High-Resolution Mapping of Biomass Burning Emissions in Three

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High-resolution Mapping of Biomass Burning Emissions in Three Tropical Regions Yusheng Shi, Tsuneo Matsunaga, and Yasushi Yamaguchi Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.5b01598 • Publication Date (Web): 19 Aug 2015 Downloaded from http://pubs.acs.org on August 20, 2015

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Environmental Science & Technology

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High-resolution Mapping of Biomass Burning

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Emissions in Three Tropical Regions

3 Yusheng Shi1,*, Tsuneo Matsunaga1, Yasushi Yamaguchi2

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2

National Institute for Environmental Studies, Tsukuba 305-8506, Japan

Graduate School of Environmental Studies, Nagoya University, Nagoya 464-8601, Japan

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Corresponding author:

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Tel./fax:+81 29 850 2751

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E-mail address: [email protected], [email protected] (Y. Shi)

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KEYWORDS: tropical biomass burning, high resolution, vegetation fires, human waste burning,

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fuelwood combustion.

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1 Environment ACS Paragon Plus


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ABSTRACT: Biomass burning in tropical regions plays a significant role in atmospheric

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pollution and climate change. This study quantified a comprehensive monthly biomass burning

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emissions inventory with 1 km high spatial resolution, which included the burning of vegetation,

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human waste and fuelwood for 2010 in three tropical regions. The estimations were based on the

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available burned area product MCD64A1 and statistical data. The total emissions of all gases and

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aerosols were 17382 Tg CO2, 719 Tg CO, 30 Tg CH4, 29 Tg NOx, 114 Tg NMOC (non-methane

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organic compounds), 7 Tg SO2, 10 Tg NH3, 79 Tg PM2.5 (particulate matter), 45 Tg OC (organic

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carbon) and 6 Tg BC (black carbon). Taking CO as an example, vegetation burning accounted

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for 74% (530 Tg) of the total CO emissions, followed by fuelwood combustion and human waste

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burning. Africa was the biggest emitter (440 Tg), larger than Central and South America (113

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Tg) and South and Southeast Asia (166 Tg). We also noticed that the dominant fire types in

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vegetation burning of these three regions were woody savanna/shrubland, savanna/grassland and

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forest, respectively. Although there were some slight overestimations, our results are supported

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by comparisons with previously published data.

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1. INTRODUCTION

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Biomass burning emissions (forest vegetation, grass, crop residue, municipal solid waste and

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biofuel) are well recognized as a significant source of atmospheric chemical compounds, and

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significantly affect global atmospheric chemistry and climate change.1 The released emissions

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(e.g., greenhouse gases (GHGs), reactive trace gases and aerosols) affect both the local and

49

regional air quality and pose a serious threat to human health and the environment.2 Therefore,

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accurate emission estimates are very important to predict atmospheric composition and air

51

quality.

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Over the past few decades, numerous studies on estimating biomass burning emissions from

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vegetation, municipal solid waste and fuelwood have been conducted using simple quantification

54

descriptions and generalizations. The fuelwood burning was tied directly to forestry statistics

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from the Food and Agriculture Organization (FAO),3 while the agricultural residues burning was

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estimated as a fraction of the available residues.4 Recently, the quantification of emissions from

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forest/grassland fires was usually based on burned area, fuel loads, combustion and emission

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factors with the help of inventory statistical methods5,6 or satellite products and biogeochemical

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models (e.g., Global Fire Emission Database (GFED)7 and Fire INventory from NCAR

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(FINN)8). The emissions from burning human waste were based on the waste production and

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human population of the target country.9 Moreover, fuelwood combustion emissions in

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developing countries were commonly calculated by the fuelwood use per capita annually and the

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population size.4

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Tropical regions are vulnerable to fires and have been experiencing rapid deforestation, slash-

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and-burn and peatland combustion,10 all of which are associated with fires that often cause

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extensive biomass burning.10-12 Meanwhile, tropical countries are mostly developing countries,



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where human waste and fuelwood combustion are commonly seen. However, until now, datasets

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of comprehensive biomass burning emissions from vegetation burning, human waste and

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fuelwood combustion have not been reported. The available inventories, GFED and FINN,

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include only vegetation burning emissions and exclude emissions from the use of biofuel or

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burning of municipal waste. Therefore, proper quantification of the comprehensive biomass

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burning emissions in tropical regions is needed. This will not only help predict atmospheric

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composition and support biogeochemical cycle studies (e.g., GOSAT (Greenhouse gases

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Observing SATellite) CO2, CH4 and aerosol products), but also support an overall assessment of

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the potential effects and appropriate measures to mitigate GHGs.

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In this study, we quantified a 1 km spatial resolution biomass burning emissions inventory

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including vegetation burning, and human waste and fuelwood combustion in the three tropical

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regions (Central and South America, Africa, and South and Southeast Asia, hereafter referred to

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as the Americas, Africa and Asia) across all land types. We employed the widely used available

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burned area product MCD64A1,13 satellite and observation data based on biomass density, and

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spatiotemporal variable combustion factors to estimate the vegetation burning emissions with 1

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km spatial resolution, and used human population density to allocate the spatial distribution of

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the emissions from human waste burning into a 1 km spatial grid. Most of the data used were

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derived in 2010. The GHGs, trace gases and primary particles in emissions included carbon

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dioxide (CO2), carbon monoxide (CO), methane (CH4), nitrogen oxides (NOx), non-methane

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organic compounds (NMOC), sulfur dioxide (SO2), ammonia (NH3), and particulate matter with

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a diameter below 2.5 µm (PM2.5), and aerosol contained organic carbon (OC) and black carbon

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(BC).

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2. METHODS AND DATA SOURCE


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2.1 Vegetation burning

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Biomass burning emissions from vegetation fires are generally calculated using the burned

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area, available fuel, combustion factor and emissions factor, as shown by Seiler and Crutzen14

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with the following equation:

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= ∑ × × ×

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where BA denotes the burned area per month m at location l (m2 month-1), F is the available fuel

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loads for combustion (kg m-2), CF is a combustion factor, defined as the fraction of combusted

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fuel to the total amount (-), EF represents emission factors for species, conveying the mass of

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species per mass of dry matter burned (g kg-1) and i is types of land cover. Since this study

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quantifies a 1 km spatial resolution biomass burning emissions inventory, therefore, to keep

(1)

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consistency, all datasets are resampled into 1 km grid maps before calculation.

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2.1.1

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Burned area data in 2010 were derived from MODerate resolution Imaging Spectroradiometer

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(MODIS) direct broadcast burned area product (MCD64A1) (http://modis-fire.umd.edu), which

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uses surface reflectance, daily active fire and land cover products to delineate burned areas, and

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burn cells are tagged with approximate burn date.13 Its direct broadcast burned area mapping

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algorithm can identify the date of burn for each grid cell by applying dynamic thresholds to

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composite imagery generated from a burn-sensitive vegetation index. The general approach is to

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produce composite imagery summarizing persistent changes in the vegetation index time series,

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and then use spatial and temporal active-fire information to guide the statistical characterization

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of burn-related and non-burn-related change within the scene. This information is used to

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estimate probabilistic thresholds suitable for classifying the scene into burned and unburned

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pixels.13 The algorithm makes heavy use of active fire observations, and is therefore somewhat

Burned area



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more tolerant of cloud and aerosol contamination in tropical regions. The primary MCD64A1

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product has 500 m spatial resolution and monthly temporal resolution, and the day of burning

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and a temporal uncertainty range of the burn date are recorded for each pixel.13 After extracting

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the approximate Julian day of burning and their images from the MCD64A1 product, we

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resampled the monthly burned areas from the original 500 m binary map into a 1 km percentage

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grid map5 to keep consistent with other input datasets with 1 km spatial resolution (Supporting

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Information (SI) Figure S1).

