Influence of biomass burning on local air pollution in mainland Southeast Asia from 2001 to 2016

Accepted Manuscript Influence of biomass burning on local air pollution in mainland Southeast Asia from 2001 to 2016 Shuai Yin, Xiufeng Wang, Xirui Zhang, Meng Guo, Moe Miura, Yi Xiao PII:

S0269-7491(19)31689-6

DOI:

https://doi.org/10.1016/j.envpol.2019.07.117

Reference:

ENPO 12949

To appear in:

Environmental Pollution

Received Date: 1 April 2019 Revised Date:

22 July 2019

Accepted Date: 22 July 2019

Please cite this article as: Yin, S., Wang, X., Zhang, X., Guo, M., Miura, M., Xiao, Y., Influence of biomass burning on local air pollution in mainland Southeast Asia from 2001 to 2016, Environmental Pollution (2019), doi: https://doi.org/10.1016/j.envpol.2019.07.117. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Influence of biomass burning on local air pollution in

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mainland Southeast Asia from 2001 to 2016

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Shuai Yin a*, Xiufeng Wang b, Xirui Zhang c, Meng Guo d, Moe Miura e and Yi Xiao f, g

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a

Center for Global Environmental Research, National Institute for Environmental Studies, Tsukuba 3058506, Japan

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b

Research Faculty of Agriculture, Hokkaido University, Sapporo, 0608589, Japan; [email protected]

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c

School of Mechanics and Electrics Engineering, Hainan University, Haikou 570228, China; [email protected]

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d

School of Geographical Sciences, Northeast Normal University, Changchun 130024, China; [email protected]

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e

School of Agriculture, Hokkaido University, Sapporo, 0608589, Japan; [email protected]

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f

Research Center of the Economy Of the Upper Reaches of the Yangtze River and the Key Research Base of Humanity,

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Ministry of Education, Chongqing Technology and Business University, Chongqing 40067, China g

College of Tourism and Land Resources, Chongqing Technology and Business University, Chongqing 40067, China; [email protected]

Abstract:

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In this study, various remote sensing data, modeling data and emission inventories were integrated to analyze the

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tempo-spatial distribution of biomass burning in mainland Southeast Asia and its effects on the local ambient air quality

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from 2001 to 2016. Land cover changes have been considered in dividing the biomass burning into four types: forest fires,

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shrubland fires, crop residue burning and other fires. The results show that the monthly average number of fire spots peaked

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at 34,512 in March and that the monthly variation followed a seasonal pattern, which was closely related to precipitation and

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farming activities. The four types of biomass burning fires presented different tempo-spatial distributions. Moreover, the

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monthly Aerosol Optical Depth (AOD), concentration of particulate matter with a diameter less than 2.5µm (PM2.5) and

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carbon monoxide (CO) total column also peaked in March with values of 0.62, 45 µg/m3 and 3.25×1018 molecules/cm2,

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respectively. There are significant correlations between the monthly means of AOD (r = 0.74, P < 0.001), PM2.5

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concentration (r = 0.88, P < 0.001), and CO total column (r = 0.82, P < 0.001) and the number of fire spots in the fire season.

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We used Positive Matrix Factorization (PMF) model to resolve the sources of PM2.5 into 3 factors. The result indicated that

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the largest contribution (48%) to annual average concentration of PM2.5 was from Factor 1 (dominated by biomass burning),

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followed by 27% from Factor 3 (dominated by anthropogenic emission), and 25% from Factor 2 (long-range transport/local

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nature source). The annually anthropogenic emission of CO and PM2.5 from 2001 to 2012 and the monthly emission from

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the Emission Database for Global Atmosphere Research (EDGAR) were consistent with PMF analysis and further prove

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that biomass burning is the dominant cause of the variation in the local air quality in mainland Southeast Asia.

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*Corresponding author. E-mail address: [email protected]

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Capsule abstract: There are significant correlations between the monthly AOD, PM2.5 concentration, and CO total column

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and the number of fire spots in the fire season. Biomass burning is the dominant cause of the variation in the local quality in

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mainland Southeast Asia.

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Keywords: AOD; CO; EDGAR; MODIS; PM2.5; PMF.

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1. Introduction

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Biomass burning is an important factor in shaping the landscape of the earth over a long evolutionary scale. It

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is a complex phenomenon involving interactions and feedbacks with climate, ecosystems, and human society

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at multiscale (Harris et al., 2016). The climate change inextricably modulates burning frequency and intensity

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through lightning ignition and drought enhancement, while vegetation types and distributions determine fuel

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load and flammability (Zou et al., 2019). Biomass burning, no matter whether it is anthropogenic or due to

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natural causes, concurrently impose feedback to both climate and ecosystem by releasing large amounts of

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aerosols and greenhouse gases. Carbon and energy exchange are affected immediately after the biomass

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burning by inducing vegetation mortality and restoration, which have effects on soils, drainage,

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decomposition and vegetation composition (Chamber et al., 2005; Liu and Randerson 2008). The massive

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amounts of particulate matter (PM) emissions from biomass burning are one of the largest sources of change

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in the earth’s radiation budget (Myhre et al., 2013) and can alter climates either on a reginal or global scale

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(Andreae, 1991; Kuhlbusch et al., 1996; Li et al., 2003). Organic carbon (OC) and black carbon (BC), the

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main aerosols released from biomass burning, are associated with light scattering and absorbing properties,

