Development of Spatial and Temporal Emission Inventoryfor Crop Residue Field Burning

Thongchai Kanabkaew & Nguyen Thi Kim Oanh

Received: 26 January 2010 /Accepted: 18 October 2010# Springer Science+Business Media B.V. 2010

Abstract Accurate emission inventory (EI) is the foremostrequirement for air quality management. Specifically, airquality modeling requires EI with adequate spatial andtemporal distributions. The development of such EI is alwayschallenging, especially for sporadic emission sources such asbiomass open burning. The country of Thailand produces alarge amount of various crops annually, of which rough(unmilled) rice alone accounted for over 30 million tonnes in2007. The crop residues are normally burned in the field thatgenerates large emissions of air pollutants and climate forcers.We present here an attempt at a multipollutant EI for cropresidue field burning in Thailand. Available country-specificand regional primary data were thoroughly scrutinized toselect the most realistic values for the best, low and highemission estimates. In the base year of 2007, the best emissionestimates in Gigagrams were as follows: particulate matter asPM2.5, 128; particulate matter as PM10, 143; sulfur dioxide(SO2), 4; carbon dioxide (CO2), 21,400; carbon monoxide(CO), 1,453; oxides of nitrogen (NOx), 42; ammonia (NH3),59; methane (CH4), 132; non-methane volatile organiccompounds (NMVOC), 108; elemental carbon (EC), 10;and organic carbon (OC), 54. Rice straw burning was by far thelargest contributor to the total emissions, especially during thedry season and in the central part of the country. Only a limitednumber of EIs for crop residue open burning were reported forThailand but with significant discrepancies. Our best estimateswere comparable but generally higher than other studies.Analysis for emission uncertainty, taking into account possiblevariations in activity data and emission factors, showsconsiderable gaps between low and high estimates. Thedifference between the low and high EI estimates for particulate

matter and for particulate EC and OC varied between 80%and +80% while those for CO2 and CO varied between 60%and +230%. Further, the crop production data of Thailandwere used as a proxy to disaggregate the emissions to obtainspatial (76 provinces) and temporal (monthly) distribution.The provincial emissions were also disaggregated on a 0.10.1 grid net and to hourly profiles that can be directly usedfor dispersion modeling.

Keywords Crop residue . Open burning . Air pollution .

Gridded emission . Hourly emission . Thailand

1 Introduction

Field burning of crop residues after harvest is a commonpractice in many countries. In developing countries, whichnormally have an agriculture-based economy, a largequantity of crop residues is produced annually and burneddirectly in the field. A number of studies indicate that thisactivity can significantly affect local air quality and humanhealth [1]. It can also be a leading cause of such regionalscale phenomena as the atmospheric brown clouds [2, 3] thatcan affect the regional and global climate. However, thissignificant source of air pollution is still mostly overlookedin air quality management programs in many countries.

Proper quantification of the air pollution generated by cropresidue burning would stimulate formulation of an appropriatenational air quality policy and international cooperation toeffectively control these emissions. Nevertheless, there is stillno comprehensive emission inventory (EI) of this source dueto challenges related to uncertainties of emission factors andburning activity data. The existing EIs for Asia either have apreliminary estimate of this open burning emission on aregional scale [4] or do not report this source [5].

Thailand is an agriculture-based country with the maincrops being rice, cassavas, and sugarcane. Most farmers burn

T. Kanabkaew :N. T. Kim Oanh (*)Environmental Engineering and Management,Asian Institute of Technology,Pathumthani, Thailande-mail: kimoanh@ait.ac.th

Environ Model AssessDOI 10.1007/s10666-010-9244-0

the crop residues directly in the field to ease preparation forthe next crops. This activity has been reported to adverselyaffect air quality in terms of PM10 (particles with the size lessthan 10 m) and CO (carbon monoxide) in the surroundingareas during the burning periods [6, 7]. Source apportion-ment studies for the Bangkok Metropolitan Region (BMR)have identified a significant contribution (3040%) ofbiomass burning to PM2.5 (particles with the size less than2.5 m) air pollution [8, 9]. The dense haze covering theChiang Mai province and the northern region of the countryduring March and April is believed to have causal links toopen burning of crop residues and forest fires [10].