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2.1.2

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Early studies aiming to estimate fuel loads used biome-averaged values from statistical data or

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observational data with country-based or land type-based aboveground biomass density.6,15

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However, they usually cannot reflect the spatial variations and heterogeneities over large areas.

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More recently, numerical biogeochemical models have been used as they are expected to better

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simulate spatial variability in fuel loads.7,16 Here, we introduced the aboveground biomass

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(AGB) from the study by Saatchi et al.,17 which employed data from multiple satellites as well as

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4079 ground plots to estimate the carbon stored in AGB and to map the spatial variations of

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biomass across three tropical regions at 1 km spatial resolution (SI Figure S2). Validation of the

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spatial distribution of modeled AGB by a mixture of ground and Lidar observation estimated

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AGB showed good agreement, with relative errors of 27.3%, 31.8% and 33.4% in Americas,

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Africa and Asia, respectively17 (SI Figure S2). In general, AGB distribution was highly

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heterogeneous, ranging from 500 g m-2 to 30,000 g m-2 in the three regions. The biomass density

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or fuel loads estimations for shrubland and grassland, crop residue in fields, and peatland were

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described in SI main text using the input data in SI Table S1 and SI Figure S3.

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2.1.3

Fuel loads

Combustion factor


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The combustion factor (CF) is primarily determined by fuel types and moisture conditions.18

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According to Ito and Penner,19 the fuel types were classified into three kinds (forest, woodland

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and grassland) based on the tree cover fraction. To represent the fuel characteristics for each fire

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type, we calculated the CF for each kind of fuel (forest, woodland and grassland) based on the

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percentage of tree cover (Tc) (MODIS Vegetation Continuous Fields MOD44B)20 following Ito

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and Penner.16 Here, forest, woodland and grassland were defined as those regions with greater

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than 60% Tc, 40–60% Tc, and less than or equal to 40% Tc.16 These categories are consistent

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with definitions in the work of Hansen et al.21 The calculations of CF for each fuel type are

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described specifically in SI main text and the spatial and temporal variable CFs averaged in 2010

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covering all fuel types is presented in SI Figure S4.

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2.1.4

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The emission factor (EF) is a signal of accompanying trace gases and aerosols during burning.

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Here, the compiled EFs data measured in each biomass type were collected. The type of

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vegetation burned in each fire pixel is determined by the MODIS Land Cover Type product

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(MCD12Q1) for 2010.22 The IGBP (International Geosphere-Biosphere Programme) land cover

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classification (SI Table S2) is used to assign each fire pixel to one of 16 land use/land cover

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(LULC) classes (Figure 1). For each LULC type, detailed EFs for various gaseous and

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particulate species have been taken from available datasets and are summarized in SI Table S2.

Emission factor

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[Figure 1]

2.2 Human waste burning

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Many atmospheric pollutants are produced from the open burning of human waste at both the

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residential level and dump sites.9 Here, we estimated the national emissions from the open

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burning of human waste (open residential and dump waste burning) according to the



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Intergovernmental Panel on Climate Change (IPCC) guidelines23 for the emissions of GHGs

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inventories (Chapters 2 and 5, http://www.ipcc-nggip.iges.or.jp/public/2006gl/vol5.html,

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accessed 21 November 2014). Annual emissions of compound i for each country (Ei) is

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expressed as the product of the amounts of burned waste and the corresponding emission factors: = ×

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(2)

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where WB is the amount of burned waste and EFi is the emission factor for waste burning,

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defined as the amount of species i emitted per unit of waste combusted.

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2.2.1

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The total amount of burned waste (WB) comprises the burned domestic waste at individual

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residences (WBres) and the burned waste at dumps (WBdump). The waste burned in incinerators or

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modern combustion systems is not considered in this study. Since all countries in this study are

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developing countries, the WB is subsequently calculated based on the general guidelines from

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section 5.3.2 in 2006 IPCC Guidelines for National GHGs Inventories:23

Burned waste

= × × ×

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(3)

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where P is the total population, Pfrac is the fraction of the population that burns waste, MSWP is

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the annual per capita waste generation and Bfrac is the fraction of the burned waste relative to the

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total amount of waste treated. In general, Pfrac is roughly estimated as the fraction of the total

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population whose waste is not collected plus the fraction of the population whose waste is

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collected and disposed of in open dumps where waste is burned. And all rural populations are

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assumed not to have waste collection, while for urban populations, waste that is not collected is

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assumed to be burnable. The estimation of WBres and WBdump are described in SI main text using

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the data compiled by Wiedinmyer et al.9 in SI Table S3.

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2.2.2

Emission factors


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Compiled emission factors of burned waste from the literature are applied to calculate emission

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estimates of GHGs, reactive trace gases and particulate matter. SI Table S4 lists the emission

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factors, references and associated uncertainty (when available) for the emission factors used in

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this study.

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2.2.3

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The aim of this study is to produce an emission inventory with high spatial resolution. The

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burned wastes and emissions depend on human populations. Therefore, the estimates of national

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amounts of burned human waste were spatially allocated to a 1 km grid according to the

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populations in the corresponding grid pixels of each country as described by the globally gridded

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maps of population for 2010 (Gridded Population of the World, v3 (GPW3)) (SI Figure S5).24

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The amount of burned human waste in the i pixel (Wi) was calculated using the following

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equation:5

Spatial allocation

= ×

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(4)

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where Pi is the population in the i pixel, Pk is the total population in country k and Wk is the total

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burned human waste in country k. Finally, 1 km high spatial resolution emissions of all trace

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gases and aerosols were produced using the emission factors.

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2.3 Fuelwood burning

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The estimation of burned fuelwood was based on the population and per capita fuelwood use

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in all countries. We estimated the fuelwood burning assuming that household fuel consumption

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correlates with population size, and computed total fuelwood use for a given country by

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multiplying the per capita usage with the population for that country. The emissions Ei are

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calculated as:

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= ×


(5)


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where WF is the amount of burned fuelwood and EFi is the emission factor for fuelwood burning.

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The amount of burned fuelwood WF in each country can be expressed as:

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= ×

(6)

where F is the fuelwood use per capita (kg cap-1 day-1), and P is the population.

