Radiative response of biomass-burning aerosols over

Science of the Total Environment 633 (2018) 892–911

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Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv

Radiative response of biomass-burning aerosols over an urban atmosphere in northern peninsular Southeast Asia Shantanu Kumar Pani a, Neng-Huei Lin a,⁎, Somporn Chantara b,⁎, Sheng-Hsiang Wang a, Chanakarn Khamkaew b, Tippawan Prapamontol c, Serm Janjai d a

Cloud and Aerosol Laboratory, Department of Atmospheric Sciences, National Central University, Taoyuan 32001, Taiwan Environmental Science Research Center, Faculty of Science, Chiang Mai University, Chiang Mai 50200, Thailand Environment and Health Research Unit, Research Institute for Health Sciences, Chiang Mai University, Chiang Mai 50200, Thailand d Department of Physics, Faculty of Science, Silpakorn University, Nakhon Pathom 73000, Thailand b c

H I G H L I G H T S

G R A P H I C A L

A B S T R A C T

• Impacts of BB on aerosol properties were investigated over Chiang Mai's urban atmosphere during 7-SEAS/BASELInE 2014. • Detailed radiation budget over BB sway urban site was quantified for the first time using in-situ datasets. • Atmospheric heating rate was estimated as high as 3.6 K d−1. • Large surface cooling and atmosphere warming was due to enhanced atmospheric absorption. • Severe haze episode linked to BB in northern PSEA can cause severe health impacts and modify the regional climate.

a r t i c l e

i n f o

Article history: Received 10 December 2017 Received in revised form 17 March 2018 Accepted 18 March 2018 Available online 28 March 2018 Editor: Jianmin Chen Keywords: 7-SEAS/BASELInE Biomass burning Urban Haze episode Radiative impacts

a b s t r a c t A large concentration of finer particulate matter (PM2.5), the primary air-quality concern in northern peninsular Southeast Asia (PSEA), is believed to be closely related to large amounts of biomass burning (BB) particularly in the dry season. In order to quantitatively estimate the contributions of BB to aerosol radiative effects, we thoroughly investigated the physical, chemical, and optical properties of BB aerosols through the integration of ground-based measurements, satellite retrievals, and modelling tools during the Seven South East Asian Studies/Biomass-burning Aerosols & Stratocumulus Environment: Lifecycles & Interactions Experiment (7-SEAS/ BASELInE) campaign in 2014. Clusters were made on the basis of measured BB tracers (Levoglucosan, nss-K+, and NO− 3 ) to classify the degree of influence from BB over an urban atmosphere, viz., Chiang Mai (18.795°N, 98.957°E, 354 m.s.l.), Thailand in northern PSEA. Cluster-wise contributions of BB to PM2.5, organic carbon, and elemental carbon were found to be 54–79%, 42–79%, and 39–77%, respectively. Moreover, the cluster-wise aerosol optical index (aerosol optical depth at 500 nm ≈ 0.98–2.45), absorption (single scattering albedo ≈0.87– 0.85; absorption aerosol optical depth ≈0.15–0.38 at 440 nm; absorption Ångström exponent ≈1.43–1.57), and radiative impacts (atmospheric heating rate ≈1.4–3.6 K d−1) displayed consistency with the degree of BB. PM2.5 during Extreme BB (EBB) was ≈4 times higher than during Low BB (LBB), whereas this factor was ≈2.5 for the magnitude of radiative effects. Severe haze (visibility ≈ 4 km) due to substantial BB loadings (BB to PM2.5 ≈ 79%) with favorable meteorology can significantly impact the local-to-regional air quality and the,

⁎ Corresponding authors. E-mail addresses: [email protected] (N.-H. Lin), [email protected] (S. Chantara).

https://doi.org/10.1016/j.scitotenv.2018.03.204 0048-9697/© 2018 Elsevier B.V. All rights reserved.

S.K. Pani et al. / Science of the Total Environment 633 (2018) 892–911

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daily life of local inhabitants as well as become a respiratory health threat. Additionally, such enhancements in atmospheric heating could potentially influence the regional hydrological cycle and crop productivity over Chiang Mai in northern PSEA. © 2018 Elsevier B.V. All rights reserved.

1. Introduction Particulate matter (PM) of either, natural and anthropogenic origin, is a significant worldwide environmental issue and well known to cause harmful effects on human health (Harrison and Yin, 2000; Metzger et al., 2004) and visibility (Tao et al., 2009). It also plays a crucial role in the local and regional air quality and represents a critical factor in solar radiation (Crutzen and Andreae, 1990; Andreae, 1993) and climate change (Novakov and Penner, 1993; Kanakidou et al., 2005). Many countries worldwide have adopted air-quality standards for ambient PM10 (cut sizes ≤ 10 μm) and PM2.5 (cut sizes ≤ 2.5 μm) and started monitoring projects to identify and quantify the sources in order to reduce their emissions. Biomass burning (BB) is a widespread routine practice for land conversion and land clearing by deforestation and the burning of secondary forests and pastures (Tsay et al., 2013) in many regions. BB emits substantial amounts of trace gases and PM into the atmosphere (Jian and Fu, 2014), and its contribution to the carbonaceous aerosol at urban sites (Fine et al., 2001; Lanz et al., 2008) has been identified to be significant. Carbonaceous aerosol mainly consists of light-absorbing organic carbon (OC) and elemental carbon (EC) and has recently been of great concern because of its significant impact on regional-to-global climate (Cao et al., 2004; Kanakidou et al., 2009). The magnitude of the scattering and absorption of BB-derived carbonaceous aerosols, responsible for both cooling and warming effects on the climate primarily depends upon the fuel type, combustion phase, environmental conditions, and atmospheric aging. The radiative effects of these particles have remained poorly quantified due to their diverse optical and cloud-activating properties (Vakkari et al., 2014). Boucher et al. (2013) recently reported the radiative forcing of EC and organic aerosols associated with BB as +0.0 (−0.2 to +0.2) W m−2. Over peninsular Southeast Asia (PSEA, here defined as Vietnam, Cambodia, Thailand, Laos, and Myanmar), large BB occurs annually in the dry season (February–April) due to slash-and-burn and land-clearing practices before the local growing season (Fox et al., 2009; Tsay et al., 2013; Gautam et al., 2013; Jian and Fu, 2014). This BB builds the regional haze generally known as “Asian Brown Cloud” (Ramanathan and Crutzen, 2003). Recently the 7-SEAS/BASELInE (Seven South East Asian Studies/Biomass-burning Aerosols & Stratocumulus Environment: Lifecycles & Interactions Experiment; Lin et al., 2014) campaign was conducted over northern PSEA during the dry seasons from 2013 till 2015 to explore numerous key atmospheric processes and the surface/ atmosphere radiation budget associated with the regional BB (Lin et al., 2013; Tsay et al., 2016). Within this framework, some studies have emphasized the BB emissions from northern PSEA and explained their impact on the atmospheric composition, regional air quality, aerosol optics, and regional climate (e.g., Lin et al., 2013, 2014; Tsay et al., 2013, 2016). Prior to this campaign, Gautam et al. (2013) reported the aerosol characterization and satellite-based aerosol radiative impact over northern PSEA during the dry seasons of 2008 and 2009. The enhancement of free tropospheric warming by the transported BB plumes over northern South China Sea from northern PSEA was quantified during the 7-SEAS/Dongsha Experiment (Pani et al., 2016a). Wang et al. (2015) investigated the distribution of aerosol optical properties over northern PSEA during 7-SEAS/BASELInE 2014 and reported their distinct variability over different locations based on the meteorological conditions, fuel type, site elevation, and proximity to BB sources. Pani et al. (2016b) provided a detailed estimation of BB radiative effects for

near-source BB aerosols but was limited to a mountainous location in northern PSEA during 7-SEAS/BASELInE 2013. However, the BB radiative impact varies greatly between different source regions and even different locations in the same region due to their heterogeneity in mass loadings, the optical properties and their vertical distributions. The aerosol vertical distribution is uneven regionally (Toth et al., 2016) and can contribute to the uncertainty in the radiative-forcing estimations (Choi and Chung, 2014). The focus site of this study is the city of Chiang Mai, a northern urban center located in Chiang Mai Province (17–21°N and 98–100°E) in northern PSEA. This province is the second largest in northern Thailand and covers approximately 20,170 km2 in area with a population of about 1,682,164 inhabitants as of June 2015 (http://www.Chiang Mai. go.th/); it also attracts over 7 million visitors each year. Owing to its geographical features, this city faces serious air-quality degradation, especially during the dry season. Chiang Mai is typically influenced by medium-traffic vehicular emissions with mixed residential, commercial, and industrial emissions, and anthropogenic activities (Chantara et al., 2012; Tsai et al., 2013; Janta and Chantara, 2017). Moreover, a wide range of BB activities over the region, particularly in the dry season deteriorates the air quality of the city and coincides with the peak of the annual haze episode. This region is also tempered by a low latitude and moderate elevation, which makes the atmospheric boundary layer (ABL) more complex. Wang et al. (2015) reported the presence of widespread smoke haze over the region in 2014 when there was likely a significant contribution from BB to the carbonaceous-aerosol (mainly EC or black carbon [BC]) loading in the atmosphere, but the radiative effects have not yet been fully characterized. Hence, this study seeks to estimate the radiative effects over the urban atmosphere of Chiang Mai by using the data obtained during the 7-SEAS/BASELInE 2014 campaign. In the present study, we use a synergistic approach to characterize the surface chemistry, optical properties, vertical distributions, and shortwave direct radiative effects of aerosols during the BB-dominated period over Chiang Mai's atmosphere by integrating ground-based measurements, satellite retrievals, and a radiative transfer model. Descriptions of the measurements made and methods used in the current analysis are provided in Sections 2 and 3, respectively. The analyses of the aerosol distribution with regard to the compositions, optical properties, vertical profiles, and radiative impacts are carefully investigated in Section 4; their implications for the regional air quality and climate are discussed in Section 5, and finally, a summary is given in Section 6. 2. Measurements and data 2.1. Aerosol sampling and chemical analysis Aerosol sampling was carried out on the rooftop of a four-story building at Chiang Mai University (CMU; 18.795°N, 98.957°E, 354 msl) in Chiang Mai, Thailand. The CMU site is located about 2 km west of the city of Chiang Mai. Details of the sampling procedures, analytical methods, quality control, and chemical analysis can be found in Khamkaew et al. (2016). Briefly, 24-h PM2.5 samples were collected gravimetrically from 8 March till 7 April 2014 (number of samples, N = 31) by using both Teflon (Whatman's, UK, 2 μm, Ø = 46.2 mm) and quartz-fiber (Whatman's, UK, Ø = 47 mm) filters in mini-volume air samplers (MiniVol, Airmetrics, USA) at a flow rate of 5 L min−1. PM2.5 samples were analyzed for water soluble inorganic ions (WSIIs; + 2+ 2− Na+, NH+ , Cl−, NO− 4 , K , Ca 3 , and SO4 ) by ion chromatography

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(882 Compact IC plus, Metrohm, Switzerland), for trace metals (Al, Cu, K, Mg, Mn, Ni, and Zn) by inductively coupled plasma optical emission spectrometery (ICP-OES; Optima 3000, Perkin Elmer, Germany), and for LG by gas chromatography with a flame ionization detector (GCFID). In addition to the abovementioned PM2.5 chemical measurements, simultaneously measured concentrations of various gaseous pollutants (CO, NO, NOx, NO2, SO2, and O3) along with PM2.5 and PM10 (measured by an automotive Tapered Element Oscillation Microbalance; TEOM) were obtained from the Air Quality Monitoring Station, Pollution Control Department Thailand (PCD; located in Yupparaj Wittayalai School about 5 km east of the CMU site; http://www.pcd.go.th/). Various meteorological parameters, viz., the temperature (T), relative humidity (RH), wind speed (WS), wind direction (WD), surface pressure (P), and visibility (VIS), were obtained from the Chiang Mai Meteorological Station, Thai Meteorological Department (TMD; located about 5 km southeast of the CMU site; https://www.tmd.go.th/en/). 2.2. Ground-based measurements of aerosol optical and microphysical properties Direct-sun measurements of the columnar aerosol optical depth (AOD) in different spectral channels (440, 500, 675, 870, and 1020 nm), recorded using a Cimel sun–sky radiometer at the Chiang Mai Met Station (Holben et al., 1998), were obtained from the Aerosol Robotic Network (AERONET; http://aeronet.gsfc.nasa.gov/). The uncertainty in the measured AODs was 0.01–0.02 and attributed primarily to the calibration uncertainty (Eck et al., 1999). The spectral AOD is represented by AODλ, where λ is the wavelength in nanometers (nm), used to indicate a spectrally varying quantity. The Ångström exponent (AE), the measure of the relative dominance of fine-mode aerosols over the coarse-mode, was estimated in this study as follows: AE ¼ −

logðAOD440 =AOD870 Þ logð440=870Þ

ð1Þ

It is also a qualitative indicator of the spectral dependence of AOD and aerosol size distributions (Pani and Verma, 2014; Verma et al., 2014). The fine-mode fraction of AOD (FMF) was estimated as the ratio of fine-mode AOD to total AOD at the 500 nm wavelength. The total columnar water vapor (CWV) in centimeters (cm) was obtained at the 940 nm wavelength. In addition to direct-sun measurements, the sun–sky radiometer also measures the sky radiance at four wavelength channels (440, 675, 870, and 1020 nm) along the solar almucantar (i.e., at a constant solar zenith angle, with varied azimuth angles). The sky-radiance measurements were used to retrieve additional columnar aerosol properties, viz., particles' effective radius (reff), volume size distribution, refractive index real part (REFR), refractive index imaginary part (REFI), singlescattering albedo (SSA), and asymmetry parameter (AP), using AERONET inversion algorithms (Dubovik and King, 2000; Dubovik et al., 2006; Holben et al., 2006). The uncertainty of the retrieved inversion product was ±0.03 (Holben et al., 2006) and specifically, the accuracies of SSA440 and AP440 were ± 0.03 and ± 0.02, respectively. The associated error with the REFI retrievals was about 10% for AERONET (Dubovik et al., 2000) in the most agreeable and error-free environments (neither systematic nor random errors). The aerosol particle size distribution over Chiang Mai was derived by the inversion method as described in Dubovik and King (2000) and Dubovik et al. (2000). The volume size distribution was estimated at 22 radius bins ranging from 0.05 to 10 μm. For each mode, the log-normal distribution can be written as follows: "

dV Cv 1 ¼ pffiffiffiffiffiffi exp − d ln r σ 2π 2

ln ðr=r v Þ σ

2 # ð2Þ

where ðd dV Þ is the aerosol particle size distribution, Cv is thecolumnar ln r volume of particles per unit cross section of the atmospheric column, r is the particle radius, rv is the volume median radius, and σ is the standard deviation. The absorption AOD (AAOD) at 440 nm was estimated as follows: AAOD440 ¼ AOD440 ð1−SSA440 Þ

ð3Þ

Similar to AE, the aerosol absorption's dependency on wavelength i.e., the absorption Ångström exponent (AAE), was estimated as follows: AAE ¼ −

logðAAOD440 =AAOD870 Þ logð440=870Þ

ð4Þ

The absorption-related inversion products (e.g., SSA, AAOD, and AAE) were retrieved in the current study when the direct-sun AOD440 was N0.4. In addition, quality-assured and cloud-screened AERONET Version 2 Level 2 direct-sun (Smirnov et al., 2000) and inversion products (Dubovik et al., 2000; Holben et al., 2006) were used in this study. 2.3. Satellite-based observations In order to check the validation of satellite retrievals over Chiang Mai, the daily columnar AOD was obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS; http://ladsweb.nascom.nasa. gov/) Level 2 Aqua 3-km atmospheric product (MYD04_3K; collection 6, quality flag 3) and Terra daily aerosol product (MOD04_L2; collection 6; 10 × 10 km). Remer et al. (2013) reported an error of ±0.05 ± 0.20 AOD for the global 3-km MODIS-AOD land product. The fire-count data was obtained from the Fire Information for Resource Management System (FIRMS; http://firms.modaps.eosdis.nasa.gov/firemap/) in order to understand the exact information about BB over the study site. Fig. 1a shows the study location with the fire hotspots occurring in and around the area. The columnar ozone was obtained from an Ozone Mapping Instrument (OMI; http://gdata1.sci.gsfc.nasa.gov/). The surface-reflectance data at seven different wavelengths (469, 555, 645, 858.5, 1240, 1640, and 2130 nm) were also retrieved from the MODIS land-surface products. In order to understand aerosol vertical distributions, vertical profile of aerosol extinction coefficients at 532 nm was obtained from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) data archive during the study period (14, 23, and 30 March; daytime data and 21 March and 6 April; nighttime data). The Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) sensor onboard CALIPSO satellite precisely characterizes the aerosol profile using cross-polarized and parallel sections of the 532 nm return signal (Kittaka et al., 2011). In addition, the ABL height was also separately procured from the Global Data Assimilation System (GDAS), National Oceanic and Atmospheric Administration, Air Resources Laboratory (NOAA–ARL) using the Real-time Environmental Applications and Display System (READY) website (http://www.arl.noaa.gov/ready/; Draxler and Rolph, 2003). 3. Methodologies 3.1. Estimation of non-seas-salt components The concentrations of non-sea-salt (nss) components in WSIIs were estimated by using Na+ as a sea-spray marker (Keene et al., 1986) with the following equations (Berg and Winchester, 1978) h

i h i nss−SO4 2− ¼ SO4 2− −0:25 Naþ

nss−Kþ ¼ Kþ −0:036 Naþ

ð5Þ ð6Þ


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Fig. 1. (a) MODIS fire counts over PSEA and (b) Wind-rose diagram depicting surface wind direction and wind speed over Chiang Mai during the study period.

h

i h i nss−Ca2þ ¼ Ca2þ −0:038 Naþ

ð7Þ

Suthep Mountain in Chiang Mai Province in March and April 2010 (Chuang et al., 2013). The CMU site is located to the east, about 20 km from this mountain site.