No biomass burning emissions were considered in thecurrent official EI of Thailand, which was prepared by thePollution Control Department (PCD) for the base year of 1997[11]. Also, this PCD EI covers only the BMR which includesBangkok, Nonthaburi, Pathumthani, Nakhonpathom, Samuth-prakarn, and Samuthsakorn. An updated EI for 2004 withtemporal profiles was reported by Pham et al. [12], but onlyfor point sources (power plants and industrial facilities). Therehave been a few research attempts to update the EI forThailand [13, 14], mostly with the emission factors (EFs)taken from international sources outside Asia such as from theUnited States Environmental Protection Agency (US EPAAP-42) [15], Jenkins et al. [16], and Andreae and Merlet [17].

This study aims to contribute to the current emissiondatabase of the country by focusing on the EI for cropresidue field burning for the entire country of Thailand in2007 with necessary spatial and temporal profiles suitablefor air quality modeling purposes. The considered airpollutants included particulate matter (PM10, PM2.5),particulate elemental carbon (EC) and organic carbon(OC), and gaseous emissions, i.e., CO, carbon dioxide(CO2), oxides of nitrogen (NOx), non-methane volatileorganic compounds (NMVOC), methane (CH4), sulfurdioxide (SO2) and ammonia (NH3). EFs from the countryand regional specific data in Asia (China, India, andIndonesia) were also compiled and used for the emissionestimates.

2 Methodology

2.1 Annual Emission Estimation

Emission rates from the crop residue burning werecalculated using Eq. 1.

EAx;i X

i

Mi EFx;i 1

where,

x=Pollutant speciesi=Crop types

EAx,i=Annual emission rate (grams)Mi=Amount of burned crop residues in a year(kilogram dry mass of residue)EFx,i=Emission factors of species x and crop type i(grams per kilogram dry mass of residue).

The amount of field-burned crop residues was estimatedbased on the total annual crop production data using Eq. 2.

Mi Pi Ni Di Bi hi 2where,

P=Crop production (kg)N=Crop specific residue-to-production ratioD=Dry matter-to-crop residue ratioB=Fraction of dry matter residues that are burned inthe field=Crop specific burn efficiency ratio (fraction oxidizedduring combustion).

We considered 12 main crops of the country, theresidues of which are subject to open burning (Table 1).The activity data were from 2007, the base year of thisinventory. A wide variety of data taken from literature andsurveys were compiled and values considered relevant forThailand were selected to use in Equations 1 and 2 toproduce the EI. The selection of a particular value to beused for the EI was made based on the following priority:(1) specific data for Thailand such as from the Departmentof Alternative Energy Development and Efficiency(DEDE) [18, 19], Office of Agriculture Economics(OAE) [20], and Energy for Environment Foundation(EFE) [21], (2) primary data generated from similar Asiancountries, and (3) other data sources providing relevantinformation.

A compilation of literature available data for cropresidue field burning and the selected values used in thisstudy is given in Table 1. The data are presented for eightgroups of crop residues: rice, maize and sorghum, soybean,potato, jute and cotton, groundnut and mung bean,sugarcane, and cassava.

The production (P) data of various crops in Thailandin 2007 were taken from OAE [20] for each of the 76provinces of the country. Note that the fraction of cropresidues burned in the field (B) is not readily availableand it is a source of uncertainty [4]. A wide range ofvalues has been used in reported EIs. For example, theIntergovernmental Panel on Climate Change [22] sug-gests using a value of 0.25 for developing countries and

0.17 for the remaining countries in the region in theirAsian EI.

In this study, we selected the B values for various cropsbased mainly on reported data by relevant governmentalagencies in Thailand [1821]. Tipayarom [6] conducted asurvey for the Pathumthani province of the central part ofThailand and shows that farmers burn rice straw (RS) in thefield whenever weather permits and around 90% of RS arefield-burned in this part of the country. Therefore, to

produce the best estimate for rice, a value of 0.9 [6] wasapplied for the central region of the country while in otherregions a B value of 0.48 suggested by DEDE [18] wasused.