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Large continents are marked by contrast in geoclimate and vegetation conditions, from

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drylands through the large desert and savanna zones with fuel deficits, to the forest zones with

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fuelwood surplus. The per capita fuelwood consumption depends on availability and demand,

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and ranges from an estimated low of 0.05 kg cap-1 day-1 in Lesotho of Africa to upwards of 4.43

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kg cap-1 day-1 in Bhutan of Asia.4 Fuelwood use per capita in each country across the three

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regions was collected from a published study4 and is presented in SI Table S5. For those

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countries excluded in SI Table S5, we used the mean values averaged in the same continent

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according to Yevich and Logan,4 which grouped together neighboring countries with similar

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woody vegetation and population density. Besides, agricultural residues as household fuel were

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also quantified by using the ratio of burned crop residues at home to that in open fields.4 The

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ratio of crop residues as household fuels and burned in open fields vary from country to country,

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and strongly affected by the availability of crop residues. We estimated the burned crop residues

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as household fuels by using the burned residues in fields, provided by FAOSTAT, and the ratio

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of agricultural residue use in biofuel to burn in open fields for each country (SI Table S6). We

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also used the average ratio in the same continent if the country is excluded in SI Table S6. The

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emission factors for fuelwood and crop residue burning were compiled from the literature and

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are described in SI Table S4 and Table S2. The global gridded population map GPW3 (SI Figure

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S5) was employed to describe the spatial allocations and distributions of fuelwood burning

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emissions according to the density of the population in grids of each country.


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3. RESULTS AND DISCUSSION

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3.1 Spatial distribution of burned biomass

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The total amounts of burned vegetation, human waste and fuelwood were calculated using the

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three methods described above. Vegetation burning distributed extensively with larger amounts

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in Africa compared with those in Americas and Asia (Figure 2). The burned biomass in these

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regions was mostly greater than 1 Gg km-2. More specifically, the spatial distribution of

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vegetation burning showed high amounts were concentrated in Central (except Democratic

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Republic Congo), West and East Africa (SI Figure S6a), central part of South America (Brazil,

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Venezuela, Bolivia and Paraguay), India, Myanmar, Cambodia and Laos. We also found that the

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spatial patterns of the burned biomass showed good consistency with the burned areas in the

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three regions. Compared with the LULC map (Figure 1), it can be concluded that open fires

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usually prevail in woody savanna, shrubland and grassland with medium biomass density where

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the fire is relatively easy to burn. Tropical forests with relatively large biomass density and high

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moisture content usually combust less completely. The extent of the burned biomass

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corresponded well with the spatial patterns of the high combustion factors, where savanna,

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shrubland and grassland dominated. While in forest, the relatively high moisture content and tree

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cover fraction resulted in the low efficiency of combustion.

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[Figure 2]

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Human waste combustion presented patterns different from vegetation burning. The spatial

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distribution and the amounts of burned human waste are far below the vegetation burning (SI

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Figure S6b). The highest amounts were around 0.5 Gg km-2. In Americas, large amounts can be

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seen in Mexico, Guatemala and Southeast Region of Brazil. While in Africa, Nigeria and South

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Africa burned the highest amounts of the human waste. The Asian countries are highly



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populated; therefore, the annual produced human waste naturally exceed that of the other

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regions, which was mainly distributed in Pakistan, North and South India, Bangladesh, Sri

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Lanka, Thailand, Vietnam and Java of Indonesia, respectively.

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Fuelwood combustion displayed spatial patterns similar to human waste burning, but with

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larger extent and magnitude (SI Figure S6c). Totally, the burned fuelwood in Asia was larger

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than that in Americas and Africa, and India was the largest contributor to the total burned

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fuelwood across the three regions with the highest amounts up to 2 Gg km-2. In Americas,

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extensive burning of fuelwood can be found in Central Mexico, Guatemala, Columbia,

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Venezuela and Southeast Region of Brazil. Developing countries rely on combustible fuelwood

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as an energy source for heating and cooking, resulting in vast consumption of fuelwood in these

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regions. The LULC map showed that these areas corresponded with the shrubland, woody

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savanna and cropland, which are readily combustible. While in Asia, most countries (India, Sri

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Lanka, Bangladesh, Bhutan, Nepal, Myanmar, Thailand, Laos, Vietnam, Philippines and Java of

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Indonesia) recorded high amounts of combustion, with the largest contributor in India due to its

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dense population.

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3.2 Spatial distribution of total emissions

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The biomass burning emissions for all trace gases and aerosols (CO2, CO, CH4, NOx, NMOC,

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SO2, NH3, PM2.5, OC and BC) in three tropical regions in 2010 were quantified with 1 km high

269

spatial resolution (Table 1). Firstly, we found that the emissions of all species presented similar

270

spatial distributions even though their magnitudes differed dramatically. Taking CO emissions

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(Figure 3) as an example, in general, CO emissions presented obvious spatial variations across

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the three regions and showed consistent patterns with the total amounts of the burned vegetation,

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human waste and fuelwood (Figure 2). Total CO emissions of vegetation burning, human waste


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and fuelwood combustion in the three tropical regions in 2010 amounted to 719 Tg. Africa was

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the largest emitter, with 440 Tg CO, much higher than that in Americas (113 Tg CO) and Asia

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(166 Tg CO). This was not only for CO emissions but also for other emission species (SI Table

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S7). Extensive emissions can be found in Central (except Democratic Republic Congo), West

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and East Africa, where most CO emissions were greater than 100 Mg CO km-2. The CO

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emissions in Americas and Asia were far below the extent and magnitude of that in Africa. In

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Americas, large CO emissions were seen in Central Brazil and Guatemala, with moderate

281

amounts in Central Mexico. While in Asia, CO emissions were distributed extensively in North

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and South India, Sri Lanka, Bangladesh, Thailand and Java of Indonesia. Compared with other

283

inventories with varying spatial resolution (ranging from 0.5°, 0.25° to 1 km) (Figure 4), the

284

biomass burning emissions in this study with 1 km high spatial resolution were more effective at

285

capturing the effects of small-sized fires in certain plots. For example, the coarse grid data of

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GFED3,7 which had a relatively low spatial resolution, were frequently misinterpreted for small-

287

sized fires because of their large smoothed pixels.25

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[Figure 3]

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[Table 1]

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[Figure 4]

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3.3 Spatial pattern of the three emission sources

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For vegetation burning, CO emissions could be observed extensively throughout most of

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Africa and part of the Americas and Asia (SI Figure S7a), with most emissions greater than 100

294

Mg CO km-2. In Americas, CO emissions were high in Brazil, Venezuela, Bolivia and Paraguay

295

and low in Mexico, Peru, Chile and Argentina. Moderate differences were found in Africa,

296

where relatively large amounts of emissions from fires originated from Central (except



297

Democratic Republic Congo), West and East Africa. Compared with these regions, the CO

298

emissions in Asia accounted for a minor proportion. Since the burned area detected in Asia was

299

far lower than that in Americas and Africa, the biomass burning emissions were also lower.