OC BB ¼ ½LG

OC LG BB

ð8Þ

EC BB ¼ ½LG

EC LG BB

ð9Þ

3.2. Estimation of OC and EC concentrations from the emission ratios Due to the unavailability of OC and EC observations during the sampling period, we made an attempt here to estimate them from the emission ratios available in literature. The choice of the emission ratio is critical to derive the OC and/or EC from BB and/or anthropogenic emissions. Thus, we selected emission ratios from the geographical region as the source region for the CMU site with respect to emissions from both BB and anthropogenic sources. The mean resultant wind direction (Fig. 1b) over CMU, as depicted in the wind-rose diagram, was from the north/northeast, indicating that the site was downwind of the nearsource BB (i.e., DAK and Southep Mountain in Chiang Mai Province, Thailand). The surface winds crossed mountains and forests located to the north and northwest of Chiang Mai before entering the city (Tsai et al., 2013). Moreover, the CMU site also received higher winds from central Thailand (Fig. 1b). The OC and EC concentrations from BB (i.e., OCBB and ECBB) were estimated (e.g., Puxbaum et al., 2007; Zhang et al., 2008; Yttri et al., 2014) from the measured surface concentrations of LG and the emission ratios EC for ðOC LG ÞBB and ðLGÞBB , i.e., 16.1 and 2.9, respectively, as reported over

As CMU is an urban location, the anthropogenic influence cannot be neglected. The OC and EC concentrations from anthropogenic sources (i.e., OCANTHRO and ECANTHRO) were estimated from the measured concentrations of SO2− 4 (i.e., an anthropogenic tracer) and emission ratios and ð EC2− Þ , i.e., 1.5 and 0.3, respectively, as refor ð OC2− Þ SO4

ANTHRO

SO4

ANTHRO

ported over Phimai in central Thailand from February till May 2006 (Li et al., 2013). Aerosols in Phimai were rarely directly influenced by large forest fires from the north but strongly influenced by anthropogenic emissions from the Bangkok metropolitan region and longrange transport from southern China (Li et al., 2013). Although the level of anthropogenic emissions in Phimai is not exactly equal with that in Chiang Mai, we use it as a basis in this study due to the

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unavailability of emission ratios for the latter. h i OC ANTHRO ¼ SO4 2− h i EC ANTHRO ¼ SO4 2−

!

OC SO4 2−

ð10Þ ANTHRO

!

EC SO4 2−

ð11Þ ANTHRO

Finally, the total concentrations were estimated as: ½OC ¼ OC BB þ OC ANTHRO

ð12Þ

½EC ¼ EC BB þ EC ANTHRO

ð13Þ

∂T i.e., the atmospheric heating rate ( ∂t in K d−1), was estimated using the first law of thermodynamics and hydrostatic equilibrium as follows:

3.3. Estimation of direct aerosol radiative forcing The clear-sky shortwave (SW; 0.25–4 μm) direct aerosol radiative forcing (ARF, W m−2) was estimated by using the two-step methodology and criteria described in Pani et al. (2016b). This approach involves (i) the reconstruction of various aerosol optical properties (viz., AOD, SSA, and AP) in the Optical Properties of Aerosols and Clouds (OPAC 3.1) model (Hess et al., 1998) by using available observations and (ii) the incorporation of the reconstructed aerosol optical properties in the Santa Barbara Discrete Ordinate Radiative Transfer (SBDART) model (Ricchiazzi et al., 1998). The OPAC model can easily incorporate the user-defined wavelength, RH, aerosol composition, and vertical profile. Different aerosol components (water-insoluble, water-soluble, EC, sea salt, and mineral dust) used in this study were assumed to be spherical and externally mixed (Hess et al., 1998). Campbell et al. (2013) also reported that the near-surface aerosols were mostly spherical over Southeast Asia and the Maritime Continent on the basis of CALIOP backscatter signals. The SBDART model can estimate the flux within 2% of direct and diffuse irradiance measurements (Michalsky et al., 2006). This model has been widely used to solve the radiative transfer equations in several studies (e.g., Podgorny et al., 2000; Satheesh, 2002; Satheesh and Srinivasan, 2006; Ramachandran et al., 2012; Pani, 2013; Pani et al., 2016b) and can estimate the ARF with an accuracy of ±2 W m−2 (Satheesh and Srinivasan, 2006; R. Kumar et al., 2011). The instantaneous net ARF at the surface ((ARFSFC)NET) and at the top of the atmosphere ((ARFTOA)NET) were estimated as the change in the net difference between the downward (↓) and upward (↑) fluxes with and without aerosol conditions as: ðARF SFC ÞNET ¼ ↓Fluxwith aerosol;

SFC −↑Fluxwithout aerosol;SFC

ð14Þ

ðARF TOA ÞNET ¼ ↓Fluxwith aerosol;TOA −↑Fluxwithout aerosol;TOA

ð15Þ

However, ARF is generally expressed as the diurnal mean radiative forcing (Xia et al., 2007; Liu et al., 2012) and the explanation is often given as: ARF SFC ¼

1 24h

AR F TOA ¼

1 24h

combination of water, sand, and vegetation surface types (e.g., Ramachandran et al., 2012; Verma et al., 2013; Pani, 2013; Pani et al., 2016b). The spectral mean surface reflectance over Chiang Mai was obtained as 0.14, which was found to be similar to previously reported values (0.12–0.16) from the Clouds and the Earth's Radiant Energy System (CERES) datasets in Gautam et al. (2013). Consistent with some of the earlier literature (e.g., R. Kumar et al., 2011; Ramachandran et al., 2012; Pani et al., 2016b), the overall uncertainty in ARF estimated in the study was b15% due to the uncertainties in the measurements, model atmosphere, OPAC simulation, aerosol parameters, and additional inputs. The amount of energy trapped in the atmosphere by the aerosols i.e., ARFATM was estimated as the difference between the ARFTOA and ARFSFC. The major consequence to the above aerosol-induced radiative impacts,

Z ðARF SFC ÞNET dt

ð16Þ

ðARF TOA ÞNET dt

ð17Þ

Z

Vertical profiles of the aerosol extinction coefficient from CALIPSO were normalized by the total AERONET AOD, whereas SSA and AP as derived from AERONET were assumed to be constant for the whole atmospheric column (Xia et al., 2007; Liu et al., 2012). Inputs to the model also included the water vapor and ozone vertical profiles, obtained from AERONET and OMI, respectively, following the standard model atmosphere (McClatchey et al., 1972). MODIS-derived values were used to reproduce the surface reflectance in the SW spectrum using the

s ∂T g ARF ATM h 3600 24 ¼ Cp day h ΔP ∂t

ð18Þ

where g is the acceleration due to gravity (9.8 m s−1), Cp is the specific heat capacity of the air at constant pressure (1006 J kg−1 K−1), and ΔP is the atmospheric-pressure difference (Liou, 1980). Different kinds of atmospheric aerosols (e.g., water-soluble, black carbon, sea salt, and mineral dust) are mainly concentrated within the 3 km (e.g., IPCC, 2007; Ramanathan et al., 2005) altitude over urban, continental, and marine locations. Hence, 300 hPa (approximately equal to the pressure difference between the surface and 3 km) was used as ΔP in this study. 3.4. Hierarchical cluster analysis LG (1, 6-anhydro-b-D-glucopyranose) is generally considered as a highly specific tracer of BB aerosols (Simoneit, 1999; Puxbaum et al., 2007; Yttri et al., 2014), as it derives from the pyrolysis of cellulose and hemicellulose at temperature N300 °C (Zhang et al., 2008). Additionally, nss-K+ is also often used as a BB chemical tracer (Andreae, 1983; Chuang et al., 2013). Moreover, significant contribution of NO− 3 to BB aerosols from agricultural-waste burning was reported earlier (e.g., Ryu et al., 2007; Lee et al., 2011; Chuang et al., 2013; Khamkaew et al., 2016). Khamkaew et al. (2016) also reported the strong positive correlation of NO− 3 with PM2.5 over northern PSEA in the dry season of 2014. We performed cluster analysis to classify the aerosol-sampling dates into clusters/groups using the surface concentrations of three BB chemical tracers (LG, nss-K+, and NO− 3 ). We made a hierarchical cluster analysis in the software package SPSS (v24.0) using Ward's method with squared Euclidean measures for the distances (e.g., Niemi et al., 2005; Lu et al., 2006; Kang et al., 2010; Genga et al., 2012), which is a highly suitable method for grouping different aerosol samples (Bernard and Van Grieken, 1992; Mangiameli et al., 1996). More details of Ward's clustering algorithm can be found elsewhere (Strauss and von Maltitz, 2017). The results are given as a dendrogram in Fig. 2. As can be clearly seen, two major clusters were formed, each with two major subgroups. The respective sampling dates for the two clusters are also shown in Fig. 2. The bottom 3 aerosol samples in Fig. 2 are classified as “Cluster A” and the rest as “Cluster B”. Cluster A includes all three BB-rich samples; however 21 March when all the BB tracers were at their highest levels, is designated as “Extreme BB (EBB)”. Wang et al. (2015) also reported the presence of excessively thick smoke haze (viz., AOD500 ≈ 4.3) on that particular day over northern PSEA on the basis of MODIS fire counts. Two other samples (observed on 18 and 20 March) are designated as “High BB (HBB)”. Cluster B likely includes a total of 28 aerosol samples; however, the top 7 in Fig. 2 are BB-poor samples and designated as Low BB (LBB; N = 7) and the remaining samples are designated as Mild BB (MBB; N = 21).


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Fig. 2. Dendrogram of the hierarchical cluster analysis performed on the basis of LG, nss-K+, and NO− 3 mass concentrations in PM2.5 measured over Chiang Mai in 2014.

4. Results and discussions 4.1. Overview of PM levels and air quality The mean PM10 mass was 119 ± 45 μg m−3 (range: 36–243 μg m−3) over Chiang Mai and demonstrated a significant statistical correlation (r = 0.84) with PM2.5 (both were measured at PCD by TEOM), indicating the similarity between their origins. In addition, the average and standard deviation of the PM mass ratio (i.e., PM2.5/PM10) is often considered as an indicator of finer PM's relative contribution to the coarser mode. Daily variations in the PM2.5/PM10 ratio persist in the range of 0.53–0.93 with an average of 0.71 ± 0.08, which clearly indicates that the finer PM is responsible for the variability of coarser PM over the urban atmosphere. A very similar PM ratio (0.73 ± 0.09) was reported over Suthep Mountain, Chiang Mai Province, Thailand, for springtime BB aerosols (Chuang et al., 2013). Fig. 3a shows the temporal variation in PM2.5 concentrations (measured at PCD and CMU) during the study period. The PM2.5 mean mass at CMU (93 ± 37 μg m−3, range: 28– 223 μg m−3) was found to be identical with the PCD (93 ± 32 μg m− 3 ; 42–188 μg m−3) data. Both the PM10 and PM2.5 mass (in μg m−3)

over Chiang Mai in most cases exceeded the 24-h prescribed mean standards of Thailand (PM10 = 120; PM2.5 = 50, http://www.pcd.go.th/), the World Health Organization (WHO; PM10 = 50; PM2.5 = 25, www. who.int/), the United States Environmental Protection Agency (USEPA; PM10 = 150; PM2.5 = 35, http://www.epa.gov/air/ particlepollution/), and the European Union (EU; PM10 = 50; PM2.5 = N/A; http://ec.europa.eu/environment/air/). This comparison shows that the PM concentrations over Chiang Mai were well above the prescribed national and international standards and can pose a significant threat to the local inhabitants. According to the USEPA, the 24-h range of 40.5–65.4 μg m−3 for the dry PM2.5 mass poses a moderate risk, and higher concentrations are considered to be potentially hazardous to human health (Salinas et al., 2013). With respect to other BB-dominated locations in PSEA during the dry season, the PM2.5 mass (in μg m−3) at CMU in this study was found to be higher than the reported values of 45.5 ± 8.8 at the Suthep Mountain site in Chiang Mai Province in 2010 (Chuang et al., 2013), 51.4 ± 19.0 in 2012 and 57.3 ± 26.5 in 2013 at Sonla, Vietnam (Lee et al., 2016), and 83.3 ± 35.0 at DAK in 2014 (Khamkaew et al., 2016). This comparison shows the severity of the BB in the dry season of 2014

898


Fig. 3. (a) Temporal variations in the PM2.5 mass concentrations; (b) Comparison of PM2.5 levels for Chiang Mai with other urban locations.

over PSEA. Fig. 3b compares the PM2.5 mass over Chiang Mai with data reported over other urban locations (i.e, Delhi: Dumka et al., 2016; Beijing, Hanoi, Bandung, Bangkok, Chennai, and Manila: Kim Oanh et al., 2006; Ahvaz: Shahsavani et al., 2012; Singapore: Balasubramanian et al., 2003; Sao Paulo: Souza et al., 2014; Rabigh: Nayebare et al., 2016; Hong Kong: Cao et al., 2004; Barcelona and Ghent: Viana et al., 2007; Yokohama: Khan et al., 2010) under similar monitoring conditions (i.e., during the dry season and when the atmospheric pollution level was at its peak) and reveals Chiang Mai as a highly polluted urban location in terms of finer aerosols. A summary of the air-quality data and meteorological parameters averaged for different clusters is provided in Table 1. Both PM2.5 and PM10 followed the order of EBB N HBB N MBB N LBB, along with their mass ratios, indicating the larger association of finer particles with BB. The PM level during EBB was found to be ≈4 times higher than during LBB. Meteorological parameters (viz., P, WS, calm conditions, and the ABL height) were found to be favorable to PM concentrations increasing with the severity of BB. Higher concentrations of PM along with the favorable meteorology (WS ≈ 0.7–2.4 m s−1 and RH b 52%) in Chiang Mai resulted in the prevalence of severe haze conditions (VIS ≈ 4–9 km; b10 km) in the dry season of 2014. Like PM, a similar order was seen for CO, NO, NOx, and NO2, which indicated that local pollution was confined due to calm wind conditions. The lowest O3 during EBB was

attributed to a lack of excess solar radiation (responsible for weaker atmospheric photochemical reaction) due to severe haze condition. 4.2. Overview of aerosol chemical compositions 4.2.1. Day-to-day variations The daily concentrations of the BB chemical tracers used in this current analysis, viz., LG, nss-K+, and NO− 3 , varied between 0.4 and 3.5 μg m−3 (mean: 1.1 ± 0.7 μg m−3), 0.8 and 3.3 μg m−3 (mean: 1.9 ± 0.6 μg m−3), and 0.7 and 6.1 μg m−3 (mean: 2.7 ± 1.1 μg m−3), respectively (Fig. 4a) over Chiang Mai. Khamkaew et al. (2016) previously reported the high correlation between PM2.5 and LG, K+, and NO− 3 at CMU as well as the DAK site during this campaign. All three BB tracers reached their highest peaks on 21 March, followed by 20 and 18 March, and displayed similar variations as the daily fire counts (Fig. 4b). The fire-count value on 19 March was also similar to those on 18, 20, and 21 March, but lower mass concentrations of LG, nss-K+, and NO− 3 including the PM2.5 mass (Fig. 3a) were observed on that day. This city is partially surrounded by the high mountain ranges and does not frequently receive cleaner air masses during the dry season. However, the influence of the oceanic air mass originating from the Andaman Sea (supplemental Fig. S1; as depicted by the two-day back trajectory information obtained using the Hybrid Single-Particle

S.K. Pani et al. / Science of the Total Environment 633 (2018) 892–911 Table 1 Average concentrations of air-pollutants, meteorological parameters as well as of WSIIs, trace metals, OC, and EC in PM2.5 (μg m−3) over Chiang Mai. Parameters

HBB

EBB

Air pollutants and meteorological parameters 61 ± 26 128 ± 22 PM10 (μg m−3)a PM2.5 (μg m−3)a 57 ± 10 98 ± 18 PM2.5/PM10 0.6 ± 0.0 0.7 ± 0.1 a CO (ppm) 1±0 1±0 a NO (ppb) 2±1 7±5 NOx (ppb)a 21 ± 3 37 ± 10 NO2 (ppb)a 18 ± 3 30 ± 6 SO2 (ppb)a 2±1 1±1 O3 (ppb)a 33 ± 6 33 ± 6 b T (°C) 27 ± 1 28 ± 1 RH (%)b 52 ± 7 43 ± 6 b P (hPa) 1014 ± 1 1012 ± 2 VIS (km)b 9±1 6±1 WDb N–NW N–NW 1.8 ± 0.5 1.6 ± 0.3 WS (m s−1)b Calm condition (%)c 15 17 Fire Counts 26 ± 36 62 ± 39 ABL (m) 1245 ± 156 1163 ± 116