There are two rice crops commonly grown in Thai-land: the major crop (Rice I) is harvested mainly duringNovember and December and the second crop (Rice II)is harvested during March and April. In the central partof the country, i.e., the Chaopraya delta, a third crop of

Table 1 Compiled and selected values for estimating amount of crop residue burning (M)

Parameter Crop type

Rice Maize andsorghum

Soybean Potato Jute andcotton

Groundnut andmung bean

Sugarcane Root/tubers(cassava)

Available data

Residue to crop ratio (N) 1.19a 0.19a 1.5b 0.5b 3.0b 1.5b 0.24a 0.12a

1.76c 2.0c 0.21c 2.1f 2.1f 0.3c 0.2c

1.2e 1.85e 2.1f 0.5e

1.4f 1.6f

Dry matter-to-crop residueratio (D)

0.85c 0.4c 0.71c 0.45g 0.8d 0.8d 0.71c 0.71c

Fraction burned in field (B)m 0.48h 0.61j 0.76k 1.0 1.0k 1.0 0.55j 0.41j

0.90i

0.30j

Burn efficiency ratio () 0.89c 0.92c 0.68c 0.9g 0.9g 0.9g 0.68c 0.68c

0.85e 0.35e

Selected value for best estimate in this study

Residue to crop ratio (N) 1.19 0.19 1.5 0.5 3.0 1.5 0.24 0.12

Dry matter-to-crop residueratio (D)

0.85 0.4 0.71 0.45 0.8 0.8 0.71 0.71

Fraction burned in field (B) 0.90;0.48n 0.61 0.76 1.00 1.00 1.00 0.55 0.41

Burn efficiency ratio () 0.89 0.92 0.68 0.9 0.9 0.9 0.68 0.68

Actual crop product in 2007(P; 1,000 tonnes)l

30,110 3,717 204 126 5 176 64,365 26,915

In this table, 12 main crops were grouped into eight groups regarding their similarity and available dataaDEDE [19], biomass potential for energy production in ThailandbYang et al. [24], primary data for crop residue burning in ChinacStreet et al. [4], data were likely to be from various area sourcesdGAPF EI Manual [25], primary data for IndiaePenner et al. [26], data selected for developing countries estimated from various literaturesfJingura and Matengaifa [27], primary data for ZimbabwegIPCC [22], default value from IPCC EI manualhDEDE [18], non-exploited rice straw in ThailandiTipayarom and Kim Oanh [7], survey data of rice straw burning in central ThailandjEFE [21], non-exploited crop residues in Thailand in 2007kSajjakulnukit et al. [28], surplus availability of crop residues in ThailandlOAE [20], annual crop product in year 2007: specific data for ThailandmStreet et al. [4] noted that B is the component with a large uncertainty and needs details specific data of open burning practicesnFactor of 0.90 was applied for central Thailand while 0.48 was for the rest area of the country

Emission Inventory for Crop Residue Field Burning

rice is also produced which is harvested during the wetseason when field burning of RS is not generallypossible. This crop accounts for around 10% of thetotal rice production.

Data on crop residue used in Thailand power generationsector were analyzed to explore if other significant RSutilization takes place in the country. Overall, only three outof 77 biomass power plants use field crop residues (mainlyRS) for power generation while the rest mainly use bagasseand rice husks which, in principle, are not subject to fieldburning [19]. Thus, only a small portion of RS may be usedfor this purpose.

Emission factor is expressed in grams of releasedpollutant per kilogram of burned dry matter of cropresidues. Country-specific EFs are available only for RS[29]. A few publications [17, 30, 31] present EF for generalcrop residues which is a best guess without givingspecific information for each crop type.

The compiled relevant values and the selected values ofEF for best estimates in this study are presented in Table 2.The selection of EFs was made based on the followingcriteria/priority:

1) EFs for specific types of crops to be used whenavailable; otherwise EF for general crop residues(combined crops) would be used.

2) Primary EF data measured in Thailand to be the firstchoice, followed by values generated for other similarAsian countries (China, India, and Indonesia) due totheir general similarity in climate and cultivationmethods. In cases where there are no data availablefor the Asian region, relevant data from other parts ofthe world would be used. For example, for RS we usedthe average EF reported in Kim Oanh et al. [29] for thebest estimates. For low and high estimates, EFs wereselected from the lower and higher values of the EFranges reported in literature.

3) EFs derived for open burning were considered morerelevant than those measured for cookstoves.

4) For crop residues other than RS, if more than one EFvalue were obtained after step 3, the highest value inTable 2 was selected. This indicates that our EIwould provide a more conservative estimate in thisregard.