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After categorizing the LULC classes into four major land types according to the fire sources,

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we quantified the contributions of CO emissions from each land type to the total amounts (SI

302

Figure S8). We concluded that woody savanna/shrubland burning (272 Tg CO, 51%) was the

303

biggest contributor to the total CO emissions throughout the three regions, followed by

304

savanna/grassland (188 Tg CO, 36%), forest (50 Tg CO, 9%) and cropland (20 Tg CO, 4%),

305

respectively. Woody savanna/shrubland mostly concentrated in the Northern and Southern part

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of Central Africa, and the Southern part of East Africa, with small amounts in Americas and

307

Asia. For savanna/grassland fires, CO emissions were mainly distributed in West Africa, North

308

Africa, Southern part of East Africa and Southern part of Central Africa. The Central-West

309

Region of Brazil and parts of Bolivia in South America also exhibited high CO emissions. Forest

310

fires, mainly in tropical forests, only accounted for a small part among the total, and only can be

311

seen in the Central-West Region of Brazil and parts of Bolivia of South America, parts of

312

Central Africa, and Myanmar and Laos of Asia. Cropland residue CO emissions were negligible

313

and only released in Central-West Region of Brazil, Southern part of West Africa, Northern part

314

of East Africa and North India. Peatland burning in Indonesia emitted few CO emissions due to

315

the lower burned area in 2010 compared with other years.

316

Human waste burning emitted small amounts of CO, which exhibited spatial patterns similar

317

to areas where human waste is burned (SI Figure S7b). Most of the CO emissions were below 6

318

Mg CO km-2 in the three regions. Some exceptions with high emissions were noticed in Mexico,


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Guatemala, Southeast Region of Brazil, Nigeria, Pakistan, North India, Bangladesh, Sri Lanka,

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Northern and Southern Vietnam and Java of Indonesia.

321

Fuelwood burning contributed significantly to the total CO emissions, but depended on the

322

area (SI Figure S7c). The strong spatial variations showed that India was the largest emitter over

323

the entire study area, with highest emissions greater than 100 Mg CO km-2. In Americas,

324

relatively high emissions were sporadically scattered in Mexico, Guatemala and Southeast

325

Region of Brazil. Africa also exhibited low emissions across the region, with moderate in

326

Southern part of West Africa and Western part of East Africa. The CO emissions in Asia covered

327

a broad area, with the highest amounts in North India, Bangladesh, Myanmar, Thailand,

328

Vietnam, Philippines and Java of Indonesia, where dense population requires massive amounts

329

of fuelwood for energy.

330

Overall, among the three sources of CO emissions, vegetation burning was demonstrated to be

331

the largest contributor, which accounted for 74% (530 Tg), compared to the fuelwood

332

combustion with 23% (170 Tg) and human waste burning with 3% (19 Tg) of the total CO

333

emissions (719 Tg) in the three regions (Figure 5; Table 1). However, for different regions, the

334

contribution of the three sources to the total CO emissions varied dramatically. In Americas,

335

vegetation burning was the dominant emission type, with 67% of its total emissions (113 Tg

336

CO), followed by fuelwood combustion and human waste burning. In Africa, the contribution of

337

the three sources to the CO emissions was similar to that in Americas; however, the proportion

338

of the vegetation accounted for 89% of the 440 Tg CO emissions. This phenomenon changed in

339

Asia, where the dominant contributor was fuelwood combustion, with 58% of the 166 Tg CO

340

emissions. The emissions of other trace gases and aerosols from the three contributors presented

341

patterns similar to CO emissions in each region (SI Table S7). We also noticed that the major



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vegetation fire types varied in different regions. The dominant fire types in vegetation burning of

343

the three regions were from savanna/grassland, woody savanna/shrubland and forest in

344

Americas, Africa and Asia, respectively.

345

[Figure 5]

346

Besides, aerosol emissions from biomass burning usually impose great impacts on regional air

347

quality and change the balance of solar radiation, though sometimes the magnitude is smaller

348

than other emissions. Taking BC for example, BC emissions presented similar spatial patterns

349

and variations with CO emissions (SI Figure S9). And Africa was the largest contributor, with

350

2.6 Tg BC emissions, which was much higher than those in Americas (1.5 Tg BC) and Asia (2.0

351

Tg BC). Among the emission sources, vegetation burning contributed dominantly to the total BC

352

emissions in the three tropical regions (SI Figure S10). Waste burning accounted for a small

353

proportion with emissions mainly in Asian countries. And fuelwood burning distributed

354

extensively across the three tropical regions with extensive BC emissions in India. While for

355

each region, the contribution of three sources to the total BC emissions varied (SI Figure S11).

356

Vegetation burning in Americas and Africa, and fuelwood combustion in Asia are the largest

357

contributor in the three regions with 78%, 80% and 59% of its total BC emissions, respectively.

358

In addition, we found the dominant fire types in vegetation burning of the three regions were

359

from savanna/grassland, woody savanna/shrubland and forest in Americas, Africa and Asia,

360

respectively. It corresponded well with the major fire types in three tropical regions on CO

361

emissions.

362

3.4 Comparisons to other studies

363

Here, the widely available data source of EDGARv4.2 (Emissions Database for Global

364

Atmospheric Research),26 dataset of all emissions of trace gases and aerosols from biomass


Page 17 of 41


365

burning including vegetation, human solid waste and residential use, were compared with this

366

study (Figure 6). Overall, our estimations of total biomass burning emissions in three regions are

367

comparable with EDGARv4.2, although there were some overestimations. EDGARv4.2

368

inventory is based on GFED2 method in estimating emissions from the burning of savanna,

369

forest, peat and grass. However, an inverse model and posteriori emission results indicate a large

370

underestimation of biomass burning in the GFED2 inventory especially during the burning

371

season (August-October in South America, July-October in Southern Africa, March-April in

372

Northeastern India and August-October in Indonesia and Malaysia).27 In addition, this study used

373

direct broadcast burned area product (MCD64A1). Since its algorithm is more tolerant of cloud

374

and aerosol contamination in tropical regions, therefore, it can capture more burned areas,

375

resulting in overestimation of biomass burning emissions compared with EDGARv4.2.7,18,27

376

Another widely used vegetation burning emissions dataset, GFED4, was also employed to make

377

a comparison, which showed that our estimations of vegetation burning emissions in the three

378

regions in 2010 were higher than GFED4 for all species (Figure 7a). For burned area, GFED4

379

monthly burned area data set was derived exclusively from the 500 m MCD64A1 burned area

380

product aggregated to 0.25° spatial resolution on a monthly basis, therefore, resulting in the same

381

burned areas of 283.8 × 104 km2 in 2010. The EFs are very similar between GFED4 and this

382

study. Therefore, the difference in emissions is due largely to fuel loads and combustion factors.

383

Among the broad fire types, the largest difference was found in Woody Savanna/Shrubland and

384

Savanna/Grassland burning and this study presented higher estimation than GFED4. Therefore,

385

the difference between GFED4 and this study is attributable to the different estimation in actual

386

burned biomass for each burned area (fuel loads multiply combustion factors) in Woody

387

Savanna/Shrubland and Savanna/Grassland burnings. We subsequently collected the emissions



Page 18 of 41

388

data on vegetation burning with 5 km spatial resolution for 2010 in Southeast Asia from a

389

published study.5 This comparison showed that our estimations of CO2, CO, CH4, NOx, SO2,

390

NH3, OC and BC with 1 km grid were in good agreement with the data on most emission species

391

(Figure 7b). While for human waste burning, the method and data used were mainly based on

392

Wiedinmyer et al.,9 which noted that the estimated amount of global waste compared reasonably

393

well with the waste atlas. The global CO2 emitted from burning of waste is equivalent to 5% of

394

the 2010 global annual anthropogenic emissions.