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respectively (Vermote et al., 2009). The emitted active trace gases, e.g., volatile organic compounds (VOCs)

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and NOx, are important precursors of O3 and secondary aerosols, which threaten human and ecosystem health

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(Bo et al., 2008; Lin et al., 2013). Many studies have found that the PM emitted from biomass burning is

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strongly associated with increased morbidity, mortality and hospital admissions (Arbex et al., 2007; Arbex et

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al., 2010; Cançado et al., 2001; Johnston et al., 2012; Lee and Schwartz, 1999; Mar et al., 2000; Marlier et al.,

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2013; Reddington et al., 2015). Biomass burning is not only a local pollution source but also a common

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transboundary pollution agent (Targino et al., 2013). Through long-range transport, the pollutants emitted

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from biomass burning deteriorate the air quality downstream the burning areas and even cause severe air

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pollution to other countries (McClure and Jaffe, 2018; Sillanpää et al., 2005; Afroz et al., 2003; Koe et al.,

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2001).

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Southeast Asia accounts for approximately 15% of the world’s tropical forests and it is also susceptible to

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biomass burning (Stibig et al., 2014). Indonesia accounts for a large proportion of Southeast Asia’s forests

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and the peatland fires in this country are one of the largest emitters of CO2 worldwide (Jaenicke et al., 2008).

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Therefore, the biomass burning in Indonesia is consistently a hot research topic and draws worldwide

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attention, especially with respect to several devastating forest fires that were induced by El Niño-Southern

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Oscillation (ENSO) events. During the 1997/1998 ENSO event, the widespread and deep-burning fires in

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Indonesia released approximately 0.95 Gt of carbon and the severe air pollution even affected its neighboring

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countries (van der Werf et al., 2010; Turetsky et al., 2015). For mainland Southeast Asia, mountainous areas

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comprise half of the land in Vietnam, Laos, Thailand, Cambodia, Myanmar and Peninsular Malaysia.

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Swiddening, which is closely related to biomass burning, is a consistent threat to indigenous forests, where

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farmers manage their land by integrating production from both cultivated fields and diverse secondary forests

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(Conklin, 1961; Fujisake et al., 1996). Some studies have investigated the influence of biomass burning on air

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quality in Southeast Asia, especially the Equatorial Asia (Aouizerats et al., 2014; Crippa et al., 2016). These

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studies focus on the severe pollution episodes with short time span (Liu et al., 1999; Heil and Goldammer.,

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2001), the downwind impact and transboundary pollution (Chan et al., 2000; Koplitz, et al., 2016; Reddington

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et al., 2014; Tsai et al., 2012), analyzing the variation of certain air pollutants (Deng et al., 2008; Chan et al.,

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2003) or mainly using the ground-measured air quality data (Engling et al., 2011; Radojevic and Hassan,

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1999).

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Since 1980, remote sensing has become one of the effective methods for monitoring biomass burning and air

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pollutants from the space. These satellites provide sufficient data and critical information for multiple

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disciplines to research biomass burning. For this study, we collected a variety of remote sensing data with a

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long time span (2001-2016) and combined the thermal anomalous spots with the land cover types in each year

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to reveal the seasonal patterns and tempo-spatial distributions of the different biomass burning types in

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mainland Southeast Asia (not including Equatorial Asia). We will discover whether biomass burning was

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effectively controlled in the past two decades, which is an essential reference for the government’s policy

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making. Meanwhile, the Aerosol Optical Depth (AOD), CO and air particulates from remote sensing data or

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modeling data are taken into consideration when analyzing the air pollution using long time series data on

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biomass burning. At last, a receptor model was used to apportion the contribution of various factors to PM2.5

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concentration. The back-trajectory model and anthropogenic emissions inventory were applied to analyze the

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effects of biomass burning and anthropogenic emissions on the local ambient air quality. These results further

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prove which factor is the dominant cause of local air pollution, biomass burning or anthropogenic activities.

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2. Datasets and methodology

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2.1 Datasets

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2.1.1 Fire spots

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The Moderate Resolution Imaging Spectroradiometer (MODIS) was launched aboard Terra satellite on

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December 18, 1999 and on May 4, 2002, a similar instrument was launched on Aqua satellite (Savchenko et al.,

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2004). Considering the different temporal coverage of the products, MODIS/Terra data with a longer

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availability since February 2000 were used in this study. To obtain the biomass burning conditions in mainland

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Southeast Asia, the MOD14A1 version 6 data (spatial resolution: 1 km×1 km), which are the Thermal

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Anomalies/Fire products of MODIS, have been collected (Giglio, 2015). The fire detection strategy was based

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on the absolute detection of fires and on the detection relative to the thermal emissions of surrounding pixels (in

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order to detect weaker fires) (Giglio et al., 2003). In this study, each fire pixel that is extracted from the images

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will be regarded as a fire spot, which is more efficient and makes it easier to directly compare the intensity of

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biomass burning during the different periods. Moreover, to assess the spatial distribution, we created a 0.25°×

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0.25° grid to cover the study region, and, by joining the fire spots with this grid, the distribution maps were

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obtained that present the intensity of biomass burning in mainland Southeast Asia.