5) EFs for PM (PM2.5, PM10) and composition (OC andEC) must be satisfied the condition of PM2.5PM10and EC+OC

of a particular pollutant in a province was done using thefollowing equation:

EPi;m EA;i Fi;m 3where

EPi,m=Annual emission of burning of crop type i inprovince m (m=1, 76) in grams (g) or other consistentmass unit

EA=Annual emission rate of the whole country ingrams (g) or other consistent mass unitFi,m=Ratio of crop type i production in province m(Pi,m) and the total production in the whole country(TPi,country) as seen in Eq. 4.

Fi;m Pi;mTPi;country 4

Table 2 Compiled emission factors and selected values for crop residue burning

Pollutants Crop residue type (g/kg dry mass of residue)

Rice Maize and sorghum Jute and cotton Sugarcane Combined crops

Available data

PM2.5 3.2a; 8.3b 11.7c; 4.1d 3.8d 3.9e

PM10 3.46a; 9.1b 6.21a; 4.3d 4.0d

PM 6.28f; 5.3g 4.4d; 5.31f; 12g; 1.68h 4.53f 4.1d; 7.0g 13e; 8.05h

SO2 0.62a; 0.18f 0.2a; 0.44c; 0.04f; 0.015h 0.40e; 0.216h

CO2 1,162a; 1,177b; 1,674f; 791i; 1,216j 1,314a; 1,350c; 2,327f; 1,160h; 1,262i 1,345f 1,515e; 1,130h

CO 31.39a; 93b; 67.98f; 64.2i; 179.9j 38.78a; 53c; 36.4d; 67.64f; 40.3h; 114.7i 105.82f 34.7d 92e; 86.3h

NOx 2.84a; 2.28f; 1.81i 0.75a; 4.3c; 1.7d; 3.60f; 1.27h; 1.28i 2.49f 2.6d 0.70h

NH3 4.10j 0.68c; 0.7d 1.0d 1.30e

CH4 9.59j 4.4c; 1.5d; 1.60h 0.4d 2.7e; 4.56h

NMVOC 10c; 4.4d 2.2d 7.0e

EC 0.51b; 0.49f; 0.86g; 0.52k 0.35c; 0.95f; 0.96g; 0.78k 0.82f,k 0.78g 0.69e; 0.47l

OC 2.99b; 2.01f; 1.96k 3.9c; 2.25f; 2.21k 1.83f,k 3.3e; 0.7l

Selected values for best estimate in this study

PM2.5 8.3 4.1 3.9 3.8 3.9

PM10m 9.1 4.3 4.53 4.0 8.05

SO2 0.18 0.44 0.216 0.216 0.216

CO2 1,177 2,327 1,345 1,130 1,130

CO 93 114.7 105.82 34.7 86.3

NOx 2.28 4.3 2.49 2.6 0.70

NH3 4.10 0.68 1.30 1.0 1.30

CH4 9.59 4.4 4.56 0.4 4.56

NMVOC 7.0 10 7.0 2.2 7.0

EC 0.51 0.95 0.82 0.78 0.47

OC 2.99 2.25 1.83 3.3 0.7

EFs of combined crop residues were applied to those unavailable data, i.e., soybean, potato, groundnut, mung bean, and cassavaaJenkins et al. [16], EFs from tunnel simulations in U.S.bKim Oanh et al. [29], EFs for field burning rice straw in ThailandcLi et al. [32], EFs for the burning of wheat straw and corn stover in ChinadDennis et al. [33], EFs for crop residue burning in Texas, U.S.eAndreae and Merlet [17], Best guess based on literature information availablefCao et al. [34], EFs for field crop residue burning in ChinagPenner et al. [26], data selected for developing countries estimated from various literatureshZhang et al. [30], EFs are applicable for maize and averaged for crop residues of household stoves in ChinaiZhang et al. [23], primary data for the burning of rice, wheat and corn straws in ChinajChristian et al. [35], EFs from laboratory measurements of biomass burning in IndonesiakCao et al. [36], EFs from experiment testing for EC and OC in ChinalReddy and Venkataraman [31], EFs for EC and OC from crop residue burning in India and the estimation was based on PM=4.9 g/kgmDue to the lack of data, EFs of PM10 for combined crop residues were selected based on available EFs of PM

Emission Inventory for Crop Residue Field Burning

2.2.2 Temporal Allocation Profiles

Similar to spatial segregation, Eqs. 3 and 4 were also usedto segregate the annual emissions into monthly emissions;however, in this case EA represents the total annualemissions rate over the study area, e.g., a selected provinceor the entire country, and m indicates the months in a year(m=1, 12). The monthly productions summarized in Table 4were used as a proxy for the monthly amount of cropresidues burned. The monthly productions were taken fromthe data provided by OAE [20], which is the average for thewhole country, not for each province. Therefore, for rice,the major crop for which residue is subject to field burning,

the monthly production for each province was obtainedfrom the direct communication with the OAE.