LBB

MBB

166 ± 19 122 ± 22 0.7 ± 0.0 1±0 6±1 44 ± 1 38 ± 0 3±1 35 ± 4 28 ± 1 39 ± 2 1013 ± 1 5±0 N–NW 1.4 ± 0.2 29 128 ± 1 1035 ± 108

243 188 0.8 2 10 54 44 2 28 27 36 1010 4 N–NW 0.7 54 154 887

Chemical compositions in PM2.5 Total mass 56 ± 16 Na+ 2.4 ± 1.9 + NH4 2.8 ± 0.6 K+ 1.4 ± 0.3 nss-K+ 1.3 ± 0.3 Ca2+ 0.4 ± 0.3 nss-Ca2+ 0.4 ± 0.3 Cl− 1.0 ± 0.2 − NO3 1.4 ± 0.4 2− SO4 8.7 ± 2.1 2− nss-SO4 8.2 ± 2.1 Cations 7.1 ± 2.5 Anions 11.1 ± 1.9 Al 1.5 ± 0.5 Cu 0.0 ± 0.0 K 2.2 ± 0.5 Mg 0.6 ± 0.7 Mn 0.0 ± 0.0 Ni 0.0 ± 0.0 Zn 0.5 ± 0.2 Total trace metals 4.8 ± 1.3 LG 0.6 ± 0.2 OC 22.5 ± 4.3 EC 4.3 ± 0.8 OCBB to OC (%) 42 ± 10 OCANTHRO to OC (%) 58 ± 10 ECBB to EC (%) 39 ± 9 ECANTHRO to EC (%) 61 ± 9 BB to PM2.5 (%) 52 ± 16

156 ± 28 4.0 ± 0.5 2.8 ± 0.5 3.1 ± 0.3 3.0 ± 0.3 0.4 ± 0.1 0.2 ± 0.1 1.0 ± 0.2 4.7 ± 0.3 6.4 ± 1.3 5.3 ± 1.2 10.5 ± 1.3 12.0 ± 1.4 1.9 ± 0.9 0.0 ± 0.0 5.1 ± 1.4 2.4 ± 3.0 0.0 ± 0.0 0.0 ± 0.0 0.1 ± 0.1 9.6 ± 5.5 2.0 ± 0.4 41.3 ± 7.8 7.6 ± 1.4 77 ± 0 23 ± 0 75 25 ± 0 63 ± 0

223 5.4 4.3 3.5 3.3 0.7 0.5 1.6 6.1 10.1 8.8 13.7 17.8 3.8 0.0 7.0 2.7 0.1 0.1 1.0 14.6 3.5 72.0 13.3 79 21 77 23 79

93 ± 16 3.3 ± 1.5 3.1 ± 0.9 2.1 ± 0.4 2.0 ± 0.4 0.5 ± 0.3 0.6 ± 0.3 1.0 ± 0.5 2.7 ± 0.5 7.5 ± 2.9 6.7 ± 3.0 9.0 ± 1.5 11.2 ± 3.1 1.7 ± 0.5 0.0 ± 0.0 3.3 ± 0.5 1.0 ± 0.6 0.0 ± 0.0 0.0 ± 0.0 0.4 ± 0.3 6.5 ± 1.2 1.1 ± 0.5 29.2 ± 8.3 5.5 ± 1.5 60 ± 14 40 ± 14 57 ± 15 43 ± 15 54 ± 14

WS b 0.5 m s−1 was taken as calm (e.g., Pani and Verma, 2014; Verma et al., 2014, 2016). a Obtained from PCD. b Obtained from TMD. c Estimated from WS data.

Lagrangian Integrated Trajectory Version 4 model at 00/06/12 UTC; Draxler and Rolph, 2003) was the primary reason for the PM dilution on 19 March (WS was also higher on that day than 18, 20, and 21 March). Peaks in LG on 12 and 13 March (Fig. 4a) did not match the patterns of nss-K+, NO− 3 , and the fire counts. These peaks in LG may be attributable to the stable atmospheric stratification (a lower WS was seen on those two days than the previous days) and resultant accumulation rather than to burning activities in and around the sampling location. It is worth noting that LG is relatively stable (compared to nss-K+ and NO− 3 ) in the atmosphere, with no decay over 10 days in highly acidic conditions (Fraser and Lakshmanan, 2000; Wu et al., 2016). Chiang Mai is a highly acidic urban atmosphere (Chantara et al., 2012) and also an important monitoring site for the Acid Deposition Monitoring Network in East Asia (2000) campaign. However, the later small peaks in LG (from 1 till 3 April) are nearly found consistent with those in nss-K+, NO− 3 , and the fire counts.

899

The LG concentration (1.13 μg m−3) in the dry season of 2014 was found to be almost identical with the value reported over Chiang Mai in 2010 (1.12 μg m−3; Tsai et al., 2013) and indicates the presence of similar BB in every dry season. However, the LG concentration obtained in this study was much higher than those reported in megacities such as Guangzhou (Zhang et al., 2015) and Beijing (Zhang et al., 2008) and BBinfluenced locations such as Morogoro in Tanzania (Mkoma et al., 2013), and Florence in Italy (Giannoni et al., 2012), and locations in the southeastern USA (Zhang et al., 2010). In addition, the contribution of BB to the ambient PM was assessed by using the ratio of LG to PM2.5 (Wang et al., 2007).

Contribution of BB to PM2:5 ð%Þ ¼

ðLG=PM 2:5 ÞAMBIENT 100 ðLG=PM2:5 ÞSOURCE

ð19Þ

Here, we have used 0.02 (≈2%) as the (LG/PM2.5)SOURCE ratio with respect to simultaneous surface measurements carried out at DAK, Chiang Mai Province (Khamkaew et al., 2016). Interestingly, this ratio was also similar to that for with Suthep Mountain, Chiang Mai Province, from March till April 2010 (Chuang et al., 2013). The contribution of BB to the ambient PM2.5 was found to be 25–79% over Chiang Mai, indicating the significant influence of BB on the ambient air quality. + 2+ Daily variations in cations (the sum of Na+, NH+ , and Mg2 4 , K , Ca + − − 2− ), anions (the sum of Cl , NO3 , and SO4 ), trace metals (the sum of Al, Cu, K, Mg, Mn, Ni, and Zn), and the estimated OC and EC in PM2.5 at the CMU site are shown in Fig. 4c. The total concentration of anions was found to be higher than the cations in the PM2.5. The OC fractions dominated the distribution, exhibiting characteristics of BB activity in the PM2.5 chemistry. The estimated mean OC and EC mass concentrations were 29.9 ± 11.4 and 5.6 ± 2.1 μg m−3, respectively. These values are close to the previously reported concentrations in PM10 over Chiang Mai (OC: 30.8 ± 13.6 μg m−3 and EC: 5.3 ± 2.6 μg m−3) during February 2008 (Pongpiachan et al., 2013), strongly validating our method of estimating OC and EC concentrations in this study. The OC and EC concentrations (in μg m−3) over Chiang Mai were higher than over nearby sites in the dry season, viz., Suthep Mountain, Chiang Mai Province in 2010 (OC: 18.2 ± 4.1 and EC: 3.3 ± 0.9; Chuang et al., 2013); Sonla,Vietnam in 2012 (OC: 21.0 ± 9.8 and EC: 3.3 ± 1.7; Lee et al., 2016); at Phimai, central Thailand (OC: 9.5 ± 3.6 and EC: 2.0 ± 2.3; Li et al., 2013); and Hat-Yai, Thailand (OC: 8.3 ± 2.5 and EC: 0.5 ± 0.8; Pongpiachan et al., 2013), but lower than the concentrations over Bangkok, Thailand in 2008 (OC: 62.9 ± 15.0 and EC: 18.4 ± 5.3; Pongpiachan et al., 2013). The OC/EC ratio, which generally provides information on the emission sources of carbonaceous aerosols (Cao et al., 2005), was 5.3 ± 0.09, indicating BB aerosols. This ratio was also similar to that for Chiang Mai, Thailand (5.8 ± 3.9) in February 2008 (Pongpiachan et al., 2013); Suthep Mountain, Chiang Mai Province (5.7) in March– April 2010 (Chuang et al., 2013); and Sonla (6.1 ± 1.5) during spring 2013 (Lee et al., 2016). The nss-K+/EC mass ratio varied from 0.21 to 0.66 (0.37 ± 0.12) in this study, similar to the typical BB range of 0.1– 0.6 (Andreae and Merlet, 2001). + + − WSIIs were distributed (Fig. 4d) as nss-SO2− 4 N Na N NH4 N NO3 + − 2+ 2− N nss-K N Cl N nss-Ca . Larger variations in the nss-SO4 and NH+ 4 indicated contributions from anthropogenic and industrial emissions other than BB, as expected over an urban location. The concentration − + of secondary inorganic aerosols (the sum of SO2− 4 , NO3 , and NH4 ) in our study was estimated as 12.7 ± 3.5 μg m−3 similar to those reported at DAK (13.2 μg m−3; Khamkaew et al., 2016); Bangkok, Thailand (11.8 μg m−3; Kim Oanh et al., 2006); and Phimai, central Thailand (9.6 μg m− 3 ; Li et al., 2013), and higher than those over Sonla, Vietnam (8.6 μg m− 3 ; Lee et al., 2016) and Suthep site in Chiang Mai Province (6.3 μg m−3; Chuang et al., 2013). To understand the atmospheric transformation of SO2 to SO2− and NO2 to NO− 4 3 , the sulfur oxidation ratio (SOR) and

900


Fig. 4. Daily variations in the (a) LG, nss-K+, and NO− 3 concentrations; (b) Fire counts and WS; (c) cations, anions, trace metals, OC, and EC concentrations; and (d) Distributions of WSIIs and trace metals in PM2.5 during the study period.

Fig. 5. (a) Correlation between the observed and reconstructed PM2.5 mass; (b–e) Relative contributions (%) of different components in reconstructed PM2.5 for different clusters.


the trace metal distribution (K N Al N Mg N Zn N Ni ≥ Cu ≥ Mn; Fig. 4d), the dominance of BB, followed by crustal and industrial emissions, was exhibited over Chiang Mai.

nitrogen oxidation ratio (NOR) were estimated as follows: i n−SO4 2− i SOR ¼ h n−SO4 2− þ ½n−SO2

h

½n−NO3 − NOR ¼ ð½n−NO3 − þ ½n−NO2 Þ

901

ð20Þ

ð21Þ

where, all the daily average concentrations were in molar units. The daily SOR and NOR varied between 0.23 and 0.87 (mean: 0.56 ± 0.19) and 0.01 and 0.06 (mean: 0.03 ± 0.01), respectively over Chiang Mai. 2− The NO− 3 /SO4 ratio in PM2.5 shows the importance of vehicular emis2− sions and stationary sources in the production of NO− 3 and SO4 in the atmosphere (Xu et al., 2014). This ratio was b1, specifically 0.39 ± 0.22 (0.1–0.8) in Chiang Mai; therefore, compared to mobile sources, the stationary BB sources played a relatively important role in contributions to PM2.5. Moreover, this ratio also indicates the influence of H2SO4, reflecting the higher aerosol acidity. Additionally, Chantara and Chunsuk (2008) reported that the acidity in rainwater in this region is mainly due to H2SO4, which is neutralized by NH+ 4 . NH3 first reacts with H2SO4 to form (NH4)2SO4 and NH4HSO4; then the remaining NH3 is picked up by HNO3 to form NH4NO3 (Hu et al., 2003). Referring to

4.2.2. Cluster variations Interestingly, the PM2.5 total mass and its corresponding anions, cations, total trace metals, OC, and EC (Table 1) followed the order of EBB N HBB N MBB N LBB. This shows the extremely strong influence of BB on the surface aerosol chemistry over Chiang Mai in the dry season of 2014. See et al. (2006) reported higher concentrations of + + 2− + most of the WSIIs (i.e., Cl −, NO − 3 , SO 4 , Na , NH 4 , and K ) and trace metals (i.e., Al, Cu, Fe, Mg, Mn, Ni, Pb, and Zn) during smoke related hazy-days than during clear-days over Singapore. The PM2.5 and LG concentrations during EBB were 4 and 6 times higher, respectively, than those during LBB. However, the OC and EC mass during EBB was ≈3 times higher than during LBB. Numerous studies have shown that BB emissions induce more organic aerosols to enter the atmosphere (Mayol-Bracero et al., 2002; Simoneit et al., 2004). In this study, the BB contribution to the total OC and EC was as high as ≈79% during EBB, 2 times higher than during LBB. The cluster variation in the BB contribution to the PM2.5 was 52–79%, ≈1.5 times higher during EBB than LBB.

Fig. 6. (a) Aerosol size distributions over Chiang Mai; Day-to-day variations in observed aerosol optical properties: (b) AOD500 with the measured PM2.5 mass, (c) AE440/870, (d) FMF500, (e) SSA440, (f) AP440, and (g) AAOD440, and AAE440/870 during the study period.

902


Table 2 Aerosol microphysical and optical properties over Chiang Mai's urban atmosphere in the dry season of 2014. Cv, F and Cv, C, Reff_F and Reff_C, Rmod_F and Rmod_C, and σF and σC are the columnar volume of particles per unit cross section of atmospheric column, effective radius, volume median radius, and the standard deviations for fine- and coarse-mode aerosols, respectively. Parameters

LBB

MBB

HBB

EBB

Cv, F (μm3 μm−2) Reff_F (μm) Rmod_F (μm) σF (μm) Cv, C (μm3 μm−2) Reff_C (μm) Rmod_C (μm) σC(μm) REFR440 REFI440 AOD500 CWV (cm) AE440/870 Fine mode AOD500 Coarse mode AOD500 FMF500 SSA440 AP440 AAOD440 AAE440/870

0.14 ± 0.04 0.16 ± 0.02 0.17 ± 0.02 0.49 ± 0.04 0.09 ± 0.03 2.28 ± 0.16 2.82 ± 0.20 0.65 ± 0.02 1.47 ± 0.05 0.02 ± 0.01 0.98 ± 0.18 2.88 ± 0.48 1.59 ± 0.06 0.94 ± 0.21 0.07 ± 0.02 0.93 ± 0.02 0.87 ± 0.04 0.69 ± 0.03 0.15 ± 0.03 1.43 ± 0.14

0.18 ± 0.06 0.14 ± 0.01 0.16 ± 0.01 0.47 ± 0.02 0.07 ± 0.03 2.61 ± 0.29 3.27 ± 0.32 0.67 ± 0.03 1.51 ± 0.03 0.03 ± 0.00 1.30 ± 0.47 2.21 ± 0.44 1.77 ± 0.06 1.25 ± 0.49 0.05 ± 0.01 0.96 ± 0.02 0.87 ± 0.02 0.67 ± 0.02 0.20 ± 0.05 1.44 ± 0.07

0.18 ± 0.01 0.13 ± 0.01 0.15 ± 0.01 0.45 ± 0.01 0.06 ± 0.01 2.52 ± 0.28 3.21 ± 0.29 0.69 ± 0.03 1.48 ± 0.01 0.03 ± 0.00 1.34 ± 0.05 2.09 ± 0.42 1.84 ± 0.04 1.04 ± 0.10 0.05 ± 0.01 0.95 ± 0.01 0.85 ± 0.01 0.66 ± 0.02 0.22 ± 0.03 1.54 ± 0.03

0.34 0.14 0.16 0.47 0.09 2.82 3.48 0.65 1.49 0.03 2.45 1.87 1.82 2.82 0.04 0.99 0.86 0.67 0.38 1.57

We reconstructed the surface PM2.5 mass by using the Interagency Monitoring of Protected Visual Environments (IMPROVE) algorithm (Pitchford et al., 2007) as follows: PM2:5 ¼ ðNH 4 Þ2 SO4 þ ½NH 4 NO3 þ ½OM þ ½EC þ ½Sea−Salt þ ½Soil

ð22Þ

− where [(NH4)2SO4] = 1.375 × [SO2− 4 ], [NH4NO3] = 1.29 × [NO3 ] (Pani − et al., 2017), [Sea-Salt] = 1.8 × [Cl ] (Shettle and Fenn, 1979), and [Soil] = 2.2 × [Al] + 1.66 × [Mg] + 1.21 × [K] + 1.95 × [Ca] (Patterson, 1981). When reconstructing the PM2.5 mass, the conversion factor of OC and OM varies with the dominant source. Considering the large variations in the LG concentration (Fig. 4a), the impact of BB on OM must be different. Hence, the conversion factor was chosen to be 2.2, 2.1, 1.9, 1.8, and 1.7 when LG was in the range of N2, 1.5–2, 1–1.5, 0.5–1, 0.2–0.5 μg m−3, respectively (e.g., Tao et al., 2016). The observed and reconstructed PM2.5 mass were well correlated (r = 0.85) over Chiang Mai (Fig. 5a), indicating that the abovementioned chemical components can closely represent the measured PM2.5. The relative contributions (%) of different components in the PM2.5 reconstructed mass (Fig. 5b–e) followed the sequence of [OM] N [(NH4)2SO4] N [Soil] N [EC] N [NH4NO3] = [Sea-Salt] for the LBB, whereas the other clusters followed the sequence of [OM] N [Soil] N [(NH4)2SO4] N [EC] N [NH4NO3] N [Sea-Salt]. Among all the chemical species, OM was the biggest contributor (60–72%) to the total PM2.5 mass and correspond to the degree of BB emissions (EBB N HBB N MBB N LBB). In contrast, the contributions of [(NH4)2SO4] followed the order of LBB N MBB N HBB N EBB, indicating the larger association between NH+ 4 and SO2− 4 during periods of lesser BB influence, mainly due to the favorable meteorological conditions (viz., a lower T and higher RH; Table 1). The contribution (%) of [(NH4)2SO4] was greater than that of [NH4NO3] 2− due to the stronger correlation between NH+ 4 and SO4 , than between + − NH4 and NO3 at the CMU site (Khamkaew et al., 2016). Chantara et al. (2012) also reported the strong correlation (r = 0.78–0.91) between 2− NH+ 4 and SO4 over Chiang Mai, indicating the significant formation of (NH4)2SO4. [NH4NO3], [EC], [Sea-Salt], and [Soil] contributed the same amounts in all clusters. Although, the contribution of [EC] in the PM2.5 mass was found to be only 6%, it can make larger contributions in AOD and ARF (e.g., Pani et al., 2016b, 2016c).