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2.1.2 Land cover

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To be consistent with the fire products, the MCD12Q1 land cover data (version 6) also belonging to the MODIS

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production series are adopted for this study (Friedl and Sulla-Menashe, 2015). With the urbanization and major

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shifts from agrarian economies to increasingly commercialized agriculture, the land covers in mainland

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Southeast Asia changed dramatically over the past decades (Fox et al., 2014); therefore, it is essential to

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consider the land use and land cover changes (LULCCs) in order to classify the biomass burning types. The

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temporal resolution of MCD12Q1 is one year, which means that the data are updated annually, and the spatial

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resolution is 500 m × 500 m. In this study, the 17 land cover classes have been divided into 4 groups, as Table

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S-1 shows. We incorporated the annual land cover with the annual fire spots to analyze the tempo-spatial

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changes of the various biomass burning from 2001 to 2016.

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2.1.3 Aerosol Optical Depth (AOD)

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The Aerosol Optical Depth (AOD) is an important index of the light extinction due to aerosol scattering and

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absorption in the atmospheric column and a high AOD means that the visibility is low (van Donkelaar et al.,

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2010). Many studies have used the AOD from remote sensing to retrieve the surface concentration of PM2.5

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(Kumar et al., 2007; van Donkelaar et al., 2006; Wang et al., 2003); therefore, the AOD can be regarded as one

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of the important indicators of air quality. The MODIS Level 3 Atmosphere Products contain statistics that were

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derived from over 100 scientific parameters from the Level 2 Atmosphere products, including the Aerosol,

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Precipitable Water, Cloud, and Atmospheric Profiles. For this study, we used the monthly AOD products

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(MOD08_M3 version 6) with a spatial resolution of 1˚×1˚ to characterize the spatial and temporal variations of

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the aerosol properties and analyze its relationship with the biomass burning in mainland Southeast Asia

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(Platnick et al., 2015).

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2.1.4 PM2.5 from Modern-Era Retrospective analysis for Research and Applications (MERRA)

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The Modern-Era Retrospective analysis for Research and Applications (MERRA) was implemented by the

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National Aeronautics and Space Administration’s (NASA) Global Modeling and Assimilation Office with

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two primary goals: to place the observations from NASA’s satellites into a climate context and to improve

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upon the hydrologic cycle that was determined by earlier generations of reanalysis (Rienecker et al., 2011).

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The simulation of MERRA Aerosol Reanalysis (MERRAero) is performed at a horizontal resolution of 0.5˚

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latitude by 0.625˚ longitude, with 72 vertical layers extending up to 80 km (Da Silva, et al., 2015). The

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temporal coverage of MERRAero is from 1980 and it provides the concentrations of five types of aerosols,

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which include dust, sea salt, black carbon (BC), organic carbon (OC) and sulfate. Buchard et al. (2016)

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proposed a method to calculate PM2.5 concentration from the five aerosols of MERRAero products. In this

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section, we use the same method to explore the changes and spatial distribution of PM2.5 with the biomass

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burning in mainland Southeast Asia. The equation is as follows:

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‫ۻ۾‬૛.૞ = ሾ۲‫܂܁܃‬૛.૞ ሿ + ሾ‫܁܁‬૛.૞ ሿ + ሾ۰۱ሿ + ૚. ૝ × ሾ‫۽‬۱ሿ + ૚. ૜ૠ૞ × ൣ‫۽܁‬૛ି ૝ ൧

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where [DUST2.5], [SS2.5], [BC], [OC], and [SO4 ] are the concentration of dust, sea salt, black carbon, organic

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carbon and sulfate particulate, respectively, all of which have diameters less or equal to 2.5 µm. The SO4

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concentrations used for PM2.5 calculations are assumed to be primarily present in the form of (NH4)2SO4.

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Since MERRAero tracer is the mass of the SO4 ion, it is multiplied by a factor of 1.375. The particulate

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organic matter (POM) is estimated from modeled OC multiplied by a factor which varies between 1.2 and 2.6

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(Malm et al., 2011). A constant value of 1.4 is applied in the simulation (Malm et al., 1994).

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2.1.5 CO from Measurements Of Pollution In The Troposphere (MOPITT)

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Unlike CO2 (long-lived GHGs), the average global lifespan of CO in the atmosphere is only about two months

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and, through a series of complicated photochemical process, CO may be converted into CO2, which is

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accompanied by the formation of O3 (Stroppiana et al., 2010). The two primary surface sources of CO are the

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combustion of fossil fuel and biomass burning (Pétron et al., 2004). MOPITT instrument on board Terra

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started to monitor the CO in the troposphere on March 2000 (Emmons et al., 2004). Since Version 5,

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MOPITT CO products exploit simultaneous near-infrared (NIR) and thermal-infrared (TIR) observations to

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enhance retrieval sensitivity in the lower troposphere (Deeter et al., 2013). In this study, the MOPITT CO

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monthly means (NIR and TIR) Version 8 with a spatial resolution of 1°×1° were used to obtain the monthly

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variation of the CO total column and analyze the biomass burning in mainland Southeast Asia.