3 Results and Discussion

3.1 Annual Emissions

We discuss in this section the best estimates of emissions.Assessment of EI uncertainty is presented in Section 3.4.The best estimates of annual pollutant emission for eachconsidered crop are presented in Table 5. The totalemissions from crop residue field burning of Thailand in

Table 3 Selected values for estimation of uncertainty using low and high ranges

Parameters Crop types/pollutants Low estimation Best estimation High estimation

Fraction burn in field (B) Rice straw 0.30 0.90 (central area); 0.48 (other area) 0.90

Other crops 0.17 0.61 (maize and sorghum); 0.76 (soybean);1.0 (potato; groundnut and mung bean; juteand cotton); 0.55 (sugarcane); 0.41 (cassava)

1.0

Emission factors (EF;g/kg dry mass of residue)

PM2.5 3.2 8.3 8.3

PM10 3.46 9.1 9.1

SO2 0.18 0.18 0.62

CO2 791 1,177 1,674

CO 64.2 93 179.9

NOx 1.81 2.28 2.84

NH3 4.10 4.10 4.10

CH4 9.59 9.59 9.59

NMVOC 7.0 7.0 7.0

EC 0.49 0.51 0.52

OC 1.96 2.99 2.99

Table 4 Monthly crop production in the country used for temporal profile allocation

Crop Monthly production in percent

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Total rice 3.19 2.60 4.85 6.87 3.81 2.88 2.99 3.64 3.87 6.80 36.3 22.2

Maize 6.24 1.05 0.51 0.53 0.05 0.02 10.4 24.7 22.2 18.7 15.5

Sorghum 38.2 23.1 5.16 5.66 27.8

Soybean 0.15 1.77 29.5 27.5 1.92 4.62 8.53 11.1 12.0 2.87

Potato 12.6 16.5 39.4 21.2 0.58 0.98 1.85 0.86 1.48 4.58

Jute 0.63 0.22 0.58 18.8 30.7 32.9 14.4 1.77

Cotton 5.81 0.03 2.85 16.4 42.3 32.7

Groundnut 1.70 3.61 9.47 14.4 5.95 1.49 2.55 17.6 16.5 9.51 9.47 7.82

Mung bean 8.10 2.60 10.6 7.30 2.40 3.70 4.70 1.80 7.20 24.9 26.7

Sugarcane 31.8 27.1 19.5 3.29 0.27 18.1

Cassava 21.1 12.9 11.5 4.77 3.40 2.25 1.55 1.98 4.07 6.79 11.2 18.5

Blank cell means no data (no crop product was reported during those months)

T. Kanabkaew, N.T. Kim Oanh

2007, in Gigagrams (Gg), are roughly as follows: PM2.5,128; PM10, 143; SO2, 4; CO2, 21,400; CO, 1,453; NOx, 42;NH3, 59; CH4, 132; NMVOC, 108; EC, 10; and OC, 54.

Overall, for most pollutants, RS burning contributes thelargest emission share, followed by sugarcane and cassavacrop residue burning. Other crops such as maize, sorghum,soybean, potato, jute, cotton, groundnut, and mung beanhave relatively small contributions.

The contribution of RS burning to overall emissionsfrom crop residue burning (averaged over all pollu-tants) is 80%, ranking from 63% (EC) to 95% (CH4),which is justified by the countrys large rice production.A high production of rough (unmilled) rice in 2007, over30 million tonnes, in combination with a high residue-to-crop ratio (Table 1) explains this significant emissioncontribution.