4.3. Aerosol size distributions and microphysical properties Fig. 6 shows the mean aerosol size distribution of different clusters. The size distribution reveals two distinct modes: fine (particle size b0.6 μm) and coarse (particle size N0.6 μm). Moreover, these modes indicate the presence of a dominant fine mode with a peak radius in the range of 0.09–0.2 μm, and a secondary coarse mode with a peak radius in the range of 3–5 μm. Both modes exhibit an approximately lognormal distribution, although the coarse mode has less of a radial skew. A bimodal structure for the volume size distribution was observed, which may be due to mixing air masses with different aerosol populations (Hoppel et al., 1985), homogeneous hetero-molecular nucleation of new fine particles in the air, or heterogeneous nucleation and the growth of large particles by the condensation of gas-phase reaction products (S. Kumar et al., 2011). This distribution is similar to those from near-source BB AERONET sites (Sayer et al., 2014). The volume concentration for fine-mode (Cv, F) aerosols (Table 2) followed the order of highest for EBB N HBB N MBB N LBB, indicating that the prevalence of the in fine mode increased with the severity of BB. REFI440 also followed the same order, suggesting an association with the higher aerosol absorption and the severity of BB loadings. 4.4. Observed aerosol optical properties 4.4.1. Day-to-day variations Fig. 6b–g shows the daily variations in various aerosol optical properties obtained from the AERONET observations. The columnar AOD represents the extinction of incoming solar radiation by aerosols and its magnitude is directly proportional to the aerosol loading in the total atmospheric column. Fig. 6b shows the evolution of the daily mean AOD500, and the differences in value indicate the inhomogeneity of the aerosol loading. The mean columnar AOD500 was found to be 1.26 ± 0.47 (0.61–2.50). There were some episodic days during the study period, with the peak AOD500 on 21 March (2.45) and 3 April (2.5) and an especially low AOD500 on 8 March (0.6) compared to the overall mean. The feature in AOD500 on 8 and 21 March was due to the lowest (27.6 μg m−3) and highest (223 μg m−3) PM2.5, respectively. However, the peak in AOD500 on 3 April (PM2.5 = 120 μg m−3) was possibly due to cloud contamination, as on that day rainfall was observed to be about 0.6 mm. Other than on those two days (21 March and 3 April), the mean AOD500 was 1.17 ± 0.34. Similarly, the peak AOD500 over northern PSEA at DAK, Maesoon, Luang Namtha, and Sonla was also observed on 21 March 2014 due to the prevalence of thick smoke conditions (Wang et al., 2015). Aside from the highest PM, the lowest ABL height and WS on 21 March might be a major reason for the severe haze episode. High smoke aerosol loading (AOD500 ≈ 2) was also observed on episodic days for tropical BB regions in Brazil and Zambia, and for transported boreal forest-/peat-fire smoke in the USA and Moldova (Eck et al., 2003a). Salinas et al. (2013) also reported a value for AOD500 as high as 2.18 over Singapore due to transboundary BB smoke on an episodic day, 20 October 2010. AE widely varies due to the environmental conditions, aerosol sources, and sinks (S. Kumar et al., 2011). For coarse-mode particles, AE is b1, and for fine-mode particles, AE is N1 (Reid et al., 1999). Daily variations in AE440/870 (Fig. 6c) suggest significant variations in the aerosol particle size distribution over Chiang Mai. AE440/870 varied between 1.53 and 1.87 with a mean of 1.73 ± 0.10, indicating the relative dominance of fine-mode aerosols mainly from BB mixed with urban emissions. The AE value is approximately 1 for fossil-fuel burning and 2 for BB, and intermediate values indicate mixed pollution (Weingartner et al., 2003; Kirchstetter et al., 2004; Bergstrom et al., 2007). The notably smaller AEs, ≈1.5 on 5, 6, and 7 April, indicate the higher amount of coarse-mode aerosols, possibly due to the influence of air masses originating from the Bay of Bengal and moving to this region (Khamkaew et al., 2016; Pani et al., 2016b). Significant variations in AE440/675 (1.2–2) were reported over DAK during spring 2015 (Sayer et al., 2016). AE


values ranging between 1.2 and 1.7 were reported over Singapore during October 2010 due to BB smoke (Salinas et al., 2013). The daily CWV varied between 1.59 and 3.33 cm with a mean of 2.35 ± 0.53 cm. The mean fine-mode and coarse-mode AOD500 was 1.21 ± 0.53 (0.52–2.82) and 0.05 ± 0.02 (0.03–0.08), respectively. The coarse-mode AOD500 was higher on 5, 6, and 7 April, specifically 0.07– 0.08, due to the greater influence from coarse-mode aerosols, as depicted by the AE values. FMF500 (Fig. 6d) ranged between 0.9 and 0.99 with a mean of 0.95 ± 0.02, indicating a predominant contribution from fine-mode aerosols. A similar FMF500 of ≈0.95 was also reported over northern PSEA during spring of 2013 and 2014 (Wang et al., 2015; Pani et al., 2016b).

903

SSA is a crucial measure of the relative contribution of absorption to the total extinction and plays an important role in aerosol climatic-impact assessments (Jacobson, 2000; Dubovik et al., 2002). SSA440 (Fig. 6e) varied between 0.81 and 0.91 with a mean of 0.86 ± 0.02 over Chiang Mai, suggesting the dominance of strongly absorbing aerosols attributed to BB emissions. The presence of moderate-to-strong absorbing (SSA440: 0.85–0.95) smoke particles was also reported over DAK in spring of northern PSEA in 2013, 2014, and 2015 (Wang et al., 2015; Pani et al., 2016b; Sayer et al., 2016). SSA440 over Chiang Mai was found to be similar to the values observed during the Southern African Regional Science Initiative (SAFARI) campaign in the dry season of 2000 in Senanga, Zambia (0.86); Mwinilunga, Zambia (0.88); Skukuza,

Table 3 Comparison of aerosol properties (optical and microphysical) from worldwide AERONET sites over BB-source regions and other urban locations. Locations Biomass-burning Chiang Mai, Thailand DAK, Thailand DAK, Thailand Maesoon, Thailand LuangNamtha, Thailand Son La, Vietnam Mukdahan, Thailand Alta Floresta, Brazil Alta Floresta, Brazil Cuiaba, Brazil Cuiaba, Brazil Bolivia Cerrado, Brazil Rondonia, Brazil Bonanza Creek, North America Moscow, Russia Tomsk 22, Russia Yakutsk, Russia Canada Canada North America Siberia and central Canada East/central Siberia North/central Canada Quebec, Canada Louisiana, Mexico PensiCola, Mexico Huatulbo, Mexico Monterrey, Mexico Monblova, Mexico Aguascalientes, Mexico CART, Mexico Skukuza, South Africa Zambia, South Africa Mongu, Africa Mongu, Africa Senega, Africa Zambezi, Africa Sesheke, Africa Jabiru, Australia Urban Pearl River Delta, China Beijing, China New Delhi, India Greenbelt, USA Brete–Paris, France Mexico City, USA Maldives Burjassot, Valencia

Regions

Northern PSEA

Amazonia

Boreal

Mexico

Savanna

Australia

Time periods

AOD500 AE440/870

AAE440/870 SSA440 AP440 REFR440 REFI440 Reff_F

Reff_C

(μm)

(μm)

References

Mar.– Apr. 2014 Mar.– Apr. 2013 Mar.– Apr. 2014 Mar.– Apr. 2014 Mar.– Apr. 2014 Mar.– Apr. 2014 Feb.–Apr. 2004–2009 Aug.–Oct. 1999–2011 May 1998 Sep.– Oct. 2001–2013 May 1998 1993–1994, 1998–1999 1993–1995 May 1998 Apr.–Sep. 1999–2012

1.26 0.71 0.75 1.37 1.25 1.49 – – 0.47b – 0.48b 0.74a

1.73 1.77 1.73 1.75 1.73 1.69 1.66 1.95 1.81 1.91 1.78 1.2–2.1

1.45 – 1.48 1.49 1.57 1.46 1.43 1.78 – 1.68 – –

0.86 0.89 0.89 0.86 0.86 0.89 0.91 0.92 0.95b 0.89 0.88b 0.94

0.67 0.67 0.67 0.67 0.68 0.69 0.71 0.68 – 0.68 – 0.69

1.50 – – – – – 1.44 1.46 1.48b 1.46 1.48b 1.47

0.03 – – – – – 0.01 0.01 – 0.01 – 0.00

0.14 – 0.14 0.14 0.14 0.16 0.16 0.15 0.16 0.13 0.14 –

3.19 – 2.36 2.51 2.52 2.59 2.95 1.55 3.59 3.27 4.15 –

1 2 3 3 3 3 4 4 5 4 5 6

0.8a 0.50b –

1.2–2.1 1.98 1.42

– – 2.20

0.91 0.89b 0.95

0.67 – 0.69

1.52 1.50b 1.52

0.01 – 0.01

– 0.16 0.19

– 4.07 3.20

6 5 4

Jun.–Sep. 2002, 2010 Apr.–Sep. 2011–2013 Apr.–Sep. 2004–2013 1994–1998 Oct. 2005 May–Aug. 2003 May–Aug. 2003 Spring 2003

1.62 1.54 1.74 1–2.3 – – 0.71 1.35 1.4 0.83–1.23 1.61 1.52 1.57 1.69 1.68 1.65 1.45 1.97 1.4–2.2 1.89 1.85 1.78 1.83 1.72 1.88

1.92 1.95 1.99 – – – – – – – – – – – – – – 1.66 – 1.43 – – – – 1.62

0.95 0.94 0.95 0.94 0.96

Aug. 2002 May 1998 May 1998 May 1998 May 1998 May 1998 May 1998 May 1998 Jul.–Oct. 1999–2010 1995–2000 Jul.–Oct. 1999–2009 May 1998 May 1998 May 1998 May 1998 2003–2012

– – – 0.41a 2.28a – – – – – 0.84b 0.39b 0.74b 0.52b 0.49b 0.48b 0.36b – 0.38a – 0.44b 0.47b 0.43b 0.53b –

0.93b 0.94b N0.9b 0.93b – – – – 0.97b 0.97b 0.98b 0.90 0.88 0.87 0.86b 0.81b 0.82b 0.82b 0.88

0.70 0.70 0.69 0.69 – – – – – – – – – – – – – 0.68 0.64 0.67 – – – – 0.69

1.47 1.46 1.48 1.50 1.41 1.52 – – – – – – – – 1.45b 1.44b 1.41b 1.45 1.51 1.50 1.50b 1.51b 1.51b 1.50b 1.43

0.01 0.01 0.01 0.01 0.01 0.00 0.00 0.00 – – – – – – – – – 0.02 0.02 0.02 – – – – 0.02

0.17 0.16 0.16 – 0.33 0.17 0.36 0.21 0.15 0.15–0.3 – – – – 0.17 0.17 0.17 0.14 – 0.13 0.14 0.14 0.14 0.14 0.15

3.14 3.29 3.36 – – – – – – – – – – – 3.73 3.89 2.97 2.81 – 3.34 3.73 4.66 4.27 4.66 2.55

4 4 4 6 7 8 8 9 10 11 5 5 5 5 5 5 5 4 6 4 5 5 5 5 4

Oct. 2004 Jan. 2005 2009 1993–2000 1999 1999–2000 1999–2000 2002–2005

– – 0.69a 0.24a 0.26a 0.43a 0.27a 0.19a

– – 0.76 1.2–2.5 1.2–2.3 1–2.3 0.4–2.0 1.30

– – – – – – – –

0.77 0.78 0.86 0.98 0.94 0.90 0.91 0.90

– – 0.73 0.68 0.68 0.68 0.74 –

1.57 1.62 1.51 1.41 1.40 1.47 1.44 1.38

0.02 0.02 0.01 0.00 0.01 0.01 0.11 0.01

0.24 0.23 – – – – – –

– – – – – – – –

12 12 13 6 6 6 6 14

Note: The content of this study are shown as bold. 1: This study; 2: Pani et al., 2016b; 3: Wang et al., 2015; 4: Sayer et al., 2014; 5: Kreidenweis et al., 2001; 6: Dubovik et al., 2002; 7: Noh et al., 2009; 8: Muller et al., 2005; 9: Murayama et al., 2004; 10: O'Neill et al., 2002; 11: Eck et al., 2003a; 12: Muller et al., 2006; 13: S. Kumar et al., 2011; 14: Estellés et al., 2007. a 440 nm. b 670 nm.

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Table 4 Correlation coefficient (r) between PM and AOD reported at different locations. Locations

Period

Source of AOD

AOD vs PM10

AOD vs PM2.5

Chiang Mai, northern PSEA Chiang Mai, northern PSEA Chiang Mai, northern PSEA Doi Ang Khang, Thailand Doi Ang Khang, Thailand Doi Ang Khang, Thailand Varanasi, middle Indo-Gangetic Plain Varanasi, middle Indo-Gangetic Plain Northern Italy, Los Angeles Beijing metropolitan area, Beijing Beijing metropolitan region, Beijing Eastern China Hong Kong Taiwan Bucharest, Romania France Cabauw, the Netherlands Canada Canada Eastern and midwest USA Singapore New England, northern Hampshire Jeffersoncounty, Alabama

Mar.–Apr. 2014 Mar.–Apr. 2014 Mar.–Apr. 2014 Feb.–Apr. 2015 Feb.–Apr. 2015 Feb.–Apr. 2015 Jan.–Mar. 2014 Jan –Mar. 2014 Jul. 2000 – May 2001 2002–2004 2009 to 2010 2007 Oct. 2004 2006 to 2008 2008 and 2009 Apr.–Oct. 2003 Aug. 2006–May 2007 Jan. 2001– Oct. 2002 Jan. 2001– Oct. 2002 Apr.–Sep. 2002 Sep. 2009– Mar. 2011 Apr.–Aug. 2001 2002

AERONET MODIS-Terra MODIS-Aqua AERONET MODIS-Terra MODIS-Aqua MODIS Sun Photometer AERONET AERONET MODIS MODIS MODIS MODIS MODIS POLDER-2 MODIS/AERONET MODIS MISR MODIS AERONET MFRSR MODIS

0.48 0.46 0.76 0.63 0.76 0.71 0.46 0.55 0.82 0.59 – – 0.5 0.47 0.89 – – – – – – – –

0.61 0.65 0.34 0.59 0.81 0.78 0.54 0.39 – – 0.58 0.52 0.55 0.65 – 0.55 N 0.4 0.69 0.58 0.43 0.68 0.87 0.70

This study This study This study Sayer et al., 2016 Sayer et al., 2016 Sayer et al., 2016 Kumar et al., 2015 Kumar et al., 2015 Chu et al., 2003 Jiang et al., 2007 Kong et al., 2016 Guo et al., 2009 Li et al., 2005a, 2005b Tsai et al., 2011 Barladeanu et al., 2012 Kacenelenbogen et al., 2006 Schaap et al., 2009 van Donkelaar et al., 2006 van Donkelaar et al., 2006 Engel-Cox et al., 2004 Chew et al., 2016 Slater et al., 2004 Wang and Christopher, 2003

Note: The content of this study are shown as bold. POLDER-2: Polarization and Directionality of the Earth's Reflectances version 2; MISR: Multi-angle Imaging Spectro Radiometer; MFRSR: Multi-Filter Rotating Shadow band Radiometer.