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2.1.6 Emission Database for Global Atmosphere Research (EDGAR)

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One of the undesired but partly unavoidable consequences of human activities is the pollution of the

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atmospheric environment (Kampa and Castanas, 2008). In order to objectively and accurately analyze the

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influence of biomass burning on local air quality over a long time span, it is imperative for this study to

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consider the effects of anthropogenic activities. In this study, we choose the widely used emission inventory,

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Emission Database for Global Atmosphere Research (EDGAR), to calculate CO and PM2.5 emission from

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anthropogenic activities, which do not include open biomass burning/ agricultural fires. The purpose of the

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EDGAR was to estimate for 1990 the annual emission per sector of greenhouse gases (GHG), SO2 and

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ozone-depleting compounds (halocarbons) on a regional and grid basis (Olivier et al., 1996). The EDGAR

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inventory has various versions and the newest version, 4.3.2, covering the yearly emissions from 1970 to 2012

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at a resolution of 0.1˚×0.1˚ was used for the interannual analysis, meanwhile, version 4.3.2 also provides the

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monthly emission data in 2010 which was used to analyze the monthly variation and the seasonal pattern in

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study region.

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2.2 Methodology

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Positive Matrix Factorization (PMF) (Paatero and Trapper, 1994; Passtero, 1997) is s model for solving a

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receptor-only, bilinear unmixing model that expresses observation of species as the sum of contributions from

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a number of time-invariant source profiles (Ulbrich et al., 2009; Reff et al., 2007). Different from principal

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component analysis (PCA), sources are constrained to have nonnegative species concentration, and no sample

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can have a negative source contribution in PMF (Ramdan et al., 2000; Lee et al., 1999). As PMF analysis is

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widely used in many studies as an effective source apportionment method of aerosols (Begum et al., 2007;

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Zhang et al., 2010; Song et al., 2006), it was also adopted in this study to estimate contribution from biomass

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burning and other sources to the PM2.5 concentration. Detailed information about EPA-PMF (v5.0) can be

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found

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on

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(https://www.epa.gov/air-research/positive-matrix-factorization-model-environmental-data-analyses).

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further justify the result of PMF, Hybrid Single Particle Lagrangian Integrated Trajectory Model (HYSPLIT)

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(Rolph et al., 2017; Stein et al., 2015), anthropogenic emission and meteorological conditions (e.g., wind

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speed, wind direction) were incorporated to illustrate the effect and contribution of each factor on local air

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pollution.

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3. Results

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3.1 The LULCC and tempo-spatial variation of various biomass burnings

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Table S-2 indicates that the forests in mainland Southeast Asia are experiencing severe deforestation and the

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area decreased by 12.22% from 2001 to 2016, which mainly concentrated in northern Laos and Cambodia;

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meanwhile, shrubland, crop land and other land use types had consecutive increases. Refer to Fig. S-1 in the

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supplementary materials for the spatial distribution of LULCC in mainland Southeast Asia. In the past

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decades, the rapid urbanization, the conversion of primary forests into rubber plantations and prevalent

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swiddening agriculture are assumed to be the main causes of LULCC in the study region (Fox and Vogler,

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2005; Lambin et al., 2003; Ouyang et al., 2016). The total number of fire spots in mainland Southeast Asia

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has remained at a high level during the study period. The annual average exceeded 100,000, and in 2008,

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which had the fewest fire spots, the number still reached 69,035. In 2007, the number of fire spots reached a

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peak with 128,923. In September-October of 2015, devastating forest fires occurred in Indonesia, which were

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the worst since 1997 (Chisholm et al., 2016), and led to persistently hazardous levels of haze pollution. The

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number of fire spots in Indonesia in 2015 was 140,699 (Yin et al., 2018), which is at the same level as the

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number of fire spots in mainland Southeast Asia in 2004, 2007 and 2010. Although the government

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authorities in all countries of Southeast Asia have tried to outlaw swidden agriculture and encourage local

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farmers to adopt permanent agriculture land use (Padoch et al., 2007), the number of the fire spots showed no

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decreasing tendency and has continuously exceeded 100,000 since 2012 (Table S-3).

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Fig. 1. The location of the study region (a); the monthly average precipitation and the number of various biomass burning

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spots (b), and the 100% stack column chart of biomass burning (c) in mainland Southeast Asia from 2001 to 2016.

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The biomass burning in mainland Southeast Asia presents a strong seasonal pattern, which is closely related

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to local farming activities and precipitation. Fig.1b and 1c shows that the biomass burning mainly occurred

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from January to April and the fire spots in these four months accounted for almost 90% of the total annual

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number of fire spots. In March, the number reached almost 35,000, which was the highest number for the

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whole year. Moreover, the fire spots increased dramatically to 56,893 in March 2004, which was the highest

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among the 17 years. Refer to Fig. S-2 in supplementary materials for the monthly variation of the different

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biomass burning types from 2001 to 2016. In May, the number of fire spots suddenly declined to only 2,994

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and, since then, it remained at an extremely low level until December. The Asian summer monsoons bring

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abundant rainfall over Southeast Asia (May/June to September/October) (Matsumoto, 1997) and the increased

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rainfall after May/June also means the start of the rice planting season in most parts of this region. In this

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section, we also obtained the monthly average precipitation of the study region from NOAA's PRECipitation

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REConstruction over Land (PREC/L) database, which uses the Optimum Interpolation (OI) technique to

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assimilate the gauged observation data (Simmons et al., 2010). The results show that the monthly variation in

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the precipitation presents the complete opposite pattern as that of the fire spots (Fig. 1b).