The results of our study are presented in Table 6together with other EI data available for Thailand forcomparison. Note that not all pollutants are reported inother EIs for Thailand. The Center for Global andRegional Environmental Research (CGRER) at the Uni-versity of Iowa [37] specifically reports crop residueburning while another global EI, the Emission Databasefor Global Atmospheric Research (EDGAR) [38], reportsemissions from all biomass burning. Significant differ-ences are observed among the cited works which are mostlikely due to the differences in EFs, activity data used, andbase year of the estimates. CGRER [37] generally reportshigher country emissions from all sources for mostpollutants as compared to other EIs presented in Table 6.Overall, the earlier studies indicate that biomass burningcontributes significantly to the total emissions in thecountry, e.g., the shares for CO, CO2, NMVOC, andNOx are 42%, 25%, 26%, and 13%, respectively, based onCGRER [37] data for the base year of 2006 and 66%,14%, 31% and 24%, respectively, based on the EDGAR[38] data for 2000. CGRER [37] data also showsignificant contributions from biomass burning to ECand OC emissions (42% and 64%); however, no PM2.5 andPM10 data are reported for biomass burning in theseemission databases.

Our emission estimates for all crop residue burning andfor RS burning, in particular, are mostly higher than thevalues given by CGRER [37]. Our values are also higherthan the data reported by Gadde et al. [14] which averagedover 20022006. The most significant difference is perhapsin the selection of fraction burned in the fields (B).Specifically, we used B=0.9 for RS burning in the centralarea based on a local survey [6] and values for other cropsfrom relevant information sources of Thailand [1821, 28].The B value used in CGRER [37] is 0.17 for all crops andGadde et al.s [14] is 0.48 for RS, which would certainlyresult in lower amounts of residues burned.Ta

ble5

Annualem

ission

bestestim

ates

forcrop

residuefieldburningin

Thailand,2007

Crop

Annualestim

ation(Gg)

PM

2.5

PM

10

SO2

CO2

CO

NOx

NH3

CH4

NMVOC

EC

OC

RiceI

83.6

91.7

1.81

11,900

937

23.0

41.3

96.6

70.5

5.14

30.1

RiceII

24.4

26.7

5.29E-01

3,460

273

6.70

12.1

28.2

20.6

1.50

8.79

Maize

6.40E-01

6.71E-01

6.87E-02

363

17.9

6.71E-01

1.06E-01

6.87E-01

1.56

1.48E-01

3.51E-01

Sorghum

9.63E-03

1.01E-02

1.03E-03

5.47

2.69E-01

1.01E-02

1.60E-03

1.03E-02

2.35E-02

2.23E-03

5.29E-03

Soybean

4.38E-01

9.04E-01

2.42E-02

127

9.69

7.86E-02

1.46E-01

5.12E-01

7.86E-01

5.28E-02

7.86E-02

Potato

9.93E-02

2.05E-01

5.50E-03

28.8

2.20

1.78E-02

3.31E-02

1.16E-01

1.78E-01

1.20E-02

1.78E-02

Jute

1.96E-02

2.28E-02

1.09E-03

6.77

5.33E-01

1.25E-02

6.55E-03

2.30E-02

3.53E-02

4.13E-03

9.22E-03

Cotton

2.84E-02

3.30E-02

1.57E-03

9.80

7.71E-01

1.81E-02

9.47E-03

3.32E-02

5.10E-02

5.98E-03

1.33E-02

Groundnut

2.03E-01

4.20E-01

1.13E-02

59.0

4.50

3.65E-02

6.78E-02

2.38E-01

3.65E-01

2.45E-02

3.65E-02

Mungbean

4.61E-01

9.51E-01

2.55E-02

133

10.2

8.27E-02

1.54E-01

5.39E-01

8.27E-01

5.55E-02

8.27E-02

Sugarcane

15.6

16.4

8.86E-01

4,640

142

10.7

4.10

1.64

9.02

3.20

13.5

Cassava

2.48

5.11

1.37E-01

717

54.8

4.44E-01

8.25E-01

2.89

4.44

2.98E-01

4.44E-01

Total

128

143

3.50

21,400

1,450

41.7

58.8

132

108

10.4

53.5

Emission Inventory for Crop Residue Field Burning

Our results for PM2.5, CO2, and NOx are comparable tothose from Gadde et al. [14], but CO emissions for RSburning in our study are about four times higher. This wascaused by different EFs and activity data used as mentionedabove. In this study, we applied the EFs produced by thefield RS burning experiments in Thailand [29] for most ofthe studied pollutants. For other pollutants that are notgiven in Kim Oanh et al. [29] (NOx, SO2, NH3, CH4, andNMVOC), EFs from relevant sources were used. Note thatour PM10 EI was higher than PM2.5 that meets criteria no. 5above; however, the difference between PM2.5 and PM10was small suggesting that majority of particulate mattersemitted from this field burning activity would belong tofine particle size range.