South Africa (0.88); Etosha Pan, Namibia (0.90); Inhaca Island, Mozambique (0.88); and Bethlehem, South Africa (0.90) in southern Africa (Eck et al., 2003b). A SSA550 of ≈0.91 was found for organic aerosols over tropical BB regions (Chu and Ha, 2016). SSA440 values between 0.85 and 0.98 due to trans-boundary BB smoke were reported over Singapore during October 2010 (Salinas et al., 2013). AP measures the angular distribution of aerosol-scattered light and provides information on the aerosol size distribution and scattering properties (Andrews et al., 2006). It also serves as another crucial parameter in regulating aerosol radiative forcing. However, ARF is less sensitive to changes in AP than AOD and SSA (Srivastava and Ramachandran, 2013; Pani et al., 2016b). Daily variations in AP440 (Fig. 6f) ranged between 0.64 and 0.73 with a mean value of 0.67 ± 0.02, close to those at DAK (Pani et al., 2016b). Wang et al. (2015) also reported similar values of AP440 (0.67–0.69) at other locations in northern PSEA during the dry season of 2014. AAOD and AAE generally represent the degree of columnar aerosol light absorption and serve as qualitative indicators of the aerosol absorption over a site. AAOD440 (Fig. 6g) varied between 0.12 and 0.38 (0.20 ± 0.06) and showed significantly strong absorption over Chiang Mai. The highest AAOD (0.38) was observed on 21 March (SSA ≈ 0.86), and the lowest AAOD (0.12) was observed on 24 March (SSA ≈ 0.91). However, Pani et al. (2016b) reported a smaller AAOD440 (0.10 ± 0.04; 0.05–0.21) over a near-source BB region (DAK) in spring 2013, suggesting greater aerosol absorption in northern PSEA during the dry season of 2014 than of 2013. Likewise, AAE440/870 (Fig. 6g) varied between 1.30 and 1.70 (1.45 ± 0.09) in this study, similar to the values reported in Wang et al. (2015). 4.4.2. Cluster variations Cluster-wise, the aerosol optical properties (Table 2) also showed high variation, similar to the previously discussed PM2.5 mass and chemical compositions. The cluster-wise AOD500 exhibited high variation in a trend similar to the PM concentrations over Chiang Mai (EBB N HBB N MBB N LBB). Following a similar order, AOD500 and AAOD440 were 2.5 times higher during severe smoke haze in EBB than in LBB. AAE440/870 (EC mass in μg m−3) was 1.43 ± 0.14 (4.3 ± 0.8), 1.44 ± 0.07 (5.5 ± 1.5), 1.54 ± 0.03 (7.6 ± 1.4), and 1.57 (13.3) for LBB, MBB, HBB, and EBB, respectively, mainly due to the presence of absorbing carbonaceous aerosols attributed to BB smoke particles over Chiang Mai. The highest AAOD440 and AAE440/870 were recorded along with the

highest EC concentration during EBB, indicating the presence of more fresh smoke (the contribution of BB to PM2.5 ≈ 79%). 4.5. Aerosol optical properties: comparison with worldwide BB and urban sites In order to examine the spatial variability of aerosol optical properties, we compared data sets for BB and urban aerosols from different AERONET sites (reported by different investigators from earlier observations) both within and without PSEA. Table 3 summarizes and compares the annual or periodic mean values for aerosol optical and microphysical properties in northern PSEA, Australia (Jabiru), Mexico, Amazonia, and the Savanna, as well as boreal and worldwide urban regions. The mean AOD500 values were similar throughout northern PSEA, except over DAK, in 2013 and 2014. Lower values of AOD500 than those found in this study were reported in northern PSEA during the dry season by Janjai et al. (2012) and Gautam et al. (2013), indicating the higher pollution load in 2014 than earlier years. AOD (as shown in Table 3) for BB aerosols varied notably over the BB regions (AOD440: 0.74–2.28; AOD500: 0.75–2.18; AOD670: 0.36–0.84) and urban areas (AOD440: 0.19–0.69) across the world. AE440/870 (≥1.7) was similar over all the sites in the northern PSEA region (Pani et al., 2016b; Wang et al., 2015). Likewise, Janjai et al. (2012) reported the maximum values for the BB-influenced AE440/870 in March (1.5–1.7) over northern PSEA. However, lower values of AE440/870 than those found in this study were reported in northern PSEA during February–April, specifically, Chiang Mai (1.54 ± 0.2 in 2008–2009), Mukdahan (1.51 ± 0.22 in 2006–2009), Phimai (1.51 ± 0.21 in 2006–2008), and Silpakorn University (1.43 ± 0.27 in 2007–2010), which were associated with smoke and urban aerosols (Gautam et al., 2013). Previous studies have shown that BB smoke particles change rapidly in size and composition after being emitted into the atmosphere (Westphal and Toon, 1991; Liousse et al., 1995; Reid et al., 1998; Salinas et al., 2013). Moreover, an increase in particle size (or a decrease in AE) may be related to aerosol aging and can be triggered by condensation, coagulation, and gas-toparticle conversion (Reid and Hobbs, 1998; Salinas et al., 2013). Higher values for and a similar trend in AE440/870 were observed for BB aerosols over northern PSEA (1.4–1.8), Amazonia (1.2–2.1), the boreal region (0.71–2.3), Mexico (1.4–1.7), the Savanna (1.7–2.2), and Australia


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Fig. 7. (a–c) Vertical profiles of the aerosol extinction coefficient (532 nm) during the study period; (d) Vertical feature mask and (e) Aerosol subtype over Chiang Mai available from CALIPSO data on 18 March 2014.

(1.9). Similarly, higher variation in AAE440/870 was found over northern PSEA (1.4–1.6), Amazonia (1.7–1.8), the boreal region (1.9–2.2), the savanna (1.4–1.7), and Australia (1.6). SSA (as shown in Table 3) for BB varied notably (ranging from 0.86 to 0.95), being 0.86–0.9 for northern PSEA, 0.88–0.95 for Amazonia, 0.93–0.96 for the boreal region, 0.97–0.98 for Mexico, 0.81–0.90 for the savanna, and 0.88 for Australia. The differences in magnitude mainly arise from differences in the combustion type and fuel-source moisture content as well as aerosol aging. Savanna and northern-PSEA smoke exhibited the strongest absorption, whereas Mexico BB exhibited the weakest aerosol absorption. AP440 (as shown in Table 3) attributed to

BB was varied 0.67–0.71 for northern PSEA, 0.68–0.69 for Amazonia, 0.69–0.70 for the boreal region, 0.64–0.68 for the savanna, and 0.69 for Australia. REFR440 over all the regions was found to be in the range of 1.38–1.52. The highest REFI440 for BB aerosols was observed over northern PSEA (0.01–0.03), followed by the savanna (≈0.02), Australia (0.02), Amazonia (0.01–0.02), and the boreal region (0.00–0.01). The fine-mode effective radii (Reff_F) obtained over northern PSEA (0.14– 0.16) were similar to those over Amazonia (0.13–0.16), Mexico (0.17), the savanna (0.13–0.14), and Australia (0.15) but considerably smaller than those over the boreal region (0.15–0.33) region, perhaps due to different RH conditions, as aerosols swell hygroscopically in high RH

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Fig. 8. (a–b) Comparison of observations from AERONET and OPAC-derived aerosol optical properties for over Chiang Mai; (c) ARF at SFC, TOA, and at atmosphere. The numbers over each bar represents the value of forcing in W m−2. The red-colored line is showing the atmospheric heating rate (K d−1).

conditions (e.g., Grandey et al., 2013). From this comparison (Table 3), we can conclude that the optical properties of BB aerosols over northern PSEA are distinct from those of other regions. AE was smaller for urban aerosols than BB aerosols in many cases, except over the USA and France. Significant variation in the SSA440 (0.77–0.98) was observed among the urban aerosols, whereas AP440 varied less (0.68–0.74). REFR440 for urban aerosols (1.4–1.5) was the same as for BB aerosols, excluding Beijing aerosols, and REFI440 varied between 0.01 and 0.03. 4.6. Relationship between surface PM and columnar AOD It is a necessary practice nowadays for atmospheric scientists to establish a link between the columnar AOD (either from ground-based instruments or space-borne retrievals) and surface PM (PM2.5 and PM10). AOD is used as a proxy for surface PM levels, allowing monitoring from space (e.g., Hoff and Christopher, 2009); as well as direct AOD–PM relationships (Sayer et al., 2016), and to develop a cost-effective monitoring approach (Kumar et al., 2015). In this study, PM was found to be moderately consistent with the AOD from AERONET and MODIS based on their linear-regression analysis. The correlation coefficients for both sizes of PM were found to be moderate to high with AERONET-AOD (r = 0.61 for PM2.5 and r = 0.48 for PM10), MODIS-AOD-Terra (r = 0.65 for PM2.5 and r = 0.46 for PM10), and MODIS-AOD-Aqua (r = 0.34 for PM2.5 and r = 0.76 for PM10). It is worth noting that here; AERONETAOD was strongly correlated (supplement Fig. S2) with both MODISAOD-Terra (r = 0.94) and MODIS-AOD-Aqua (r = 0.98). Recently, the MODIS Terra- and Aqua-derived AODs were validated agreeing well with in-situ ground-based observations (e.g., AERONET) over DAK in northern PSEA in the dry season of 2015 (Sayer et al., 2016). The association between PM and AOD is highly specific to the region. Table 4 summarizes the correlation between AOD and PM over different locations worldwide. In general, a higher PM–AOD correlation can be expected over regions where aerosols truly exist in a well-mixed ABL during the satellite overpass times (e.g., over Bucharest and Alabama). 4.7. Aerosol vertical profiles The vertical profile of the mean aerosol extinction coefficient at 532 nm (Fig. 7a–c) obtained from CALIPSO near Chiang Mai shows the aerosol vertical distribution over the city during LBB (6 April; Fig. 7a); MBB (averaged for 14 and 23 March; Fig. 7b), and EBB (21 March; Fig. 7c). Note that the CALIOP nighttime detection was used for the LBB and EBB events because daytime data were unavailable. Probably due to unavailable data from CALIPSO, the aerosol vertical profile for MBB

was used for HBB in ARF estimation. The aerosol vertical profile on 21 March i.e., EBB (peak aerosol extinction ≈8 km−1) shows that most of the total aerosol extinction was from a layer within 1 km (≈70%) and displays the confinement of aerosols near to the surface via thermal capping (e.g., Wang et al., 2015; Khamkaew et al., 2016), which corroborates the previous discussion of PM loadings and AOD in this study. The maximum height of the aerosol layer during HBB was about 4– 5 km, as depicted by the vertical feature mask image (Fig. 7d) for 18 March, and almost the same as the height in the MBB profile (Fig. 7b). Moreover, on that particular day (18 March), the vertical distribution of aerosol subtypes (Fig. 7e) also shows the high prevalence of smoke over Chiang Mai. Overall, the analysis of the made from satellite-retrieved aerosol vertical distributions indicates the severity of BB aerosol and loading over the Chiang Mai urban atmosphere.

4.8. Radiative forcing analysis The clear-sky SW ARF over Chiang Mai was estimated using the methodology described in Section 3.3. The OPAC-derived aerosol optical properties (viz., AOD, AAOD, SSA, and AP) matched the observations (Fig. 8a–b) well. The observed and OPAC-derived spectral AODs for the each of the clusters were in good agreement (supplemental Fig. S3); hence initial assumptions in the OC/EC estimation, or OPAC optical model do not have a significant impact on the ARF estimation (Satheesh and Srinivasan, 2006; Pani, 2013; Pani et al., 2016b). ARFSFC and ARFTOA (Fig. 8c) ranged between −45.3 to −103.4 and − 1.7 to 6.2 W m−2, respectively within the clusters and in the order of EBB N HBB N MBB N LBB. Variations in ARF were mainly observed due to the variations in AOD and SSA (Ramachandran et al., 2012; Pani et al., 2016b). EBB showed the highest negative ARFSFC and positive ARFTOA mainly due to having the strongest aerosol absorption (AAOD440 ≈ 0.38; AAE440/ −3 ). The estimated 870 ≈ 1.57) as a result of the highest EC (13.3 μg m ARFSFC for Low-BB (−45.3 W m−2) was found similar to those reported for fire-impacted Arctic atmosphere (−46 W m−2; Stone et al., 2008) and central Himalayas (−53 W m−2; R. Kumar et al., 2011). ARFSFC for High-BB (−65.8 W m−2) over Chiang Mai was found similar (− 73 W m−2) to that reported for extremely fresh BB outbreak in Barcelona, Spain on 23 July 2009 (Sicard et al., 2012). ARF estimates during EBB indicated an additional cooling at the surface (58.1 W m−2) and heating at TOA (7.9 W m−2) as compared to LBB. A higher negative ARFSFC indicates a significant decrease in solar radiation at the surface that is enhanced by atmospheric absorption. ARFATM estimates (Fig. 8c) indicated 2.5 times enhancement of heating at atmosphere during


EBB than during LBB, which translated to an additional atmospheric warming of 66 W m−2. The aerosol radiative efficiency (ARE), the change in ARF per unit AOD, is an effective criterion for quantifying and comparing aerosol radiative effects at different places under different aerosol conditions (Satheesh and Ramanathan, 2000; Pani et al., 2016a, 2016b). The mean values for the total ARESFC (ARETOA) in W m−2 per unit AOD500 were − 46.3 (−1.7), −45.9 (−0.5), −49.2 (2.4), and − 49.2 (2.5) for LBB, MBB, HBB, and EBB, respectively. ARESFC and ARETOA were much higher over Chiang Mai than the values reported over DAK (Pani et al., 2016b), which is attributable to the higher aerosol absorption mainly due to the higher EC during the dry season of 2014 than that of 2013 in northern PSEA. The atmospheric heating rate (Fig. 8c) also followed the order of AOD500, AAOD440, and AAE440/870 in terms of the severity and magnitude of BB emissions and was about ≈2.5 times higher during EBB (3.6 K d−1) than LBB (1.4 K d−1). 5. Implications 5.1. Air quality and public health The overall mean PM2.5 (i.e., 93 μg m−3) over Chiang Mai was found to be ≈4 times higher than the WHO standard for a 24-h exposure level (i.e., 25 μg m−3), but this factor increased to ≈9 during severe smoke episodes (EBB ≈223 μg m−3), resulting in visibility as low as 4 km. Such a decrease in the horizontal visibility can cause road accidents and flight delays/cancellations at the nearby Chiang Mai International Airport (CNX). Moreover, severe haze conditions can also adversely affect the local inhabitants' physiological and psychological well-being and damage materials, plants, forests, and ecosystems. Each year, millions of tourists visit Chiang Mai to see the national parks and wilderness areas, but this type of haze can lead to fewer visitors or shorter stays. Additionally, inhaling such a large concentration of finer PM can cause serious health problems such as respiratory illnesses for the local inhabitants. Pongpiachan and Paowa (2015) showed the impact of air pollution on daily hospital walk-ins and admissions in Chiang Mai especially in the dry season of 2007–2013. Kumharn and Hanprasert (2016) recently reported an increase in the number of patients with respiratory disease due to the increased occurrences of atmospheric turbidity from BB over Chiang Mai with respect to AERONET-retrieved aerosol optical properties. 5.2. Climatic implications: regional and transboundary The substantial impact of northern-PSEA BB on the regional-totransboundary climate was highlighted with data measurements over some near-source locations in northern PSEA as well as over the South China Sea (SCS), East Asia (southern and northeastern China, Hong Kong, Taiwan, and Japan), and the western Pacific Rim (Lin et al., 2013). Excessively thick smoke in northern PSEA was observed on 21 March 2014 with AOD500 reaching as high as 4.3 (Wang et al., 2015); the value over Chiang Mai was 2.45. Our study also revealed the presence of highly absorbing aerosols (SSA ≈ 0.86–0.91) over the Chiang Mai urban atmosphere in northern PSEA in 2014. The atmospheric solar heating mainly due to the absorbing biomass BC (ECBB) was found to be 2–3 times larger over Chiang Mai than over the near-source BB regional site of DAK in northern PSEA and the transboundary location over northern SCS (Pani et al., 2016a, 2016b). Such substantial atmospheric heating due to regional BC over northern PSEA can have a significant impact on atmospheric circulation prior to the onset of the Asian summer monsoon; can evaporate the low-level clouds occurring in a decrease in planetary-albedo and cloud-cover (Ackerman et al., 2000). As discussed elsewhere (Lin et al., 2013), this atmospheric heat can be transported to the downwind regions, where it can boost greenhouse/aerosol warming. Substantial atmospheric heating accompanying with surface dimming (as seen for HBB and EBB in this current

907

study) can disturb the surface-energy budget, regional hydrological cycle (Ramanathan et al., 2001); it can also decrease the crop productivity due to less sunlight being available for photosynthesis (Auffhammer et al., 2006). We propose long-term and extensive field measurements to upgrade our knowledge of BB-induced radiative impacts over urban cities in northern PSEA as well as downwind locations in East Asia. 6. Summary A detailed analysis of the chemical composition of BB aerosols and their microphysical, optical, and radiative properties during the dry season in 2014 over an urban atmosphere in northern PSEA is presented here. The urban atmosphere above the city of Chiang Mai (18.795°N, 98.957°E, and 354 msl) in northern PSEA receives large amounts of aerosols from the regular practice of BB in this region. Enhanced concentrations of PM2.5 due to the BB contribution (25–79%) were found to exceed national/international standards. PM2.5 during EBB was ≈4 times higher than during LBB. It is likely that the concentrations of cations, anions, trace metals, OC, EC, and LG were also associated with the regional BB emissions. The contribution of BB to OC and EC was 27–84% and 25– 2− 83%, respectively. Some important ratios, viz., NO− 3 /SO4 (0.1–0.8; b1), OC/EC (5.3 ± 0.09), and nss-K+/EC (0.21–0.66) also indicated the high prevalence of BB over Chiang Mai. Similarly, changes in aerosol optical properties were consistent with the severity of BB. Overall, the aerosol optical properties were attributable to BB, owing to the dominance of strongly absorbing (columnar SSA440 ≈ 0.81–0.91, AP440 ≈ 0.67–0.73, AAOD440 ≈ 0.12–0.38, and AAE ≈ 1.30–1.70) fine-mode (FMF500 ≈ 0.90–0.99, AE440/870 ≈ 1.53– 1.87) aerosols over Chiang Mai. Cluster variations in the aerosol microphysical and optical properties followed the sequence of BB magnitude (i.e., EBB N HBB N MBB ≥ LBB). The satellite-retrieved aerosol vertical distribution indicated that aerosols were trapped near the surface during severe BB and also confirmed the presence of a thick smoke layer. ARFSFC, ARFATM, and the atmospheric heating rate followed the order of EBB N HBB N MBB N LBB and were ≈2.5 times higher during EBB than LBB. The highest positive ARFTOA during EBB was primarily due to the highest EC, which resulted from fresh smoke. This study provides necessary information for understanding the radiative effects of BB-influenced aerosols over Chiang Mai during the 7SEAS/BASELInE 2014 campaign, which has significant implications for regional air quality, public health, and the climate. The results indicate enhanced surface dimming as well as the atmospheric heating over northern PSEA, mainly due to BB-produced carbonaceous aerosols (primarily EC), which may be larger by 2–3 times than in other regional and transboundary locations. Furthermore, the findings from this study clearly establish the need to focus on scientifically assessing the transport of EC emitted from BB in northern PSEA to downwind sites and its potential warming effect, which may accelerate changes in regional climate. Acknowledgments S. K. Pani sincerely thanks the Ministry of Science and Technology of Taiwan (Project Number: MOST 105-2119-M-088-014 and 106-2811M-008-032) and Taiwan Environmental Protection Administration (EPA-104-U1L1-02-101) for financial support. The 7-SEAS/BASELInE and AERONET projects were supported by the NASA Earth Observing System and Radiation Sciences Program. CALIPSO data were obtained from the Atmospheric Science Data Center at NASA's Langley Research Center. The authors also acknowledge the NOAA–ARL for providing the HYSPLIT model and the NCEP/NCAR Reanalysis team for providing synoptic meteorological maps. Other raw data used to produce the results of this paper can be obtained from the corresponding authors upon request. Lastly, we thank the editor and two anonymous reviewers for their insightful comments and suggestions, which substantially improved this manuscript.