395

[Figure 6]

396

[Figure 7]

397

The emission estimates described herein contain relatively high uncertainties (SI Table S8).

398

The employed MCD64A1 burned area product has been shown to be reliable in some big fires,28

399

but it usually misses small agricultural burning. Since the underlying MCD64A1 maps have a

400

minimum detectable burn size of ~40 ha in cropland, which still exceeds the size of many

401

agricultural waste burns, therefore underestimates the extent of small agricultural burns.28,29

402

According to Saatchi et al.,17 the fuel load was within an uncertainty range of approximately

403

50% around the mean value. Available EF uncertainties of trace gases and aerosols from each

404

land type are shown within parenthesis in SI Table S2. The estimation of emissions from human

405

waste burning and fuelwood combustion are dependent on the per capita waste generation rate,

406

fuelwood use per capita and the population of each country. Each of these values has associated

407

large uncertainties, and differences in the reported values can lead to differences in emission

408

estimates. SI Table S4 lists the EF uncertainties in both kinds of burning, noted within

409

parentheses. Actually, only a few measurements have been made of emission factors and only for

410

a few species and regions; those available factors are highly variable and are dependent on the


Page 19 of 41


411

composition of the waste burned as well as the burning conditions. Therefore, this emission

412

inventory should be further improved incorporating more relevant EFs and more accurate

413

activity data, and regularly updated to reflect the variabilities in the emission sources. In

414

particular, measurements of EF for local sources are needed, especially for human waste burning

415

emissions. Wiedinmyer et al.9 indicated the typical uncertainty of the inputs was on the order of

416

20–50% (including national population, national urban population fraction, national waste

417

generation rate and national waste collection efficiency) (SI Table S8). Further, the highly

418

variable Bfrac, dependent on the waste composition and burning condition, was assigned with the

419

uncertainty ranging from 0.25 to 0.8. We then quantified the minimum and maximum of

420

emissions species by considering the uncertainties, and the low and high estimates are presented

421

in SI Figure S12, which quantifies the range of emissions, not the standard deviation. The

422

average uncertainty of different species ranged from -78% to 203% for vegetation burning, -

423

276% to 238% for human waste burning and -87% to 132% for fuelwood combustion,

424

respectively.

425

Despite the large uncertainties, the comprehensive biomass burning emissions inventory

426

presented here suggest that the emissions of trace gases and aerosols from burning of vegetation,

427

human waste and fuelwood are substantial. The high-resolution biomass burning emissions as a

428

priori flux dataset will help to improve the optimization of surface atmospheric flux in inversing

429

model and will contribute to regional top-down atmospheric flux estimates using data from

430

satellite, such as the current GOSAT and Orbiting Carbon Observatory-2 (OCO-2), and the

431

future satellite, such as GOSAT-2. Besides, it can also provide guidelines to mitigate GHGs and

432

measures to help REDD+ (Reducing Emissions from Deforestation and Forest Degradation in

433

Developing Countries) for tropical developing countries across the three regions.



Page 20 of 41

434 435

ASSOCIATED CONTENT

436

Supporting Information

437

This material is available free of charge via the Internet at http://pubs.acs.org.

438 439

AUTHOR INFORMATION

440

Corresponding Author

441

Tel./fax:+81 29 850 2751; e-mail address: [email protected] or [email protected].

442

Notes

443

The authors declare no competing financial interest.

444 445

ACKNOWLEDGEMENTS

446

This work was supported by NIES GOSAT-2 Project.

447 448 449 450

REFERENCES (1) Andreae, M. O.; Merlet, P. Emission of trace gases and aerosols from biomass burning. Global Biogeochem. Cycles 2001, 15 (4), 955-966.

451

(2) Marlier, M. E.; DeFries, R. S.; Voulgarakis, A.; Kinney, P. L.; Randerson, J. T.; Shindell, D.

452

T.; Chen, Y.; Faluvegi, G. El Niño and health risks from landscape fire emissions in Southeast

453

Asia. Nat. Clim. Change 2013, 3, 131-136.

454

(3) Streets, D. G.; Yarber, K. F.; Woo, J. H.; Carmichael, G. R. Biomass burning in Asia:

455

Annual and seasonal estimates and atmospheric emissions. Global Biogeochem. Cycles 2003, 17

456

(4), 1099.


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(4) Yevich, R.; Logan, J. A. An assessment of biofuel use and burning of agricultural waste in the developing world. Global Biogeochem. Cycles 2003, 17 (4), 1095. (5) Shi, Y.; Yamaguchi, Y. A high-resolution and multi-year emissions inventory for biomass burning in Southeast Asia during 2001-2010. Atmos. Environ. 2014, 98, 8-16. (6) Chang, D.; Song, Y. Estimates of biomass burning emissions in tropical Asia based on satellite-derived data. Atmos. Chem. Phys. 2010, 10, 2335-2351.

463

(7) van der Werf, G. R.; Randerson, J. T.; Giglio, L.; Collatz, G. J.; Mu, M.; Kasibhatla, P. S.;

464

Morton, D. C.; DeFries, R. S.; Jin, Y.; van Leeuwen, T. T. Global fire emissions and the

465

contribution of deforestation, savanna, forest, agricultural, and peat fires (1997-2009). Atmos.

466

Chem. Phys. 2010, 10, 11707-11735.

467

(8)

Wiedinmyer, C.;

Akagi, S. K.;

Yokelson, R. J.;

Emmons, L. K.;

Al-Saadi, J. A.;

468

Orlando, J. J.; Soja, A. J. The Fire INventory from NCAR (FINN): a high resolution global

469

model to estimate the emissions from open burning. Geosci. Model Dev. 2011, 4, 625-641.

470

(9) Wiedinmyer, C.; Yokelson, R. J.; Gullett, B. K. Global emissions of trace gases, particulate

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matter, and hazardous air pollutants from open burning of domestic waste. Environ. Sci. Technol.

472

2014, 48 (16), 9523-9530.

473

(10) Shi, Y.; Sasai, T.; Yamaguchi, Y. Spatio-temporal evaluation of carbon emissions from

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biomass burning in Southeast Asia during the period 2001-2010. Ecol. Model. 2014, 272, 98-

475

115.

476

(11) Bistinas, I.; Oom, D.; Sa, A. C. L.; Harrison, S. P.; Prentice, I. C.; Pereira, J. M. C.

477

Relationships between human population density and burned area at continental and global

478

scales. PLoS One 2013, 8 (12), e81188.



479 480 481 482 483 484 485 486 487 488

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(12) Andela, N.; van der Werf, G. R. Recent trends in African fires driven by cropland expansion and El Niño to La Niña transition. Nat. Clim. Change 2014, 4, 791-795. (13) Giglio, L.; Loboda, T.; Roy, D. P.; Quayle, B.; Justice, C. O. An active-fire based burned area mapping algorithm for the MODIS sensor. Remote Sens. Environ. 2009, 113, 408-420. (14) Seiler, W.; Crutzen, P. J. Estimates of gross and net fluxes of carbon between the biosphere and the atmosphere from biomass burning. Clim. Change 1980, 2, 207-247. (15) Langmann, B.; Duncan, B.; Textor, C.; Trentmann, J.; van der Werf, G. R. Vegetation fire emissions and their impact on air pollution and climate. Atmos. Environ. 2009, 43, 107-116. (16) Ito, A.; Penner, J. E. Global estimates of biomass burning emissions based on satellite imagery for the year 2000. J. Geophys. Res.: Atmos. 2004, 109, D14S05.