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The forest and shrubland fires, as the principal form of biomass burning in mainland Southeast Asia,

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accounted for 72.70% of the multi-annual average fire spots and were mainly concentrated from February to

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April for the whole year (Fig. 1b). In March, they peaked at 14,267 and 13,571, respectively. Fig.S-3a and 3b

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indicates that, in January, intense forest and shrubland fires only occurred in northern Cambodia. Then, in

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February, the fires started to spread to north Laos and central, eastern and western Myanmar. In the following

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two months, March and April, the forest and shrubland fires in the northwestern part of the study region had

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overwhelmed the fires in northern Cambodia. In addition, from January to April, the spatial distribution of

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forest fires was basically consistent with shrubland fires. Another important form of biomass burning is crop

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residue burning, which accounted for 17.25% of the annual average fire spots and cannot be ignored in this

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study. Different from forest and shrubland fires, the crop residue burning mainly occurred from January to

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March and the numbers of fire spots in these three months are almost equal. In the wet season, most of the

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rice is harvested from November to January in mainland Southeast Asia and crop residue burning is a

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convenient way for local farmers to dispose of massive agricultural waste. Fig.1 shows that, starting in

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December, both the number and the percentage of crop residue burning fires started to increase and, in

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February, the number peaked at 4,814. The spatial distribution of crop residue burning was very different

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from forest and shrubland fires. They were mainly concentrated in Thailand and the extreme west of

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Cambodia in January (Fig. S-3c). Then, in February and March, intense crop residue burning was also found

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in the southern part of Myanmar. Other fires only accounted for 10.05% of the total fire spots and the

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distribution is scattered and showed no obvious aggregation from January to April (Fig. S-3d). For the

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interannual spatial distribution, Fig.S-4 in the supplementary materials shows that the fire spots were mainly

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concentrated in northern Cambodia, western Myanmar and northern Laos. Meanwhile, in 2004, 2007 and

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2010, the intense biomass burning covered more area than it did in other years.

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3.2 Biomass burnings and the variation of AOD, PM2.5 and CO

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3.2.1 AOD

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Fig. 2. The monthly variations of the AOD (a), CO total column(b) and PM2.5 concentrations (c); and the 100% stack

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figure of the various aerosols (d).

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The mean, maximum, minimum, and standard deviation of AOD were obtained from the MOD08 products

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and a 95% confidence interval (CI) for each month was calculated in this study. The results indicated that

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multi-annual monthly means of AODs over mainland Southeast Asia in March (0.62) and April (0.55) are

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much higher than those in other months and the maximums reached 1.39 and 1.18, respectively (Fig. 2a). As

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mentioned in the last section, the severe biomass burning occurred in these two months. In addition, the

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monthly average AODs in March 2010, April 2014 and April 2016 were 0.77, which was the highest number

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among the 17 years. Refer to Fig. S-5 in the supplementary material for the monthly variation of the AOD.

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The monthly AOD after April remained at a low and stable level until next February, which is consistent with

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the variation of the fire spots. To specifically illustrate the effect of biomass burning on local air pollution, the

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study period was divided into two groups, fire season from December to May (accounting for 95.86 % of the

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annual fire spots) and non-fire season from June to November. The results showed that the correlation

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coefficient between the monthly number of fire spots and the AOD in the fire season reached 0.74 (P < 0.001)

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and was only -0.22 (0.01 < P < 0.05) in the non-fire season (Fig. 3a). Therefore, we assume that the intense

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biomass burning during the fire season plays a significant role in affecting local AOD and in the non-fire

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season AOD is possibly affected by long-range transport or anthropogenic activities that will be further

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analyzed in section 3.3. With respect to the spatial distribution, the average AOD in January was only 0.24 in

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mainland Southeast Asia (Fig. 4a). In February, the AOD in the southern part of the study area tended to rise

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as the count of regional fire spots increased. Since the biomass burning was ongoing and continuously

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increasing, the high AOD spread to the northern part of the study area in March and April and it passed that of

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the North China Plain, which is one of the most renowned regions with the worst haze pollution (Tao et al.,

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2012; Fu et al., 2014). For the interannual variation, the annual mean of AOD range from 0.30 to 0.36 also

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correlates with the number of fire spots (r = 0.75, P < 0.01), which was always below 0.35 when the number

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of fire spots did not exceed 100,000 (Table S-3).

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3.2.2 PM2.5

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Fig. 3. The correlation between the monthly number of fire spots and AOD (a), PM2.5 (b) and CO (c).

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Similar to the AOD, the average PM2.5 concentration also peaked in March at 45 µg/m3 (Fig. 2c) and it even

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reached 74 µg/m3 in March 2010, which was the highest value from 2001 to 2016 (Fig. S-6a). Meanwhile, the

269

number of fire spots reached 52,835 in March 2010 (the second highest). Consistent with the monthly

270

variation of fire spots, the PM2.5 concentration was consecutively below 20 µg/m3 from May to next January.