The EFs for other crops (besides RS) used in our studieswere selected mainly from Asian regional data with theintention to realistically represent conditions in Thailand;however, due to the selection of the upper values among theEF ranks available, as mentioned above, the emissionestimates for non-RS crop residue burning should beconsidered as conservative ones. Nevertheless, this wouldnot significantly affect the overall crop residue burningemission results, as these non-RS residues contribute only20% of the total emissions.

The contribution of crop residue field burning emissions(obtained in this study) to the countrys total emission datareported by Vongmahadlek et al. [13] was assessed for eightpollutants which shows 11% for PM10, 15% for CO, 13%for NH3, 5% for NOx, 4% for NMVOC, 7% for EC, 17%for OC, and less than 1% for SO2. These values areconsiderably higher than the contribution estimated basedon data reported by CGRER [37], which are around 14%for most pollutants. The official EI data by PCD [11] for thebase year of 1997, presented in Table 6, were only for a partof the country, the BMR domain, and that no biomassburning emission was included.

3.2 Spatial Emission Distribution

Emissions were initially disaggregated according to theadministrative boundary of all 76 provinces in Thailand toobtain annual provincial emissions. The central part of thecountry has the highest crop residue field burning emis-sions, followed by the northeastern, northern, and southernplains. This coincides with the rice plantation pattern that isdominant in the central region (along the Chaopraya River).In this central part of Thailand, two or more crops areproduced in each year as discussed earlier.

Table 6 Emission estimates for Thailand from different sources

Species Annual emission estimate (Gg) for different base years

This study Gaddea Vongmahadlekb CGRERc EDGARd PCDe

All cropresidues

Ricestraw

Ricestraw

Allbiomass

Allsources

All cropresidues

Allbiomass

Allsources

Allbiomass

Allsources

Othersources

PM2.5 128 108 108 388

PM10 143 118 31 514 1,277 475 38 (TPM)

SO2 4 2 17 21 886 3 28 1,327 59 1,306 240

CO2 21,400 15,300 12,207 11,600 87,819 350,930 30,869 214,191

CO 1,450 1,210 290 4,213 9,466 704 5,227 12,416 6,474 9,839 464

NOx 42 30 26 158 790 29 189 1,467 301 1,246 329

NH3 59 53 61 439 10 69 388

CH4 132 125 10 21 293 3,567 177

NMVOC 108 91 33 283 2,583 120 932 3,570 515 1,669 36

EC 10 7 30 136 5 35 84

OC 54 39 192 326 25 253 394

aGadde et al. [14], base year 20022006 (activity data are averaged from 2002 to 2006)bVongmahadlek et al. [13], base year is 2005cCGRER [37], base year is 2006, no PM2.5 and PM10 reported for biomass burning (http://www.cgrer.uiowa.edu/EMISSION_DATA_new/index_16.html)dEDGAR [38], base year is 2000 (http://www.mnp.nl/edgar/)ePCD [11], base year is 1997, only total particulate matter (TPM) is reported (no size segregation), for BMR and no biomass burning emission.

T. Kanabkaew, N.T. Kim Oanh

Intensive field burning of crop residue in the provincesadversely affects the local air quality in the surroundingareas. In particular, the higher emissions in the surroundingprovinces of large urban areas, such as Bangkok, KhonKaen, and Chiang Mai contribute substantially to airpollution in those cities. Smoke from intensive RS burningin Pathumthani in the dry season, for example, is trans-ported toward Bangkok city following the northeastmonsoon direction [7].

For future modeling studies, we overlaid a grid net witha resolution of 0.10.1 (1212 km) on the map of theprovincial emissions of each pollutant. As an example,

Fig. 1 visualizes the spatial distribution of annual estima-tion for PM2.5.

3.3 Temporal Emission Distribution

Monthly emission fractions of the combined crop residueemissions are shown in Fig. 2. For RS, the monthlyemissions of each province were also determined. It isclearly seen that major crop residue burning emissionsoccur during the dry season (OctoberApril) and peak inNovember and December when the Rice I crop and manyother crops are being harvested. In fact, the dry season isthe most polluted season, with higher particulate matterlevels in the country which may be due to a number ofcauses such as stagnant atmosphere, lack of wet removal,and enhanced long-range transport of emissions fromupwind regions. In addition, more intensive crop residuefield burning is also considered as an important factor forelevated air pollution in the dry season [3941].