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(17) Saatchi, S. S.; Harris, N. L.; Brown, S.; Lefsky, M.; Mitchard, E. T. A.; Salas, W.; Zutta,

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B. R.; Buermann, W.; Lewis, S. L.; Hagen, S.; Petrova, S.; White, L.; Silman, M.; Morel, A.

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Benchmark map of forest carbon stocks in tropical regions across three continents. Proc. Natl.

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Acad. Sci. U. S. A. 2011, 108, 9899-9904.

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(18)

van der Werf, G. R.;

Randerson, J. T.;

Giglio, L.;

Collatz, G. J.;

Kasibhatla, P. S.;

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Arellano Jr., A. F. Interannual variability in global biomass burning emissions from 1997 to

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2004. Atmos. Chem. Phys. 2006, 6, 3423-3441.

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(19) Ito, A.; Penner, J. E. Estimates of CO emissions from open biomass burning in southern Africa for the year 2000. J. Geophys. Res.: Atmos. 2005, 110 (D19), D19306.

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(20) DiMiceli, C. M.; Carroll, M. L.; Sohlberg, R. A.; Huang, C.; Hansen, M. C.; Townshend, J.

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R. G. Annual Global Automated MODIS Vegetation Continuous Fields (MOD44B) at 250 m

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Spatial Resolution for Data Years Beginning Day 65, 2000 - 2010, Collection 5 Percent Tree

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Cover. University of Maryland, College Park, MD, USA, 2011.


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(21) Hansen, M. C.; DeFries, R. S.; Townshend, J. R. G.; Sohlberg, R. Global land cover

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classification at 1 km spatial resolution using a classification tree approach. Int. J. Remote Sens.

504

2000, 21, 1331-1364.

505

(22) Friedl, M. A.; Sulla-Menashe, D.; Tan, B.; Schneider, A.; Ramankutty, N.; Sibley, A.;

506

Huang, X. MODIS Collection 5 global land cover: Algorithm refinements and characterization

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of new datasets. Remote Sens. Environ. 2010, 114, 168-182.

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(23) IPCC, 2006. IPCC Guidelines for National Greenhouse Gas Inventories; National Greenhouse Gas Inventories Programme Japan; IPCC: Geneva, Switzerland, 2006.

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(24) Balk, D. L.; Deichmann, U.; Yetman, G.; Pozzi, F.; Hay, S. I.; Nelson, A. Determining

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global population distribution: Methods, applications and data. Adv. Parasitol. 2006, 62, 119-

512

156.

513

(25) Randerson, J. T.; Chen, Y.; van der Werf, G. R.; Rogers, B. M.; Morton, D. C. Global

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burned area and biomass burning emissions from small fires. J. Geophys. Res.: Biogeosci. 2012,

515

117, G04012.

516

(26) Emission Database for Global Atmospheric Research (EDGAR), release version 4.2;

517

European Commission, Joint Research Centre (JRC)/Netherlands Environmental Assessment

518

Agency (PBL): The Hague, Netherlands, 2011.

519

(27) Kopacz, M.; Jacob, D. J.; Fisher, J. A.; Logan, J. A.; Zhang, L.; Megretskaia, I. A.;

520

Yantosca, R. M.;

Singh, K.;

Henze, D. K.;

Burrows, J. P.;

521

McMillan, W. W.; Gille, J. C.; Edwards, D. P.; Eldering, A.; Thouret, V.; Nedelec, P. Global

522

estimates of CO sources with high resolution by adjoint inversion of multiple satellite datasets

523

(MOPITT, AIRS, SCIAMACHY, TES). Atmos. Chem. Phys. 2010, 10, 855-876.


Buchwitz, M.;

Khlystova, I.;


524

(28) Giglio, L.; Randerson, J. T.; van der Werf, G. R. Analysis of daily, monthly, and annual

525

burned area using the fourth-generation global fire emissions database (GFED4). J. Geophys.

526

Res.: Biogeosci. 2013, 118, 317-328.

527 528

(29) McCarty, J.; Justice, C. O.; Korontzi, S. Agricultural burning in the southeastern United States detected by MODIS. Remote Sens. Environ. 2007, 108, 151-162.


Page 24 of 41

Page 25 of 41


Captions: Figure 1. Land cover types in three tropical regions. Figure 2. Spatial distribution of the total amounts of burned vegetation, human waste and fuelwood in 2010 (1 km grid). Figure 3. Spatial distribution of the total CO emissions from burning of vegetation, human waste and fuelwood in 2010 (1 km grid). Figure 4. CO emissions from vegetation burning in mainland Southeast Asia in 2010 with different spatial resolutions derived from (a) GFED3 (0.5° × 0.5°), (b) GFED4 (0.25° × 0.25°) and (c) this study (1 km × 1 km). Figure 5. Contributions of vegetation, human waste and fuelwood burnings to the total CO emissions and the four major fire types to the CO emissions of vegetation burning in the three tropical regions. Figure 6. Total biomass burning emissions (including vegetation, solid waste and residential burning) in the three tropical regions between EDGARv4.2 and this study. EDGARv4.2

reported

the

emissions

of

all

species

for

2008.

http://edgar.jrc.ec.europa.eu/index.php. Figure 7. (a) Total emissions from vegetation burning in the three tropical regions in 2010 between

GFED4

and

this

study.

GFED4:

http://www.falw.vu/~gwerf/GFED/GFED4/tables/. (b) Comparison of emissions

ACS Paragon Plus Environment


from vegetation burning in Southeast Asia in 2010 between Shi and Yamaguchi5 and this study.


Page 26 of 41

Page 27 of 41


Figure 1. Land cover types in three tropical regions.



Page 28 of 41

(Gg km-2)

Figure 2. Spatial distribution of the total amounts of burned vegetation, human waste and fuelwood in 2010 (1 km grid).


Page 29 of 41


(Mg CO km-2)

Figure 3. Spatial distribution of the total CO emissions from burning of vegetation, human waste and fuelwood in 2010 (1 km grid).



(a)

(b)

Page 30 of 41

(c)

.

150 (Mg CO km-2)

0

Figure 4. CO emissions from vegetation burning in mainland Southeast Asia in 2010 with different spatial resolutions derived from (a) GFED3 (0.5° × 0.5°), (b) GFED4 (0.25° × 0.25°) and (c) this study (1 km × 1 km).


Page 31 of 41


Americas

Africa

Asia

Figure 5. Contributions of vegetation, human waste and fuelwood burnings to the total CO emissions and the four major fire types to the CO emissions of vegetation burning in the three tropical regions.



Figure 6. Total biomass burning emissions (including vegetation, solid waste and residential burning) in the three tropical regions between EDGARv4.2 and this study. EDGARv4.2 reported the emissions of all species for 2008. http://edgar.jrc.ec.europa.eu/index.php.