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The correlation coefficient between the monthly number of fire spots and the PM2.5 concentration during the

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fire season and non-fire season was 0.88 (P < 0.001) and 0.04 (P > 0.05), separately (Fig. 3b). Fig 2c and 2d

273

indicate that both the concentration and percentage of BC and OC drastically increased from January to April,

274

given that a large proportion of the annual number of fire spots was concentrated in these four months. The

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correlation coefficients between the monthly number of fire spots and the OC and BC concentration in the fire

276

season was approximately 0.90, which is much higher than that with other types of aerosols (Fig. S-7). OC

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and BC are the typical components of biomass burning aerosols and OC accounts for about two-thirds of the

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biomass burning aerosols (Cachier et al., 1995; Duan et al., 2004). Therefore, we suggest that the massive

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amounts of OC and BC aerosols that were emitted as a result of the biomass burning in mainland Southeast

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Asia may significantly raise the local PM2.5 concentrations and caused severe air pollution. Meanwhile, from

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January to April, Fig. 4b shows that the PM2.5 distribution had high spatial consistency with the fire spots and

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it was only relatively high in northern Cambodia in January and February. The PM2.5 concentration in the

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northwest part of mainland Southeast Asia rose sharply in March and it overtook the heavily polluted North

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China Plain. Referring to the interannual data, the annual average of PM2.5 concentration always exceeded 20

285

µg/m3 if the number of fire spots was above 100,000 and it also correlates (r = 0.75, P < 0.01) with the annual

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number of fire spots (Table S-3).

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Fig. 4. The spatial distribution of AOD (a), PM2.5 concentration (b) and CO total column (c) from January (1) to April (4).

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3.2.3 CO

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The mean of CO total column over mainland Southeast Asia in March (3.25×1018 molecules/cm2) was much

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higher than that in other months, which is the same as those of AOD and PM2.5. Meanwhile, the CO total

291

column exceeded 2.5×1018 molecules/cm2 in 5 straight months from December to April and the total column

292

in other months were always below 2.5×1018 molecules/cm2. The highest monthly mean of CO total column

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among the 17 years was in March 2014 with the value of 3.42×1018 molecules/cm2 (Fig. S-5b). Fig. 3c shows

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that there was a significant correlation between the monthly number of fire spots and the CO total column in

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the fire season and the correlation coefficient reached 0.82 (P < 0.001), which is also much higher than that in

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the non-fire season. With respect to the spatial distribution, the CO total column is consistent with the fire

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spots from January to April (Fig. 4c). Especially in March, the CO total column in the southwest part of

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mainland Southeast Asia exceeded 4.0×1018 molecules/cm2 and intense biomass burning simultaneously

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occurred in this region. In contrast to the AOD and PM2.5, we found no correlation (r = 0.01, P > 0.05)

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between the annual total of fire spots and the annual mean of CO total column on an interannual level (Table

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S-3).

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3.3 The contribution of various factors to local air pollution

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3.3.1 Source apportionment of PM2.5 and trajectory analysis

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Composition profiles for the 3 factors resolved by PMF are shown in Fig. 5 (left panel). Factor 1 is

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characterized by high level of OC (77%) and BC (42%); Factor 2 is characterized by high level of dust (70%)

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and SS (55%); Factor 3 is characterized by high level of SO4 (66%) and BC (40%). On a global scale,

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approximately 69% of primary OC and 23% of secondary OC are contributed by biomass burning (Hallquist

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et al., 2009). The main source of SO4 aerosol is via SO2 emissions from fossil fuel burning (about 72%), with

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a small contribution from biomass burning (about 2%) (IPCC, 2007). While, BC is emitted from coal, diesel

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and jet fuel, natural gas, kerosene, biofuel, and biomass burning (Jacobson, 2001). Water-soluble potassium

311

(K+) and levoglucosan have been widely used as tracers to apportion biomass burning contributions to

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ambient aerosols (Ramadan et al., 2000; Ma et al., 2003; Giannoni et al., 2012; Puxbaum et al., 2007). Since

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there are no highly specific tracer for biomass burning aerosols from MERRAero data, we roughly referred

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Factor 1 as dominated by biomass burning, Factor 2 as dominated by long-range transport/local nature source

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and Factor 3 as dominated by anthropogenic emission. To justify the accuracy of PMF analysis, the HYSPLIT

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model, anthropogenic emission inventory, fire burning spots and meteorological conditions have been

317

incorporated to illustrate the contribution of the three factors. From 2001 to 2016, the largest contribution

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(48%) was from Factor 1 (dominated by biomass burning), followed by 27% from Factor 3 (dominated by

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anthropogenic emission), and 25% from Factor 2 (dominated by long-range transport /local nature source).

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Fig. 5. Composition profiles for the three factors resolved by PMF (left panel): columns represent average concentrations

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and square dots represent average fraction (in percent) of those species; the monthly relative contribution (average = 1) of

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each factor (d); monthly PM2.5 composition (e) and 100% stack figure by three factors (f).

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On a monthly-scale, both Fig. 5d, 5e and 5f indicate that from January to April Factor 1 (dominated by

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biomass burning) contributed much more than other factors and in March the contribution reached the highest

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(82%), which is consistent to the results from MODIS fire spots. Meanwhile, we chose two sites with severe

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biomass burning in March and April to conduct the HYSPLIT trajectory analysis, one is located on the

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western Myanmar and the other one is located on the junction region between Myanmar and Thailand. The

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results indicate that the trajectory frequency of these two sites (Fig. S-9) are consistent with the spatial

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distribution of high PM2.5 concentration and CO total column (Fig. 4b-3 and 4c-3), which furtherly prove the

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biomass burning made a great contribution to the air pollution in this region in March and April. Refer to Fig.