An hourly emission profile can be further proposedbased on the survey data of RS field burning in the centralpart of Thailand [6], which show that farmers normallyconduct field burning from 11001700 hours (Fig. 3).Data for other crops residue burning were not available;

Fig. 3 Hourly profile for crop residue open burning emissions

Fig. 2 Monthly profile for crop residue open burning emissions

Fig. 1 Spatial distribution of PM2.5 emission from crop residue openburning in Thailand (0.10.1 grids)

Emission Inventory for Crop Residue Field Burning

however, as RS open burning contributes about 80% ofthe emissions, it can be used to represent the overallhourly emission profiles for crop residue burning in thecountry, especially in the central part of the country(around BMR).

Hourly profile information is important for dispersionmodeling, e.g., photochemical smog modeling. Thehighest emission occurs around noon when intensiveinsolation presents would enhance ozone formation fromits precursors (NOx and NMVOC) in urban areassurrounded by agricultural fields. Ozone air pollution isnow recognized as an important air quality issue inThailand. Together with particulate air pollution, ozoneregularly exceeds national ambient air quality standardsin large urban areas [42].

3.4 Uncertainty Assessment

The result of EI uncertainty assessment is shown in Fig. 4.Average uncertainty ranged from 60% to +140%. A loweruncertainty was obtained for particulate species, includingEC and OC which ranged from around 80% to +80%. Forgaseous species, i.e., CO2 and CO, the uncertainty was high(from 60% to +230%). Both variations in EFs and activitydata (amounts of residue burned) contributed to these wideranges of the uncertainty. In particular, the EF of CO2 isexpected to have less variation as it depends mainly on thecarbon content of fuels; however, a large range of CO2 EFwas also found in literature (from 790 to 1,670 g/kg RS, asshown in Table 3) which would cause the wide range ofCO2 emission estimates.

4 Conclusions

Field burning of crop residue contributes significantly tothe countrys total air pollution. More importantly, theseemissions peak during the dry polluted season whichfurther enhances the build-up of high ambient air pollution.

RS field burning contributes the largest share (80%) ofthe total crop residue burning emissions in Thailand,followed by sugarcane and cassava. The provinces locatedin the central, northeastern, and northern parts of thecountry have the largest crop residue burning emissions,coinciding with the rice production distribution pattern. Theproduction data (provincial and monthly) are useful proxiesto calculate the spatial and temporal distribution ofemissions. The resulting gridded and monthly/hourlyemission profiles can be directly used for further dispersionmodeling studies. The uncertainty assessments show sig-nificant variations between low and high emission esti-mates, from 60% to +140% on average.

The countrys operational emission database should beupdated with realistic data on crop and other biomassburning sources. Information on large amounts of airpollution emitted from the field burning activity wouldhelp to raise awareness in the implementation of non-burning alternatives to minimize adverse effects on local airquality, the atmosphere, and the climate.

Acknowledgments The air quality research group at AIT isacknowledged for their kind support and information exchange.Government sectors in Thailand (OAE, DEDE, PCD, EFE) and otherpartners are sincerely thanked for their provision of relevant data forthe emission inventory.

128.0 143.1

3.5

214.0

145.3

41.7 58.8

131.5108.4

10.453.5

0

100

200

300

400

500

600

PM2.

5

PM10

SO2

CO2

(x100

)

CO (x

10)

NO

x

NH

3

CH4

NM

VO

C EC OC

Gg/

year

Fig. 4 Uncertainty assessmentof crop residue open burning

T. Kanabkaew, N.T. Kim Oanh

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Emission Inventory for Crop Residue Field Burning

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T. Kanabkaew, N.T. Kim Oanh

Development of Spatial and Temporal Emission Inventory for Crop Residue Field BurningAbstractIntroductionMethodologyAnnual Emission EstimationSpatial and Temporal Allocation ProfilesSpatial Allocation ProfilesTemporal Allocation Profiles

Results and DiscussionAnnual EmissionsSpatial Emission DistributionTemporal Emission DistributionUncertainty Assessment

ConclusionsReferences

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