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Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi. org/10.1016/j.scitotenv.2018.03.204.

References Acid Deposition Monitoring Network in East Asia (EANET), 2000. Technical Document for Filter Pack Method in East Asia. Ackerman, A.S., Toon, O.B., Stevens, D.E., Heymsfield, A.J., Ramanathan, et al., 2000. Reduction of tropical cloudiness by soot. Science 288, 1042–1047. Andreae, M.O., 1983. Soot carbon and excess fine potassium: long-range transport of combustion-derived aerosols. Science 220, 1148–1151. Andreae, M.O., 1993. The influence of tropical biomass burning on climate and the atmospheric environment. In: Oremland, R.S. (Ed.), Biogeochemistry of Global Change. Springer, Boston, MA, pp. 113–150. Andreae, M.O., Merlet, P., 2001. Emission of trace gases and aerosols from biomass burning. Glob. Biogeochem. Cycles 15 (4), 955–966. Andrews, E., Sheridan, P.J., Fiebig, M., McComiskey, M., Ogren, J.A., Arnott, P., Covert, D., Elleman, R., Gasparini, R., Collins, D., Jonsson, H., Schmid, B., Wang, J., 2006. Comparison of methods for deriving aerosol asymmetry parameter. J. Geophys. Res. 111, D05S04. https://doi.org/10.1029/2004JD005734. Auffhammer, M., Ramanathan, V., Vincent, J.R., 2006. Integrated model shows that atmospheric brown clouds and greenhouse gases have reduced rice harvests in India. PNAS 103:19668–19672. https://doi.org/10.1073/pnas.0609584104. Balasubramanian, R., Qian, W.B., Decesari, S., Facchini, M.C., Fuzzi, S., 2003. Comprehensive characterization of PM2.5 aerosols in Singapore. J. Geophys. Res. 108 (D16): 4523. https://doi.org/10.1029/2002JD002517. Barladeanu, R., Stefan, S., Radulescu, R., 2012. Correlation between the particulate matter (PM10) mass concentrations and aerosol optical depth in Bucharest, Romania. Rom. Rep. Phys. 64 (4), 1085–1096. Berg, J.W.W., Winchester, J.W., 1978. Aerosol chemistry of marine atmosphere. Chem. Oceanogr. 7, 173–231. Bergstrom, R.W., Pilewskie, P., Russell, P.B., Redemann, J., Bond, T.C., Quinn, P.K., Sierau, B., 2007. Spectral absorption properties of atmospheric aerosols. Atmos. Chem. Phys. 7: 5937–5943. https://doi.org/10.5194/acp-7-5937-2007. Bernard, P.C., Van Grieken, R.E., 1992. Comparison and evaluation of hierarchical cluster techniques applied to automated electron probe X-ray microanalysis data. Anal. Chim. Acta 267, 81–93. Boucher, O., Randall, D., Artaxo, P., Bretherton, C., Feingold, G., Forster, P., Kerminen, V.M. K.Y., Liao, H., Lohmann, U., Rasch, P., Satheesh, S.K.S.S., Stevens, B., Zhang, X.Y., 2013. Contribution of working group to the fifth assessment report of the intergovernmental panel on climate change. clouds and aerosols. In: Stocker, T.F., Qin, D., Plattner, G. K., Tignor, M., Allen, S.K., Boschung, J., Nauels, A., Xia, Y., Bex, V., Midgley, P.M. (Eds.), Climate Change 2013: The Physical Science Basis. IPCC, pp. 616–617. Campbell, J.R., Reid, J.S., Westphal, D.L., Zhang, J., Tackett, J.L., Chew, B.N., Welton, E.J., Shimizu, A., Sugimoto, N., Aoki, K., Winker, D.M., 2013. Characterizing the vertical profile of aerosol particle extinction and linear depolarization over Southeast Asia and the maritime continent: the 2007–2009 view from CALIOP. Atmos. Res. 122, 520–543. Cao, J.J., Lee, S.C., Ho, K.F., Zou, S.C., Fung, K., Li, Y., Watson, J.G., Chow, J.C., 2004. Spatial and seasonal variations of atmospheric organic carbon and elemental carbon in Pearl River Delta Region, China. Atmos. Environ. 38, 4447–4456. Cao, J.J., Wu, F., Chow, J.C., Lee, S.C., Li, Y., Chen, S.W., An, Z.S., Fung, K., Watson, J.G., Zhu, C. S., Liu, S.X., 2005. Characterization source apportionment of atmospheric organic and elemental carbon during fall and winter of 2003 in Xi’an, China. Atmos. Chem. Phys. 5, 3127–3137. Chantara, S., Chunsuk, N., 2008. Comparison of wet-only and bulk deposition at Chiang Mai (Thailand) based on rainwater chemical composition. Atmos. Environ. 42, 5511–5518. Chantara, S., Sillapapiromsuk, S., Wiriya, W., 2012. Atmospheric pollutants in Chiang Mai (Thailand) over a five-year period (2005–2009), their possible sources and relation to air mass movement. Atmos. Environ. 60, 88–98. Chew, B.N., Campbell, J.R., Hyer, E.J., Salinas, S.V., Reid, J.S., Welton, E.J., Holben, B.N., Liew, S.C., 2016. Relationship between aerosol optical depth and particulate matter over Singapore: effects of aerosol vertical distributions. Aerosol Air Qual. Res. 16, 2818–2830. Choi, J., Chung, C.E., 2014. Sensitivity of aerosol direct radiative forcing to aerosol vertical profile. Tellus. Ser. B. 66, 24376. https://doi.org/10.3402/tellusb.v66.24376. Chu, D.A., Kaufman, Y.J., Zibordi, G., Chern, J.D., Mao, J.M., Li, C., Holben, H.B., 2003. Global monitoring of air pollution over land from EOS-Terra MODIS. J. Geophys. Res. 108 (D21):4661. https://doi.org/10.1029/2002JD003179. Chu, J.E., Ha, K.J., 2016. Quantifying organic aerosol single scattering albedo over the tropical biomass burning regions. Atmos. Environ. 147, 67–78. Chuang, M.-T., Chou, C.-K., Sopajareepom, K., Lin, N.-H., Wang, J.-L., Sheu, G.-R., Chang, Y.C., Lee, C.-T., 2013. Characterization of aerosol chemical properties from near-source biomass burning in Chiang Mai, Thailand during 7-SEAS/Dongsha experiment. Atmos. Environ. 78, 72–81. Crutzen, P.J., Andreae, M.O., 1990. Biomass burning in the tropics: impact on atmospheric chemistry and biogeochemical cycles. Science 250, 1669–1678. Draxler, R.R., Rolph, G.D., 2003. HYSPLIT (Hybrid Single-Particle Lagrangian Integrated Trajectory) Model Access via NOAA ARL READY Website. NOAA Air Resources Laboratory, Silver Spring, MD http://www.arl.noaa.gov/ready/hysplit4.html.

Dubovik, O., King, M.D., 2000. A flexible inversion algorithm for retrieval of aerosol optical properties from sun and sky radiance measurements. J. Geophys. Res. 105 (D16): 20,673–20,696. https://doi.org/10.1029/2000JD900282. Dubovik, O., Smirnov, A., Holben, B.N., King, M.D., Kaufman, Y.J., Eck, T.F., Slutsker, I., 2000. Accuracy assessments of aerosol optical properties retrieved from Aerosol Robotic Network (AERONET) sun and sky radiance measurements. J. Geophys. Res. 105 (D8):9791–9806. https://doi.org/10.1029/2000JD900040. Dubovik, O., Holben, B.N., Eck, T.F., Smirnov, A., Kaufman, Y.J., King, M.D., Tanre, D., Slutsker, I., 2002. Variability of absorption and optical properties of key aerosol types observed in worldwide locations. J. Atmos. Sci. 59, 590–608. Dubovik, O., Sinyuk, A., Lapyonok, T., Holben, B.N., Mishchenko, M., Yang, P., Eck, T.F., Volten, H., Muñoz, O., Veihelmann, B., van der Zande, W.J., Leon, J.F., Sorokin, M., Slutsker, I., 2006. Application of spheroid models to account for aerosol particle nonsphericity in remote sensing of desert dust. J. Geophys. Res. 111 (D11): 2156–2202. https://doi.org/10.1029/2005JD006619. Dumka, U.C., Tiwari, S., Kaskaoutis, D.G., Hopke, P.K., Singh, J., Srivastava, A.K., Bisht, D.S., Atrri, S.D., tyagi, S., Misra, A., Pasha, G.S.M., 2016. Assessment of PM2.5 chemical compositions in Delhi: primary vs secondary emissions and contribution to light extinction coefficient and visibility degradation. J. Atmos. Chem. 74 (4), 423–450. Eck, T.F., Holben, B.N., Reid, J.S., Dubovik, O., Smirnov, A., O'Neill, N.T., Slutsker, I., Kinne, S., 1999. Wavelength dependence of the optical depth of biomass burning, urban, and desert dust aerosols. J. Geophys. Res. 104 (D24):31,333–31,349. https://doi.org/ 10.1029/1999JD900923. Eck, T.F., Holben, B.N., Reid, J.S., O'Neill, N.T., Schafer, J.S., Dubovik, O., Smirnov, A., Yamasoe, M.A., Artaxo, P., 2003a. High aerosol optical depth biomass burning events: a comparison of optical properties for different source regions. Geophys. Res. Lett. 30 (2035). https://doi.org/10.1029/2003GL017861. Eck, T.F., Holben, B.N., Ward, D.E., Mukelabai, M.M., Dubovik, O., Smirnov, A., Schafer, J.S., Hsu, N.C., Piketh, S.J., Queface, A., Le Roux, J., Swap, R.J., Slutsker, I., 2003b. Variability of biomass burning aerosol optical characteristics in southern Africa during the SAFARI 2000 dry season campaign and a comparison of single scattering albedo estimates from radiometric measurements. J. Geophys. Res. 108 (8477). https://doi. org/10.1029/2002JD002321. Engel-Cox, J.A., Holloman, C.H., Coutant, B.W., Hoff, R.M., 2004. Qualitative and quantitative evaluation of MODIS satellite sensor data for regional and urban scale air quality. Atmos. Environ. 38, 2495–2509. Estellés, V., Martınez-Lozano, J.A., Utrillas, M.P., Campanelli, M., 2007. Columnar aerosol properties in Valencia (Spain) by ground-based sun photometry. J. Geophys. Res. 112, D11201. https://doi.org/10.1029/2006JD008167. Fine, P., Cass, G., Simoneit, B.R.T., 2001. Chemical characterization of fine particle emissions from fireplace combustion of woods grown in the Northeastern United States. Environ. Sci. Technol. 35, 2665–2675. Fox, J., Fujita, Y., Ngidang, D., Peluso, N., Potter, L., Sakuntaladewi, N., Sturgeon, J., Thomas, D., 2009. Policies, political economy, and Swidden in Southeast Asia. Hum. Ecol. 37: 305–322. https://doi.org/10.1007/s10745-009-9240-7. Fraser, M., Lakshmanan, K., 2000. Using levoglucosan as a molecular marker for the longrange transport of biomass combustion aerosols. Environ. Sci. Technol. 34: 4560–4564. https://doi.org/10.1021/es991229l. Gautam, R., Hsu, N.C., Eck, T.F., Holben, B.N., Janjai, S., Jantarach, T., Tsay, S.-C., Lau, K.-M., 2013. Characterization of aerosols over the Indochina peninsula from satellite-surface observations during biomass burning pre-monsoon season. Atmos. Environ. 78, 51–59. Genga, A., Baglivi, F., Siciliano, M., Siciliano, T., Tepore, M., Mirocci, G., Tortorella, C., Aiello, D., 2012. SEM-EDS investigation on PM10 data collected in Central Italy: principal component analysis and hierarchical cluster analysis. Chem. Cent. J. 6 (Suppl. 2):S3. http://journal.chemistrycentral.com/content/6/S2/S3. Giannoni, M., Martellini, T., Bubba, M.D., Gambaro, A., Zangrando, R., Chiari, M., Lepri, L., Cincinelli, A., 2012. The use of levoglucosan for tracing biomass burning in PM2.5 samples in Tuscany (Italy). Environ. Pollut. 167, 7–15. Grandey, B.S., Stier, P., Wagner, T.M., 2013. Investigating relationships between aerosol optical depth and cloud fraction using satellite, aerosol reanalysis and general circulation model data. Atmos. Chem. Phys. 13:3177–3184. https://doi.org/10.5194/acp13-3177-2013 (2013). Guo, J.P., Zhang, X.Y., Che, H.Z., Gong, S.L., An, X., Cao, C.X., et al., 2009. Correlation between PM concentrations and aerosol optical depth in eastern China. Atmos. Environ. 43, 5876–5886. Harrison, R.M., Yin, J., 2000. Particulate matter in the atmosphere: which particle properties are important for its effects on health? Sci. Total Environ. 249, 85–101. Hess, M., Koepke, P., Schult, I., 1998. Optical properties of aerosols and clouds: the software package OPAC. Bull. Am. Meteorol. Soc. 79 (5), 831–844. Hoff, R.M., Christopher, S.A., 2009. Remote sensing of particulate pollution from space: have we reached the promised land? J. Air Waste Manage. Assoc. 59, 645–675. Holben, B.N., Eck, T.F., Slutsker, I., Tanre, D., Buis, J.P., Setzer, A., Vermote, E., Reagan, J.A., Kaufman, Y.J., Nakajima, T., Lavenu, F., Jankowiak, I., Smirnov, A., 1998. AERONET: a federated instrument network and data archive for aerosol characterization. Remote Sens. Environ. 66, 1–16. Holben, B.N., Eck, T.F., Slutsker, I., Smirnov, A., Sinyuk, A., Schafer, J., Giles, D., Dubovik, O., 2006. Aeronet's Version 2.0 quality assurance criteria. Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series. Vol. 6408 of Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series https://doi.org/10.1117/ 12.706524. Hoppel, W.A., Fitzgerald, J.W., Larson, R.E., 1985. Aerosol size distributions in air masses advecting off the East Coast of the United States. J. Geophys. Res. 90 (D1): 2365–2379. https://doi.org/10.1029/JD090iD01p02365. Hu, G.P., Balasubramanian, R., Wu, C.D., 2003. Chemical characterization of rainwater at Singapore. Chemosphere 51, 747–755.

