U

SC

ARF is highly dependent on the aerosol load and type, increasing with increase in AOD.