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S-9 in supplementary material for the details of HYSPLIT trajectory analysis. From May to August, Factor 2

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(dominated by long-range transport /local nature source) became the dominant factor to affect local PM2.5

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concentration that is assumed to be closely related to the shift of southwest monsoon. To justify the

334

assumption, the National Centers for Environmental Prediction (NCEP) and the National Center for

335

Atmospheric Research (NCAR) Reanalysis data were used to calculate the average wind direction and wind

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speed from 2001 to 2016. Fig. S-8 shows that from May the southwest monsoon started to influence mainland

337

Southeast Asia and wind speed in this region surged to 2.45 m/s in June and 2.62 m/s in July which was the

338

highest among the twelve months. Meanwhile, the results of PMF indicate that the contribution from Factor 2

339

exceeded 80% in June and July. From September, as the southwest monsoon weakened, the wind speed in the

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study region decreased dramatically to only 1.27 m/s in September and 1.14 m/s in October. Although there

341

were no intense biomass burning during this period, the light wind weather provided condition to accumulate

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pollutants from anthropogenic emission. Therefore, in the following months Factor 3 (dominated by

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anthropogenic emission) made more contribution to PM2.5. Refer to Fig. S-8 in the supplementary materials

344

for the monthly wind field map.

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As previous section presented that in the non-fire season there were no significant correlations between fire

346

spots and AOD, PM2.5 and CO (Fig. 3). Integrating the result of PMF with meteorology data, we concluded

347

that long-range transport/local nature source or anthropogenic emission is the dominant factor to affect local

348

PM2.5 concentration in this season.

349

3.3.2 Anthropogenic emission of PM2.5 and CO from EDGAR

350

Fig. 6. The interannual anthropogenic PM2.5 and CO emissions from 2000 to 2012 (a); the monthly anthropogenic PM2.5(b)

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and CO emissions of mainland Southeast Asia and the North China Plain in 2010.

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With the rapid economic development and the increased fossil fuel combustion of the past two decades in

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mainland Southeast Asia, the PM2.5 and CO emissions (except 2005) from anthropogenic activities presented

354

steady and continuous growth and the total emissions increased by 16% and 36% from 2000 to 2012,

355

respectively (Fig.6). If we only consider the anthropogenic emissions, the air quality should become worse in

356

mainland Southeast Asia. Table S-3 reveals that the interannual variations of the AOD, PM2.5 and CO

357

fluctuated from 2001 to 2012, and, unlike the anthropogenic emissions, they did not present any increasing

358

trend.

359

With respect to the monthly emissions, they are stable in 2010 and showed no obvious seasonal pattern over

360

the study area (Fig. 6b and 6c). As mentioned before, the average AOD, PM2.5 concentration and CO total

361

column in March and April were the highest. Meanwhile, the anthropogenic PM2.5 and CO emissions in these

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two months showed no abnormally high value. We chose two sites with high anthropogenic PM2.5 and CO

363

emission (Hanoi city and Ho Chi Minh city) to conduct HYSPLIT trajectory analysis in March and April. The

364

results show that the trajectory frequency from these two cities are inconsistent with the spatial distribution of

365

high PM2.5 concentration and CO total column in March and April (Fig. S-11) and anthropogenic emission

366

from these two cities has less effect on the variation of air quality in the northern part of the study region. To

367

further elaborate on the monthly variation of PM2.5 and CO emissions and the relationship between

368

anthropogenic activities and the ambient air quality, the heavily polluted North China Plain was chosen for

369

comparison. Unlike the study region, the anthropogenic PM2.5 and CO emissions in the North China Plain

370

have a strong seasonal pattern and the emissions in the winter are much higher than other seasons (Fig.6b and

371

6c). Simultaneously, the ambient air quality is the worst and this region is suffering from devastating haze

372

pollution episodes in the winter. The anthropogenic emissions are the dominant cause and the main source of

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air pollutant emissions in the North China Plain. Regardless of whether the analysis is on the interannual scale

374

or the monthly scale, the anthropogenic CO and PM2.5 emissions in Thailand, North and South Vietnam are

375

always much higher than those of other regions (Fig. S-10), which is inconsistent with the spatial distributions

376

of the AOD, PM2.5 concentration and CO total column during the fire season. The average number of fire

377

spots in October from 2001 to 2016 was only 439, which was the lowest average month of the year. Therefore,

378

this month was chosen to further analyze the anthropogenic emission and the air quality in mainland

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Southeast Asia. The results show that the monthly average AOD and PM2.5 of October in Thailand and

380

southern and northern Vietnam were higher than those of other regions and they had evident spatial

381

consistency with the anthropogenic emissions (Fig. S-12a, 12b and Fig. S-10). Meanwhile, the AOD, PM2.5

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concentration and CO total column all were extremely high in the Sumatra and Borneo islands because

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intense peat fires frequently occurred in September and October in this region and the air pollutants emitted

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from the fires even affected the ambient air quality in mainland Malaysia. Refer to Fig. S-12 in the

385

supplementary materials for the specific spatial distributions of the AOD, PM2.5 concentration and CO total

386

column in October. This section further validates the results from PMF that biomass burning played a larger

387

role than anthropogenic activities in affecting the variation of ambient air quality on mainland Southeast Asia.