S.K. Pani et al. / Science of the Total Environment 633 (2018) 892–911 IPCC, 2007. In: Metz, B., Davidson, O.R., Bosch, P.R., Dave, R., Meyer, L.A. (Eds.), Climate Change 2007: Contribution of Working Group III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. Jacobson, M.Z., 2000. A physically-based treatment of elemental carbon optics: implications for global direct forcing of aerosols. Geophys. Res. Lett. 27:217–220. https:// doi.org/10.1029/1999GL010968. Janjai, S., Nunez, M., Masiri, I., Wattan, R., Buntoung, S., Jantarach, T., Promsen, W., 2012. Aerosol Optical Properties at Four Sites in Thailand. Atmos. Clim. Sci. 2:441–453. https://doi.org/10.4236/acs.2012.24038. Janta, R., Chantara, S., 2017. Tree bark as bioindicator of metal accumulation from road traffic and air quality map: a case study of Chiang Mai, Thailand. Atmos. Pollut. Res. 8 (5), 956–967. Jian, Y., Fu, T.M., 2014. Injection heights of springtime biomass-burning plumes over peninsular Southeast Asia and their impacts on long-range pollutant transport. 14: 3977–3989. https://doi.org/10.5194/acp-14-3977-2014. Jiang, X., Liu, Y., Yu, B., Jiang, M., 2007. Comparison of MISR aerosol optical thickness with AERONET measurements in Beijing metropolitan area. Remote Sens. Environ. 107, 45–53. Kacenelenbogen, M., Le'on, J.F., Chiapello, I., Tanré, D., 2006. Characterization of aerosol pollution events in France using ground-based and POLDER-2 satellite data. Atmos. Chem. Phys. 6:4843–4849. https://doi.org/10.5194/acp-6-4843-2006. Kanakidou, M., Seinfeld, J.H., Pandis, S.N., Barnes, I., Dentener, F.J., Facchini, M.C., Van Dingenen, R., Ervens, B., Nenes, A., Nielsen, C.J., Swietlicki, E., Putaud, J.P., Balkanski, Y., Fuzzi, S., Horth, J., Moortgat, G.K., Winterhalter, R., Myhre, C.E.L., Tsigaridis, K., Vignati, E., Stephanou, E.G., Wilson, J., 2005. Organic aerosol and global climate modelling: a review. Atmos. Chem. Phys. 5:1053–1123. https://doi.org/10.5194/ acp-5-1053-2005. Kanakidou, M., Myriokefalitakis, S., Tsigaridis, K., Daskalakis, N., 2009. Global sources of organic aerosols in the atmosphere: reconciling model results with observations. Geochim. Cosmochim. Acta 73, A619. + Kang, J., Cho, B.C., Lee, C.B., 2010. Atmospheric transport of water-soluble ions (NO− 3 , NH4 and nss-SO2− 4 ) to the southern East Sea (Sea of Japan). Sci. Total Environ. 408, 2369–2377. Keene, W.C., Pszenny, A.A.P., Galloway, J.N., Hawley, M.E., 1986. Sea-salt corrections and interpretation of constituent ratios in marine precipitation. J. Geophys. Res. Atmos. 91:6647–6658. https://doi.org/10.1029/JD091iD06p06647. Khamkaew, C., Chantara, S., Janta, R., Pani, S.K., Prapamontol, T., Kawichai, S., Wiriya, W., Lin, N.H., 2016. Investigation of biomass burning chemical components over Northern Southeast Asia during 7-SEAS/BASELInE 2014 campaign. Aerosol Air Qual. Res. 16 (11), 2655–2670. Khan, A.F., Shirasuna, Y., Hirano, K., Masunaga, S., 2010. Characterization of PM2.5, PM2.5– 10 and PMN10 in ambient air, Yokohama, Japan. Atmos. Environ. 96 (1), 159–172. Kim Oanh, N.T., Upadhyay, N., Zhuang, Y.-H., Hao, Z.-P., Murthy, D.V.S., Lestari, P., Villarin, J.T., Chengchua, K., Co, H.X., Dung, N.T., Lindgren, E.S., 2006. Particulate air pollution in six Asian cities: spatial and temporal distributions, and associated sources. Atmos. Environ. 40, 3367–3380. Kirchstetter, T.W., Novakov, T., Hobbs, P.V., 2004. Evidence that the spectral dependence of light absorption by aerosols is affected by organic carbon. J. Geophys. Res. 109, D21208. https://doi.org/10.1029/2004JD004999. Kittaka, C., Winker, D.M., Vaughan, M.A., Omar, A., Remer, L.A., 2011. Intercomparison of column aerosol optical depths from CALIPSO and MODIS-aqua. Atmos. Meas. Tech. 4:131–141. https://doi.org/10.5194/amt-4-131-2011. Kong, L., Xin, J., Zhang, W., Wang, Y., 2016. The empirical correlations between PM2.5, PM10 and AOD in the Beijing metropolitan region and the PM2.5, PM10 distributions retrieved by MODIS. Environ. Pollut. 216, 350–360. Kreidenweis, S.M., Remer, L.A., Bruintjes, R., Dubovik, O., 2001. Smoke aerosol from biomass burning in Mexico: hygroscopic smoke optical model. J. Geophys. Res. 106 (D5):4831–4844. https://doi.org/10.1029/2000JD900488. Kumar, R., Naja, M., Satheesh, S.K., Ojha, N., Joshi, H., Sarangi, T., Pant, P., Dumka, U.C., Hegde, P., Venkataramani, S., 2011. Influences of the springtime northern Indian biomass burning over the central Himalayas. J. Geophys. Res. 116, D19302. https://doi. org/10.1029/2010JD015509. Kumar, S., Devara, P.C.S., Dani, K.K., Sonbawne, S.M., Saha, S.K., 2011. Sun-sky radiometer– derived column-integrated aerosol optical and physical properties over a tropical urban station during 2004–2009. J. Geophys. Res. 116, D10201. https://doi.org/ 10.1029/2010JD014944. Kumar, M., Tiwari, S., Murari, V., Singh, A.K., Banerjee, T., 2015. Wintertime characteristics of aerosols at middle Indo-Gangetic Plain: impacts of regional meteorology and long range transport. Atmos. Environ. 104, 162–175. Kumharn, W., Hanprasert, K., 2016. Aerosol optical properties in ultraviolet ranges and respiratory diseases in Thailand. Atmos. Environ. 142, 221–228. Lanz, V.A., Alfarra, M.R., Baltensperger, U., Buchmann, B., Hueglin, C., Szidat, S., Wehrli, M. N., Wacker, L., Weimer, S., Caseiro, A., Puxbaum, H., Prevot, A.H., 2008. Source attribution of submicron organic aerosols during wintertime inversions by advanced factor analysis of aerosol mass spectra. Environ. Sci. Technol. 42, 214–220. Lee, C.T., Chuang, M.T., Lin, N.H., Wang, J.L., Sheu, G.R., Wang, S.H., Huang, H., Chen, H.W., Weng, G.H., Hsu, S.P., 2011. The enhancement of biosmoke from Southeast Asia on PM2.5 water-soluble ions during the transport over the Mountain Lulin site in Taiwan. Atmos. Environ. 45, 5784–5794. Lee, C.T., Ram, S.S., Nguyen, D.L., Chou, C.C.K., Chang, S.Y., Lin, N.H., Chang, S.C., Hsiao, T.C., Sheu, G.R., OuYang, C.F., Chi, K.H., Wang, S.H., Wu, X.C., 2016. Aerosol chemical profile of near-source biomass burning smoke in Sonla, Vietnam during 7-SEAS campaigns in 2012 and 2013. Aerosol Air Qual. Res. 16 (11), 2603–2617. Li, C.C., Mao, J.T., Lau, A.K.H., Yuan, Z.B., Wang, M.H., Liu, X.Y., 2005a. Application of MODIS satellite products on the air pollution research in Beijing. Sci. China Ser. D48 (Suppl. II), 209–219.

909

Li, C., Lau, A.K.H., Mao, J., Chu, D.A., 2005b. Retrieval, validation, and application of the 1km aerosol optical depth from MODIS measurements over Hong Kong. IEEE Trans. Geosci. Remote Sens. 43 (11), 2650–2658. Li, C., Tsay, S.-C., Hsu, N.C., Kim, J.Y., Howell, S.G., Huebert, B.J., Ji, Q., Jeong, M.-J., Wang, S.H., Hansell, R.A., Bell, S.W., 2013. Characteristics and composition of atmospheric aerosols in Phimai, central Thailand during BASE-ASIA. Atmos. Environ. 78, 60–71. Lin, N.H., Tsay, S.C., Reid, J.S., Yen, M.C., Sheu, G.R., Wang, S.H., Chi, K.H., Chuang, M.T., OuYang, C.F., Fu, J.S., Lee, C.T., Wang, L.C., Wang, J.L., Hsu, C.N., Holben, B.N., Chu, Y.C., Maring, H.B., Nguyen, A.X., Sopajaree, K., Chen, S.J., Cheng, M.T., Tsuang, B.J., Tsai, C. J., Peng, C.M., Chang, C.T., Lin, K.S., Tsai, Y.I., Lee, W.J., Chang, S.C., Liu, J.J., Chiang, W. L., 2013. An overview of regional experiments on biomass burning aerosols and related pollutants in Southeast Asia: from BASE-ASIA and Dongsha Experiment to 7SEAS. Atmos. Environ. 78, 1–19. Lin, N.H., Sayer, A.M., Wang, S.H., Loftus, A.M., Hsiao, T.C., Sheu, G.R., Hsu, N.C., Tsay, S.C., Chantara, S., 2014. Interactions between biomass-burning aerosols and clouds over Southeast Asia: current status, challenges, and perspectives. Environ. Pollut. 195, 292–307. Liou, K.N., 1980. An Introduction to Atmospheric Radiation. Academic Press, San Diego, Calif, p. 392. Liousse, C., ByrneDevaux, C., Cachier, H., 1995. Aging of savannah biomass burning aerosols: consequences on their optical properties. J. Atmos. Chem. 22, 1–17. Liu, J., Zheng, Y., Li, Z., Flynn, C., Cribb, M., 2012. Seasonal variations of aerosol optical properties, vertical distribution and associated radiative effects in the Yangtze Delta region of China. J. Geophys. Res. 117, D00K38. https://doi.org/10.1029/ 2011JD016490. Lu, H.C., Chang, C.L., Hsieh, J.C., 2006. Classification of PM10 distributions in Taiwan. Atmos. Environ. 40, 1452–1463. Mangiameli, P., Chen, S.K., West, D., 1996. A comparison of SOM of neural network and hierarchical methods. Eur. J. Oper. Res. 93, 402–417. Mayol-Bracero, O., Guyon, P., Graham, B., Roberts, G., Andreae, M.O., Decesari, S., Facchini, M.C., Fuzzi, S., Artaxo, P., 2002. Water soluble organic compounds in biomass burning aerosols over Amazonia: 2. Apportionment of the chemical composition and importance of the polyacidic fraction. J. Geophys. Res. 107 (8091). https://doi.org/ 10.1029/2001JD000522. McClatchey, R.A., Fenn, R.W., Selby, J.E.A., Volz, F.E., Garing, J.S., 1972. Optical Properties of the Atmosphere. third edition. Air Force Cambridge Research Laboratories (Report AFCRL-72-0497). Metzger, K.B., Tolbert, P.E., Klein, M., Peel, J.L., Flanders, W.D., Todd, K., Mulholland, J.A., Ryan, P.B., Frumkin, H., 2004. Ambient air pollution and cardiovascular emergency department visits. Epidemiology 15, 46–56. Michalsky, J., Anderson, G.P., Barnard, J., Delamere, J., Gueymard, C., Kato, S., Kiedron, P., McComiskey, A., Ricchiazzi, P., 2006. Shortwave radiative closure studies for clear skies duringthe atmospheric radiation measurement 2003 aerosol intensive observation period. J. Geophys. Res. 111, D14S90. https://doi.org/10.1029/2005JD006341. Mkoma, S.L., Kawamura, K., Fu, P.Q., 2013. Contributions of biomass/biofuel burning to organic aerosols and particulate matter in Tanzania, East Africa, based on analysis of ionic species, organic and elemental carbon, levoglucosan and mannosan. Atmos. Chem. Phys. 13, 10325–30338. Muller, D., Mattis, I., Wandinger, U., Ansmann, A., Althausen, D., Stohl, A., 2005. Raman lidar observations of aged Siberian and Canadian forest fire smoke in the free troposphere over Germany in 2003: microphysical particle characterization. J. Geophys. Res. 110, D17201. https://doi.org/10.1029/2004JD005756. Muller, D., Tesche, M., Eichler, H., Engelmann, R., Althausen, D., Ansmann, A., Cheng, Y.F., Zhang, Y.H., Hu, M., 2006. Strong particle light absorption over the Pearl River Delta (south China) and Beijing (north China) determined from combined Raman lidar and sun photometer observations. Geophys. Res. Lett. 33, L20811. https://doi.org/ 10.1029/2006GL027196. Murayama, T., Müller, D., Wada, K., Shimizu, A., Sekiguchi, M., Tsukamoto, T., 2004. Characterization of Asian dust and Siberian smoke with multi-wavelength Raman lidar over Tokyo, Japan in spring 2003. Geophys. Res. Lett. 31, L23103. https://doi.org/ 10.1029/2004GL021105. Nayebare, S.R., Aburizaiza, O.S., Khwaja, H.A., Siddique, A., Hussain, M.M., Zeb, J., Khatib, F., Carpenter, D.O., Blake, D.R., 2016. Chemical characterization and source apportionment of PM2.5 in Rabigh, Saudi Arabia. Aerosol Air Qual. Res. 16, 3114–3129. Niemi, J.V., Tervahattu, H., Vehkamaki, H., Martikainen, J., Laakso, L., Kulmala, M., Aarnio, P., Koskentalo, T., Sillanpaa, M., Makkonen, U., 2005. Characterization of aerosol particle episodes in Finland caused by wildfires in Eastern Europe. Atmos. Chem. Phys. 5, 2299–2310. Noh, Y.M., Muller, D., Shin, D.H., Lee, H., Jung, J.S., Lee, K.H., Cribb, M., Li, Z., Kim, Y.J., 2009. Optical and microphysical properties of severe haze and smoke aerosol measured by integrated remote sensing techniques in Gwangju, Korea. Atmos. Environ. 43 (4), 879–888. Novakov, T., Penner, J.E., 1993. Large contribution of organic aerosols to cloud condensations. Nature 365, 823–826. O'Neill, N.T., Eck, T.F., Holben, B.N., Smirnov, A., Royer, A., Li, Z., 2002. Optical properties of boreal forest fire smoke derived from Sun photometry. J. Geophys. Res. 107, 4125. Pani, S.K., 2013. Sources and Radiative Effects of Ambient Aerosols in an Urban Atmosphere in East India. (Ph. D. Dissertation). Indian Institute of Technology Kharagpur, India (211pp). Pani, S.K., Verma, S., 2014. Variability of winter and summertime aerosols over eastern India urban environment. Atmos. Res. 137, 112–124. Pani, S.K., Wang, S.H., Lin, N.H., Tsay, S.C., Lolli, S., Chuang, M.T., Lee, C.T., Chantara, S., Yu, J. Y., 2016a. Assessment of aerosol optical property and radiative effect for the layer decoupling cases over the Northern South China Sea during the 7-SEAS/Dongsha Experiment. J. Geophys. Res. 121:4894–4906. https://doi.org/10.1002/2015JD024601. Pani, S.K., Wang, S.H., Lin, N.H., Lee, C.T., Tsay, S.C., Holben, B.N., Janjai, S., Hsiao, T.C., Chuang, M.T., Chantara, S., 2016b. Radiative effect of springtime biomass-burning