Page 32 of 41

Page 33 of 41


(a)

(b)

Figure 7. (a) Total emissions from vegetation burning in the three tropical regions in 2010 between GFED4 and this study. GFED4: http://www.falw.vu/~gwerf/GFED/GFED4/tables/. (b) Comparison of emissions from vegetation burning in Southeast Asia in 2010 between Shi and Yamaguchi5 and this study.



Captions: Table 1. Emissions of Biomass Burning (Tg) from Vegetation, Human Waste and Fuelwood Combustion in the Three Tropical Regions and Their Total Amounts in 2010 Compared with Data from Other Sources.


Page 34 of 41

Page 35 of 41


Table 1. Emissions of Biomass Burning (Tg) from Vegetation, Human Waste and Fuelwood Combustion in the Three Tropical Regions and Their Total Amounts in 2010 Compared with Data from Other Sources. Emissions Vegetation Waste

Fuelwood

Total

CO2

13235

724

3423

17382

CO

530

19

170

719

CH4

17

2

11

30

NOx

24

2

3

29

NMOC

68

4

42

114

SO2

5

0

2

7

NH3

7

1

2

10

PM2.5

59

5

15

79

OC

36

3

6

45

BC

4

0

2

6



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If any prospective posting of the Submitted Work, whether voluntary or mandated by the Author(s)’ funding agency, primary employer, or, in the case of Author(s) employed in academia, university administration, would violate any of the above conditions, the Submitted Work may not be posted. In these cases, Author(s) may either sponsor the immediate public availability of the final Published Work through participation in the fee-based ACS AuthorChoice program (for information about this program see http://pubs.acs.org/page/policy/authorchoice/index.html) or, if applicable, seek a waiver from the relevant institutional policy. 7. Posting Accepted and Published Works on Websites and Repositories: A digital file of the Accepted Work and/or the Published Work may be made publicly available on websites or repositories (e.g. the Author’s personal website, preprint servers, university networks or primary employer’s institutional websites, third party institutional or subject-based repositories, and conference websites that feature presentations by the Author(s) based on the Accepted and/or the Published Work) under the following conditions: • •

•

It is mandated by the Author(s)’ funding agency, primary employer, or, in the case of Author(s) employed in academia, university administration. If the mandated public availability of the Accepted Manuscript is sooner than 12 months after online publication of the Published Work, a waiver from the relevant institutional policy should be sought. If a waiver cannot be obtained, the Author(s) may sponsor the immediate availability of the final Published Work through participation in the ACS AuthorChoice program—for information about this program see http://pubs.acs.org/page/policy/authorchoice/index.html . If the mandated public availability of the Accepted Manuscript is not sooner than 12 months after online publication of the Published Work, the Accepted Manuscript may be posted to the mandated website or repository. The



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• •


following notice should be included at the time of posting, or the posting amended as appropriate: “This document is the Accepted Manuscript version of a Published Work that appeared in final form in [JournalTitle], copyright © American Chemical Society after peer review and technical editing by the publisher. To access the final edited and published work see [insert ACS Articles on Request author-directed link to Published Work, see http://pubs.acs.org/page/policy/articlesonrequest/index.html].” The posting must be for non-commercial purposes and not violate the ACS’ “Ethical Guidelines to Publication of Chemical Research” (see http://pubs.acs.org/ethics). Regardless of any mandated public availability date of a digital file of the final Published Work, Author(s) may make this file available only via the ACS AuthorChoice Program. For more information, see http://pubs.acs.org/page/policy/authorchoice/index.html.

Author(s) may post links to the Accepted Work on the appropriate ACS journal website if the journal posts such works. Author(s) may post links to the Published Work on the appropriate ACS journal website using the ACS Articles on Request author-directed link (see http://pubs.acs.org/page/policy/articlesonrequest/index.html). Links to the Accepted or Published Work may be posted on the Author’s personal website, university networks or primary employer’s institutional websites, and conference websites that feature presentations by the Author(s). Such posting must be for non-commercial purposes.

SECTION III: Retained and Other Rights 1. Retained Rights: The Author(s) retain all proprietary rights, other than copyright, in the Submitted Work. Authors should seek expert legal advice in order to secure patent or other rights they or their employer may hold or wish to claim. 2. Moral Rights: The Author(s) right to attribution and the integrity of their work under the Berne Convention (article 6bis) is not compromised by this agreement. 3. Extension of Rights Granted to Prior Publications: The rights and obligations contained in Section II: Permitted Uses by Author(s), Section III: Retained and Other Rights, and Appendix A, Section I: Author Warranties and Obligations of this agreement are hereby extended to the Author(s)’ prior published works in ACS journals.

SECTION IV: Works-for-Hire If the Submitted Work was written by the Author(s) in the course of the Author(s)’ employment as a “Work-Made-for-Hire” as defined under U.S Copyright Law, the Submitted Work is owned by the company/employer which must sign the Journal Publishing Agreement (in addition to the Author(s) signature). In such case, the company/employer hereby assigns to ACS, during the full term of copyright, all copyright in and to the Submitted Work for the full term of copyright throughout the world as specified in Section I, paragraph 1 above. In the case of a Work-Made-for-Hire, Authors and their employer(s) have the same rights and obligations as contained in Section II: Permitted Uses by Author(s), Section III: Retained and Other Rights, and Appendix A, Section I: Author Warranties and Obligations. Any restrictions on commercial use in this agreement do not apply to internal company use of all or part of the information in the Submitted, Accepted, or Published Work. Upon payment of the ACS' reprint or permissions fees, the Author(s)’ employers may systematically distribute (but not re-sell) print copies of the Published Work externally for promotional purposes, provided that such promotions do not imply endorsement by ACS. Although printed copies so made shall not be available for individual re-sale, they may be included by the employer as part of an information package included with software or other products the employer offers for sale or license. Posting of the final Published Work by the employer on a public access website may only be undertaken by participation in the ACS AuthorChoice option, including payment of applicable fees.



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APPENDIX A: Author Warranties, Obligations, Definitions, and General Provisions SECTION I: Author Warranties and Obligations 1. ACS Ethical Guidelines: By signing this agreement, Author(s) acknowledge they have read and understand the ACS’ “Ethical Guidelines to Publication of Chemical Research” (http://pubs.acs.org/ethics). 2. Author Warranties: By signing this agreement the Corresponding Author and all co-authors (and in the case of a Work-Made-forHire, the Author(s)’ employer(s)) jointly and severally warrant and represent the following: • • • • • •

• •

•

The Submitted Work is original. The Submitted Work does not contain any statements or information that is intentionally misleading or inaccurate. All Authors have been informed of the full content of the Submitted Work at, or prior to, the time of submission. The Submitted Work has not been previously published in any form (except as permitted in Section II: Permitted Uses by Author(s)). The Submitted Work is not being considered for publication elsewhere in any form and will not be submitted for such consideration while under review by ACS. Nothing in the Submitted Work is obscene, defamatory, libelous, or otherwise unlawful, violates any right of privacy or infringes any intellectual property rights (including without limitation copyright, patent, or trademark) or any other human, personal. or other rights of any kind of any person or entity, and does not contain any material or instructions that might cause harm or injury. Any unusual hazards inherent in the chemicals, equipment, or procedures used in an investigation are clearly identified in the Submitted Work. Nothing in the Submitted Work infringes any duty of confidentiality which the Author(s) may owe to another party or violates any contract, express or implied, that the Author(s) may have entered into, and all of the institutions where the work, as reflected in the Submitted Work, was performed have authorized publication. Permission has been obtained and included with the Submitted Work for the right to use and authorize use in print and online formats, or of any format that hereafter may be developed for any portions that are owned or controlled by a third party. Payments, as appropriate, have been made for such rights, and proper credit has been given in the Submitted Work to those sources. Potential and/or relevant competing financial or other interests that might be affected by publication of the Submitted Work have been disclosed to the appropriate ACS journal editor.