388

4. Discussions and conclusions

389

By taking advantage of remote sensing technology, we analyzed the tempo-spatial distributions and variations

390

of different types of biomass burning and their effects on local air quality from 2001 to 2016. The results

391

indicate that biomass burning follows a strong seasonal pattern and it is closely related to precipitation and

392

farming activities. Forest and shrubland fires are the dominant types of biomass burning and the four kinds of

393

biomass burning presented different temporal and spatial distributions. The local ambient air quality is closely

394

related to biomass burning, and the AOD (0.62), PM2.5 concentration (45.42 µg/m3) and CO total column

395

(3.25×1018 molecules/cm2) all reached their peaks in March, similar to the number of fire spots. Meanwhile,

396

we found significant correlations between the monthly AOD (r = 0.74, P < 0.001), PM2.5 concentration (r =

397

0.88, P < 0.001), and CO total column (r = 0.82, P < 0.001) and the number of fire spots in the burning season,

398

while, no correlations were found in the non-burning season. The results from PMF analysis indicated that

399

Factor 1 (dominated by biomass burning) contributed 48% to local PM2.5 concentration and Factor 3

400

(dominated by anthropogenic emission) only contributed 27%. On a monthly-scale, PMF analysis also

401

revealed that Factor 2 (dominated by long-range transport/nature local source) or Factor 3 (dominated by

402

anthropogenic activities) was the dominant factor to affect PM2.5 concentration during the non-fire season.

403

Meanwhile, the results from EDGAR inventory further indicated that despite the consecutive increase of

404

anthropogenic PM2.5 and CO emissions, biomass burning is still dominant cause of the variation of the local

405

ambient air quality.

406

We proposed a method of integrating MERRAero with PMF to calculate the contribution of three factors to

407

PM2.5 concentration and various remote sensing datasets have been integrated to conduct the temporo-spatial

408

analysis, but there exist several uncertainties. One is the lack of highly specific tracer, we cannot precisely

409

quantify the contribution of biomass burning only through PMF analysis. To compensate the shortcomings

410

and reduce the uncertainty of this method, the HYSPLIT analysis, anthropogenic emission inventory, fire

411

spots and meteorology data have been taken into consideration to justify the result of PMF analysis. If more

412

aerosol species can be incorporated into MERRAero and the spatial resolution can be improved in the future,

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the accuracy of PMF analysis can be substantially improved and the factors can be further detailed. The

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second uncertainty is the inconsistent coverage of the remote sensing data in the dry and wet season. The

415

coverage of monthly remote sensing data keeps an extremely high level in the dry season, e.g. monthly AOD

416

is above 99% and CO total column is above 95%. However, the coverage of remote sensing data declines in

417

the wet season, monthly AOD coverage can still reach 95% and CO total column coverage is above 75%.

418

Since the analysis is on a monthly-scale, the uncertainties of the data coverage between wet and dry season

419

have no significant impact on the result. But for other studies on a daily-scale, the inconsistent coverage of the

420

remote sensing data in the different seasons will dramatically increase the uncertainties. With more and more

421

ground-measure data are becoming available, we would like to compare them with remote sensing data in our

422

future study. Finally, combining the advantages of these two kinds of datasets and incorporating the biomass

423

burning emission inventory, the comprehensive analysis will provide more useful information for

424

understanding the effect of biomass burning on local air pollution in mainland Southeast Asia or other

425

regions.

426

Although government authorities tried to outlaw swidden farming, the number of fire spots did not decrease in

427

mainland Southeast Asia. The annual number of fires spots exceeded 100,000 each year from 2012 to 2016.

428

Unlike mainland Southeast Asia, the severe biomass burning in Indonesia always draws worldwide attention,

429

which pushes the local authorities to take effective measures to reduce biomass burning in the following years.

430

For example, after the devastating forest fires in 2015, the number of Indonesia’s fire spots decreased to very

431

low levels in 2016 and 2017. In fact, when only using the number of fire spots, sometimes the biomass

432

burning in mainland Southeast Asia is even more dramatic. Based on the results of this study, we assume that

433

to effectively improve the air quality in mainland Southeast Asia, it is essential for the authorities to take

434

some effective measures to alter local farming activities and reduce biomass burning.

435

Acknowledgements

436

We are grateful to NASA, NOAA and EDGAR for the use of their data. Without their hardworking, we cannot

437

obtain sufficient data to support this study. This study is supported by Science and Technology Research

438

Program of Chongqing Municipal Education Commission (Grant No. KJQN201800803).

439

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745 746 747 748 Fig. 6. The interannual anthropogenic PM2.5 and CO emissions from 2000 to 2012 (a); the monthly anthropogenic PM2.5(b)

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ACCEPTED MANUSCRIPT Various datasets and models have been integrated for a long-time span study. The forest and shrubland fires are the principal form of local biomass burning. The variation of air quality is closely related to local biomass burning. Biomass burning contributed more than human activities to local PM2.5.

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The annual number of fire spot consecutively exceeded 100,000 from 2012 to 2016.

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Declaration of Interest Statement We would like to submit the manuscript entitled “Influence of biomass burning on local air pollution in mainland Southeast Asia from 2001 to 2016”, which we wish to be considered for publication in “Environmental

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Pollution”. No conflict of interest exits in the submission of this manuscript, and manuscript is approved by all authors for publication. I would like to declare on behalf of my co-authors that the work described was original research that has not been published previously, and not under consideration for publication elsewhere, in whole or in

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part. All the authors listed have approved the manuscript that is enclosed.

Author name: Shuai Yin Date: 20/03/2019

Affiliation: National Institute for Environmental Studies, Tsukuba 3058506, Japan Email: [email protected]; Tel.: +81-29-850-2981