910


aerosols over northern Indochina during 7-SEAS/BASELInE 2013 campaign. Aerosol Air Qual. Res. 16 (11), 2802–2817. Pani, S.K., Wang, S.H., Lin, N.H., Lee, C.T., Tsay, S.C., Holben, B.N., Janjai, S., Hsiao, T.C., Chuang, M.T., Chantara, S., 2016c. Impact of Springtime Biomass Burning Aerosols on Radiative Forcing over Northern Thailand during the 7-SEAS Campaign (EGU General Assembly 18, EGU2016-11795). Pani, S.K., Lee, C.T., Chou, C.C.K., Shimada, K., Hatakeyama, S., Takami, A., Wang, S.H., Lin, N. H., 2017. Chemical characterization of wintertime aerosols over islands and mountains in East Asia: impacts of the continental Asian outflow. Aerosol Air Qual. Res. 17 (12), 3006–3036. Patterson, E.M., 1981. Optical properties of the crustal aerosol: relation to chemical and physical characteristics. J. Geophys. Res. 86:3236–3246. https://doi.org/10.1029/ JC086iC04p03236. Pitchford, M., Malm, W., Schichtel, B., Kumar, N., Lowenthal, D., Hand, J., 2007. Revised algorithm for estimating light extinction from IMPROVE particle speciation data. J. Air Waste Manage. Assoc. 57, 1326–1336. Podgorny, I.A., Conant, W., Ramanathan, V., Satheesh, S.K., 2000. Aerosol modulation of atmospheric and surface solar heating over the tropical Indian Ocean. Tellus Ser. B 52: 947–958. https://doi.org/10.1034/j.1600-0889.2000.d01-4.x. Pongpiachan, S., Paowa, T., 2015. Hospital out-and-in-patients as functions of trace gaseous species and other meteorological parameters in Chiang-Mai, Thailand. Aerosol Air Qual. Res. 15, 479–493. Pongpiachan, S., Ho, K.F., Cao, J., 2013. Estimation of gas-particle partitioning coefficients (Kp) of carcinogenic polycyclic aromatic hydrocarbons in carbonaceous aerosol collected at Chiang-Mai, Bangkok and Hat-Yai, Thailand. Asian Pac. J. Cancer Prev. 14: 2461–2476. https://doi.org/10.7314/APJCP.2013.14.4.2461. Puxbaum, H., Caseiro, A., Sánchez-Ochoa, A., Kasper-Giebl, A., Claeys, M., Gelencsér, A., Legrand, M., Preunkert, S., Pio, C., 2007. Levoglucosan levels at background sites in Europe for assessing the impact of biomass combustion on the European aerosol background. J. Geophys. Res. 112, D23S05. https://doi.org/10.1029/2006JD008114. Ramachandran, S., Srivastava, R., Kedia, S., Rajesh, T.A., 2012. Contribution of natural and anthropogenic aerosols to optical properties and radiative effects over an urban location. Environ. Res. Lett. 7, 034028. Ramanathan, V., Crutzen, P.J., 2003. New directions: atmospheric brown clouds. Atmos. Environ. 37, 4033–4035. Ramanathan, V., Crutzen, P.J., Lelieveld, J., Mitra, A.P., Althausen, D., Anderson, J., Andreae, M.O., Cantrell, W., Cass, G.R., Chung, C.E., Clarke, A.D., Coakley, J.A., Collins, W.D., Conant, W.C., Dulac, F., Heintzenberg, J., Heymsfield, A.J., Holben, B., Howell, S., Hudson, J., Jayaraman, A., Kiehl, J.T., Krishnamurti, T.N., Lubin, D., McFarquhar, G., Novakov, T., Ogren, J.A., Podgorny, I.A., Prather, K., Priestley, K., Prospero, J.M., Quinn, P.K., Rajeev, K., Rasch, P., Rupert, S., Sadourny, R., Satheesh, S.K., Shaw, G.E., Sheridan, P., Valero, F.P.J., 2001. Indian ocean experiment: an integrated analysis of the climate and the great Indo-Asian haze. J. Geophys. Res. 106, 28371–28398. Ramanathan, V., Ramana, M.V., Roberts, G., Kim, D., Corrigan, C., Chung, C., Winker, D., 2005. Warming trends in Asia amplified by brown cloud solar absorption. Nature 448, 575–578. Reid, J.S., Hobbs, P.V., 1998. Physical and optical properties of young smoke from individual biomass fires in Brazil. J. Geophys. Res. 103:32013–32030. https://doi.org/ 10.1029/98JD00159. Reid, J.S., Hobbs, P.V., Ferek, R.J., Blake, D.R., Martins, J.V., Dunlap, M.R., Liousse, C., 1998. Physical, chemical, and optical properties of regional hazes dominated by smoke in Brazil. J. Geophys. Res. 103, 32059–32080. Reid, J.S., Eck, T.F., Christopher, S.A., Hobbs, P.V., Holben, B., 1999. Use of the Ångstrom exponent to estimate the variability of optical and physical properties of aging smoke particles in Brazil. J. Geophys. Res. 104, 27473–27489. Remer, L.A., Mattoo, S., Levy, R.C., Munchak, L.A., 2013. MODIS 3 km aerosol product: algorithm and global perspective. Atmos. Meas. Tech. 6 (7), 1829–1844. Ricchiazzi, P., Yang, S., Gautier, C., Sowle, D., 1998. SBDART: a research and teaching software tool for plane-parallel radiative transfer in the Earth's atmosphere. Bull. Am. Meteorol. Soc. 79, 2101–2114. Ryu, S.Y., Kwon, B.G., Kim, Y.J., Kim, H.H., Chun, K.J., 2007. Characteristics of biomass burning aerosol and its impact on regional air quality in the summer of 2003 at Gwangju, Korea. Atmos. Res. 84, 362–373. Salinas, S.V., Chew, B.B., Miettinen, J., Campbell, J.R., Welton, E.J., Reid, J.S., Yu, L.E., Liew, S. C., 2013. Physical and optical characteristics of the October 2010 haze event over Singapore: a photometric and lidar analysis. Atmos. Res. 122, 555–570. Satheesh, S.K., 2002. Radiative forcing by aerosols over Bay of Bengal region. Geophys. Res. Lett. 29:2083. https://doi.org/10.1029/2002GL015334. Satheesh, S.K., Ramanathan, V., 2000. Observations of large difference in tropical aerosol forcing at the Earth’s surface and top of the atmosphere. Nature 405, 60–63. Satheesh, S.K., Srinivasan, J., 2006. A method to estimate aerosol radiative forcing from spectral optical depths. J. Atmos. Sci. 63, 1082–1092. Sayer, A.M., Hsu, N.C., Eck, T.F., Smirnov, A., Holben, B.N., 2014. AERONET-based models of smoke dominated aerosol near source regions and transported over oceans, and implications for satellite retrievals of aerosol optical depth. Atmos. Chem. Phys. 14: 11493–11523. https://doi.org/10.5194/acp-14-11493-2014. Sayer, A.M., Hsu, N.C., Hsiao, T.C., Pantina, P., Kuo, F., Ou-Yang, C.F., Holben, B.N., Janjai, S., Chantara, S., Wang, S.H., Loftus, A.M., Lin, N.H., Tsay, S.C., 2016. In-situ and remotelysensed observations of biomass burning aerosols at Doi Ang Khang, Thailand during 7-SEAS/BASELInE 2015. Aerosol Air Qual. Res. 16 (11), 2786–2801. Schaap, M., Apituley, A., Timmermans, R.M.A., Koelemeijer, R.B.A., de Leeuw, G., 2009. Exploring the relation between aerosol optical depth and PM2.5 at Cabauw, the Netherlands. Atmos. Chem. Phys. 9:909–925. https://doi.org/10.5194/acp-9-909-2009. See, S.W., Balasubramanian, R., Wang, W., 2006. A study of the physical, chemical, and optical properties of ambient aerosol particles in Southeast Asia during hazy and nonhazy days. J. Geophys. Res. 111, D10S08. https://doi.org/10.1029/2005JD006180.

Shahsavani, A., Naddafi, K.J., Haghighifard, N.J., Mesdaghinia, A., Yunesian, M., Nabizadeh, R., Arahami, M., Sowlat, M.H., Yarahmadi, M., Saki, H., Alimohamadi, M., Nazmara, S., Motevalian, S.A., Goudarzi, G., 2012. The evaluation of PM10, PM2.5, and PM1 concentrations during the Middle Eastern Dust (MED) events in Ahvaz, Iran, from April through September 2010. J. Arid Environ. 77:72–83. https://doi.org/10.1016/j. jaridenv.2011.09.007. Shettle, E.P., Fenn, R.W., 1979. Models for the Aerosols for the Lower Atmosphere and the Effects of Humidity Variations on Their Optical Properties (AFGL-TR-79-0214 Environmental Research, Paper 676). Sicard, M., Mallet, M., Garcia-Vizcaino, D., Comeron, A., Rocadenbosch, F., Dubuisson, P., Munoz-Porcar, C., 2012. Intense dust and extremely fresh biomass burning outbreak in Barcelona, Spain: characterization of their optical properties and estimation of their direct radiative forcing. Environ. Res. Lett. 7, 034016. https://doi.org/10.1088/ 1748-9326/7/3/034016. Simoneit, B.R.T., 1999. A review of biomarker compounds as source indicators and tracers for air pollution. Environ. Sci. Pollut. Res. 6 (3), 159–169. Simoneit, B.R.T., Kobayashi, M., Mochida, M., Kawamura, K., Lee, M., Lim, H.-J., Turpin, B.J., Komazaki, Y., 2004. Composition and major sources of organic compounds of aerosol particulate matter sampled during the ACE-Asia campaign. J. Geophys. Res. 109, D19S10. https://doi.org/10.1029/2004JD004598. Slater, J.F., Dibb, J.E., Campbell, J.W., Moore, T.S., 2004. Physical and chemical properties of surface and column aerosols at a rural New England site during MODIS overpass. Remote Sens. Environ. 92, 173–180. Smirnov, A., Holben, B.N., Eck, T.F., Dubovik, O., Slutsker, I., 2000. Cloud-screening and quality control algorithms for the AERONET database. Remote Sens. Environ. 73, 337–349. Souza, D.Z., Vasconcellos, P.C., Lee, H., Aurela, M., Saarnio, K., Teinila, K., Hillamo, R., 2014. Composition of PM2.5 and PM10 collected at urban sites in Brazil. Aerosol Air Qual. Res. 14, 168–176 (2014). Srivastava, R., Ramachandran, S., 2013. The mixing state of aerosols over the indo-gangetic plain and its impact on radiative forcing. Q. J. R. Meteorol. Soc. 139, 137–151. Stone, R.S., Anderson, G.P., Shettle, E.P., Andrews, E., Loukachine, K., Dutton, E.G., Schaaf, C., Roman, M.O., 2008. Radiative impact of boreal smoke in the Arctic: observed and modeled. J. Geophys. Res. 113, D14S16. https://doi.org/10.1029/2007JD009657. Strauss, T., von Maltitz, M.J., 2017. Generalising Ward's method for use with Manhattan distances. PLoS One 12 (1), e0168288. https://doi.org/10.1371/journal.pone.0168288. Tao, J., Ho, K.-F., Chen, L., Zhu, L., Han, J., Xu, Z., 2009. Effect of chemical composition of PM2.5 on visibility in Guangzhou, China, 2007 spring. Particuology 7 (1), 68–75. Tao, J., Gao, J., Zhang, L., Wang, H., Qiu, X., Zhang, Z., Wu, Y., Chai, F., Wang, S., 2016. Chemical and optical characteristics of atmospheric aerosols in Beijing during the Asia-Pacific Economic Cooperation China. 2014. Atmos. Environ. 144, 8–16. Toth, T.D., Zhang, J., Campbell, J.R., Reid, J.S., Vaughan, M.A., 2016. Temporal variability of aerosol optical thickness vertical distribution observed from CALIOP. J. Geophys. Res. Atmos. 121:9117–9139. https://doi.org/10.1002/2015JD024668. Tsai, T.C., Jeng, Y.J., Chu, D.A., Chen, J.P., Chang, S.C., 2011. Analysis of the relationship between MODIS aerosol optical depth and particulate matter from 2006 to 2008. Atmos. Environ. 45, 4777–4788. Tsai, Y.I., Sopajaree, K., Chotruksa, A., Wu, H.C., Kuo, S.C., 2013. Source indicators of biomass burning associated with inorganic salts and carboxylates in dry season ambient aerosol in Chiang Mai Basin, Thailand. Atmos. Environ. 78, 93–104. Tsay, S.C., Hsu, N.C., Lau, W.K.M., Li, C., Gabriel, P.M., Ji, Q., Holben, B.N., Judd Welton, E., Nguyen, A.X., Janjai, S., Lin, N.H., Reid, J.S., Boonjawat, J., Howell, S.G., Huebert, B.J., Fu, J.S., Hansell, R.A., Sayer, A.M., Gautam, R., Wang, S.H., Goodloe, C.S., Miko, L.R., Shu, P.K., Loftus, A.M., Huang, J., Kim, J.Y., Jeong, M.J., Pantina, P., 2013. From BASEASIA toward 7-SEAS: a satellite-surface perspective of boreal spring biomass-burning aerosols and clouds in Southeast Asia. Atmos. Environ. 78, 20–34. Tsay, S.C., Maring, H.B., Lin, N.H., Buntoung, S., Chantara, S., Chuang, H.C., Gabriel, P.M., Goodloe, C.S., Holben, B.N., Hsiao, T.C., Hsu, N.C., Janjai, S., Lau, W.K.M., Lee, C.T., Lee, J., Loftus, A.M., Nguyen, A.X., Nguyen, C.M., Pani, S.K., Pantina, P., Sayer, A.M., Tao, W.K., Wang, S.H., Welton, E.J., Wiriya, W., Yen, M.C., 2016. Satellite-surface perspectives of air quality and aerosol-cloud effects on the environment: an overview of 7SEAS/BASELInE. Aerosol Air Qual. Res. 16 (11), 2581–2602. Vakkari, V., Kerminen, V.M., Beukes, J.P., Tiitta, P., Zyl, P.G.V., Josipovic, M., Venter, A.D., Jaars, K., Worsnop, D.R., Kulmala, M., Laakso, L., 2014. Rapid changes in biomass burning aerosols by atmospheric oxidation. Geophys. Res. Lett. 41:2644–2651. https://doi. org/10.1002/2014GL059396. van Donkelaar, A.V., Martin, R.V., Park, R.J., 2006. Estimating ground-level PM2.5 using aerosol optical depth determined from satellite remote sensing. J. Geophys. Res. 111, D21201. https://doi.org/10.1029/2005JD006996. Verma, S., Pani, S.K., Bhanja, S.N., 2013. Sources and radiative effects of wintertime black carbon aerosols in an urban atmosphere in east India. Chemosphere 90, 260–269. Verma, S., Bhanja, S.N., Pani, S.K., Misra, A., 2014. Aerosol optical and physical properties during winter monsoon pollution transport in an urban environment. Environ. Sci. Pollut. Res. 21:4977–4994. https://doi.org/10.1007/s11356-013-2385-5. Verma, S., Priyadharshini, B., Pani, S.K., Kumar, D.B., Faruqi, A.R., Bhanja, S.N., Mandal, M., 2016. Aerosol extinction properties over coastal West Bengal Gangetic plain under inter-seasonal and sea breeze influenced transport processes. Atmos. Res. 167, 224–236. Viana, M., Maenhaut, W., Chi, X., Querol, X., Alastuey, A., 2007. Comparative chemical mass closure of fine and coarse aerosols at two sites in south and west Europe: implications for EU air pollution policies. Atmos. Environ. 41 (2), 315–326. Wang, L., Christopher, L., 2003. Inter-comparison between satellite-derived aerosol optical thickness and PM2.5 mass: implications for air quality studies. Geophys. Res. Lett. 30 (21). https://doi.org/10.1029/2003GL018174. Wang, Q., Shao, M., Liu, Y., William, K., Paul, G., Li, X., Liu, Y., Lu, S., 2007. Impact of biomass burning on urban air quality estimated by organic tracers: Guangzhou and Beijing as cases. Atmos. Environ. 41, 8380–8390.

S.K. Pani et al. / Science of the Total Environment 633 (2018) 892–911 Wang, S.H., Welton, E.J., Holben, B.N., Tsay, S.C., Lin, N.H., Giles, D., Stewart, S.A., Janjai, S., Nguyen, X.A., Hsiao, T.C., Chen, W.N., Lin, T.H., Buntoung, S., Chantara, S., Wiriya, W., 2015. Vertical distribution and columnar optical properties of springtime biomassburning aerosols over northern Indochina during 2014 7-SEAS campaign. Aerosol Air Qual. Res. 15, 2037–2050. Weingartner, E., Saathoff, H., Schnaiter, M., Streit, N., Bitnar, B., Baltensperger, U., 2003. Absorption of light by soot particles: determination of the absorption coefficient by means of aethalometers. J. Aerosol Sci. 34, 1445–1463. Westphal, D.L., Toon, O.B., 1991. Simulations of microphysical, radiative, and dynamical processes in a continental-scale forest fire smoke plume. J. Geophys. Res. 96, 22379–22400. Wu, S.P., Zhang, Y.J., Schwab, J.J., Huang, S., Wei, Y., Yuan, C.S., 2016. Biomass burning contributions to urban PM2.5 along the coastal lines of southeastern China. 68 (1), 30666. https://doi.org/10.3402/tellusb.v68.30666. Xia, X., Li, Z., Holben, B., Wang, P., Eck, T., Chen, H., Cribb, M., Zhao, Y., 2007. Aerosol optical properties and radiative effects in the Yangtze Delta region of China. J. Geophys. Res. 112, D22S12. https://doi.org/10.1029/2007JD008859. Xu, J.Z., Wang, Z.B., Yu, G.M., Qin, X., Ren, J.W., Qin, D., 2014. Characteristics of water soluble ionic species in fine particles from a high altitude site on the northern boundary

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of Tibetan Plateau: mixture of mineral dust and anthropogenic aerosol. Atmos. Res. 143, 43–56. Yttri, K.E., Lund Myhre, C., Eckhardt, S., Fiebig, M., Dye, C., Hirdman, D., Ström, J., Klimont, Z., Stohl, A., 2014. Quantifying black carbon from biomass burning by means of levoglucosan – a one-year time series at the Arctic observatory Zeppelin. Atmos. Chem. Phys. 14:6427–6442. https://doi.org/10.5194/acp-14-6427-2014. Zhang, T., Claeys, M., Cachier, H., Dong, S., Wang, W., Maenhaut, W., Liu, X., 2008. Identification and estimation of the biomass burning contribution to Beijing aerosol using Levoglucosan as a molecular marker. Atmos. Environ. 42, 7013–7021. Zhang, X., Hecobian, A., Zheng, M., Frank, N.H., Weber, R.J., 2010. Biomass burning impact on PM2.5 over the southeastern US during 2007: integrating chemically speciated FRM filter measurements, MODIS fire counts and PMF analysis. Atmos. Chem. Phys. 10, 6839–6853. Zhang, Z., Gao, J., Engling, G., Tao, J., Chai, F., Zhang, L., Zhang, R., Sang, X., Chan, C.Y., Lin, Z., Cao, J., 2015. Characteristics and applications of size-segregated biomass burning tracers in China's Pearl River Delta region. Atmos. Environ. 102, 290–301.