The Author (and, in the case of a Work-Made-for-Hire, the Author(s)’ employer(s)) represent and warrant that the undersigned has the full power to enter into this Agreement and to make the grants contained herein. The Author(s) (and, in the case of a Work-Made-for-Hire, the Author(s)’ employer(s)) indemnify the ACS and/or its successors and assigns for any and all claims, costs, and expenses, including attorney’s fees, arising out of any breach of this warranty or other representations contained herein. 3. General Author Obligations: If the Submitted Work includes material that was published previously in a non-ACS journal, whether or not the Author(s) participated in the earlier publication, the copyright holder’s permission must be obtained to republish such material in print and online with ACS. It is the Author’s obligation to obtain any necessary permissions to use prior publication material in any of the ways described in Section II: Permitted Uses by Author(s). No such permission is required if the ACS is the copyright holder. All uses of the Submitted, Accepted, or Published Work made under any of activities described in Section II: Permitted Uses by Author(s) must include appropriate citation. Appropriate citation should include, but is not limited to, the following information (if available): author, title of article, title of journal, volume number, issue number (if relevant), page range (or first page if this is the only information available), date, and Copyright © [year] American Chemical Society. Copyright notices or the display of unique Digital Object Identifiers (DOIs), ACS or journal logos, bibliographic (e.g. authors, journal, article title, volume, issue, page numbers) or other references to ACS journal titles, Web links, and any other journal-specific “branding” or notices that are included by ACS in the Accepted or Published Work or that are provided by the ACS with instructions that such should accompany its display, should not be removed or tampered with in any way. SECTION II: Definitions Accepted Work: The version of the Submitted Work that has been accepted for publication in an ACS journal that includes, but is not limited to, changes resulting from peer review but prior to ACS' copy editing and production. ACS Articles on Request: A link emailed to Corresponding Authors upon publication of their article in an ACS journal that provides free e-prints of the Published Work. For more information, see: http://pubs.acs.org/page/policy/articlesonrequest/index.html. ACS AuthorChoice: A fee-based program that allows ACS Authors or their funding agencies to provide immediate or deferred, unrestricted online access to the Published Work from the ACS website. Under this program Authors may also post the Published ACS Journal Publishing Agreement Form A


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Work on personal websites and institutional repositories of their choosing. For more information, see http://pubs.acs.org/page/policy/authorchoice/index.html. Author: An individual who has made significant scientific contributions to the Submitted Work and who shares responsibility and accountability for the results and conclusions contained therein. For further clarification of the criteria for participation in authorship, see ACS’ “Ethical Guidelines to Publication of Chemical Research” at http://pubs.acs.org/ethics. Commercial Use: Use of the Submitted, Accepted, or Published Work for commercial purposes (except as provided for employers in the case of a Work-Made-for-Hire; see Section IV: Works-for-Hire) is prohibited or requires ACS’ prior written permission. Examples of prohibited commercial purposes or uses that require prior permission include but are not limited to: • • • • • •

Copying or downloading of the Submitted, Accepted, or Published Work, or linking to postings of the Submitted, Accepted, or Published Work, for further access, distribution, sale or licensing, for a fee; Copying, downloading, or posting by a site or service that incorporates advertising with such content; The inclusion or incorporation of the Submitted, Accepted, or Published Work in other works or services (other than as permitted in Section II: Permitted Uses by Author(s)) that are then available for sale or licensing, for a fee; Use of the Submitted, Accepted, or Published Work (other than normal quotations with appropriate citation) by a for-profit organization for promotional purposes, whether for a fee or otherwise; Systematic distribution to others via email lists or list servers (to parties other than known colleagues), whether for a fee or for free; Sale of translated versions of the Submitted, Accepted, or Published Work that have not been authorized by license or other permission from the ACS.

Corresponding Author: The Author who transmits the Submitted Work on behalf of any co-authors and who receives and engages in all subsequent editorial communications regarding the status of the Submitted Work (including its reviews and revisions), and who is responsible for the dissemination of reviewers’ comments and other manuscript information to co-authors (as appropriate). The Corresponding Author authorizes all revisions to the Submitted and Accepted Work prior to publication and is the primary point of contact after publication of the Version of Record. In some instances, more than one co-author may be designated as a Corresponding Author. Published Work: The version of the Submitted Work as accepted for publication in an ACS journal that includes but is not limited to all materials in the Submitted Work and any changes resulting from peer review, editing, and production services by ACS. Submitted Work: The version of the written manuscript or other article of intellectual property as first submitted to ACS for review and possible publication. The Submitted Work consists of the manuscript text or other contribution including but not limited to the text (including the abstract or other summary material) and all material in any medium to be published as part of the Submitted Work, including but not limited to figures, illustrations, diagrams, tables, movies, other multimedia files, and any accompanying Supporting Information. Supporting Information: Ancillary information that accompanies the Submitted Work and is intended by the Author to provide relevant background information for evaluation of the Submitted Work during the peer review process, or that is made available as a further aid to interested readers of the Published Work, but is not considered essential for comprehension of the main body of the Submitted, Accepted, or Published Work. Supporting Information may also be material that is deemed by the Editor to be too lengthy or of too specialized and limited interest for inclusion in the main body of the Accepted or Published Work. Examples of Supporting Information include but are not limited to: computer software program code, machine-readable data files or other background datasets, supporting applications and derivations, and complex tables, illustrations, diagrams, and multimedia files (e.g. video, audio, animation, 3D graphics, or high-resolution image files). SECTION III: General Provisions ACS shall have the right to use any material in the Submitted, Accepted, or Published Work, including use for marketing, promotional purposes, and on publication covers, provided that the scientific meaning and integrity of the content is not compromised. When the ACS is approached by third parties for permission to use, reprint, or republish entire articles the undersigned Author's or employer's permission may also be sought at the discretion of ACS. The American Chemical Society or its agents will store the information the Corresponding Author supplies in connection with the Submitted Work within its electronic records. Information about ACS activities, products, and services may be sent to ACS Authors by mail, telephone, email, or fax. Authors may inform ACS if they do not wish to receive news, promotions, and special offers about our products and services. No personal information will be shared with third parties. Headings contained in this Agreement are for reference purposes only and shall not be deemed to be an indication of the meaning of the clause to which they relate. ACS Journal Publishing Agreement Form A


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