Satellite monitoring for carbon monoxide and ...

Satellite monitoring for carbon monoxide and particulate matter during forest fire episodes in Northern Thailand Manlika Sukitpaneenit & Nguyen Thi Kim Oanh

Environmental Monitoring and Assessment An International Journal Devoted to Progress in the Use of Monitoring Data in Assessing Environmental Risks to Man and the Environment ISSN 0167-6369 Environ Monit Assess DOI 10.1007/s10661-013-3556-x

1 23

Author's personal copy Environ Monit Assess DOI 10.1007/s10661-013-3556-x

Satellite monitoring for carbon monoxide and particulate matter during forest fire episodes in Northern Thailand Manlika Sukitpaneenit & Nguyen Thi Kim Oanh

Received: 23 May 2013 / Accepted: 19 November 2013 # Springer Science+Business Media Dordrecht 2013

Abstract This study explored the use of satellite data to monitor carbon monoxide (CO) and particulate matter (PM) in Northern Thailand during the dry season when forest fires are known to be an important cause of air pollution. Satellite data, including Measurement of Pollution in the Troposphere (MOPITT) CO, Moderate Resolution Imaging Spectroradiometer aerosol optical depth (MODIS AOD), and MODIS fire hotspots, were analyzed with air pollution data measured at nine automatic air quality monitoring stations in the study area for February–April months of 2008–2010. The correlation analysis showed that daily CO and PM with size below 10 μm (PM10) were associated with the forest fire hotspot counts, especially in the rural areas with the maximum correlation coefficient (R) of 0.59 for CO and 0.65 for PM10. The correlations between MODIS AOD and PM10, between MOPITT CO and CO, and between MODIS AOD and MOPITT CO were also analyzed, confirming the association between these variables. Two forest fire episodes were selected, and the dispersion of pollution plumes was studied using the MOPITT CO total column and MODIS AOD data, together with the surface wind vectors. The results Electronic supplementary material The online version of this article (doi:10.1007/s10661-013-3556-x) contains supplementary material, which is available to authorized users. M. Sukitpaneenit : N. T. Kim Oanh (*) Environmental Engineering and Management, School of Environment, Resources and Development (SERD), Asian Institute of Technology, P.O. Box 4, Khlong Luang, Pathum Thani 12120, Thailand e-mail: [email protected]

showed consistency between the plume dispersion, locations of dense hotspots, ground monitoring data, and prevalent winds. The satellite data were shown to be useful in monitoring the regional transport of forest fire plumes. Keywords Satellite data . Ground monitoring . CO . PM10 . Northern Thailand . Forest fire

Introduction Smoke from forest fires has been reported to affect many locations in the world. Forest fire events, for example, are reported to affect the Mediterranean region of Europe, especially Portugal, Spain, Italy, and Greece, as well as southern France in the August to September period (Jesús et al. 2012). In USA, big fire events are reported frequently which destroyed thousands of forest acres together with people and properties (US Worst Wildfire, http:// www.infoplease.com/ipa/A0778688.html). In Southeast Asia, smoke from big forest fire events, which frequently take place during the dry season, causes severe transboundary haze pollution problems (Koe et al. 2001). Forest fires release a significant amount of air pollutants which adversely affect the human health, ecosystem, and climate. Of concern is a group of air pollutants, collectively known as the products of incomplete combustion (PIC), that includes particulate matter (PM) and a range of toxic gases, such as carbon monoxide (CO) and volatile organic compounds (VOC), as well as semivolatile organic compounds (semi-VOC). Large

Author's personal copy Environ Monit Assess

amounts of CO and PM are reported to be released from forest fires. For example, forest fire emission has been reported to account for more than 20 % of the total global CO budget (Khalil and Rasmussen 1984; Bates et al. 1995). CO, a significant trace gas in the atmosphere, participates in the photochemical reactions to form tropospheric ozone which is a strong greenhouse gas (United Nations Environment Program (UNEP) and World Meteorological Organization (WMO) 2011). With its relative long lifetime (a few months), CO can also participate in and serves as an indicator for longrange transport pollution (Lamarque 2003). Forest fires also release a large amount of fine particles that have long known to be toxic to human health (Health Effect Institute (HEI) 2002; Pope III et al. 2009). These particles absorb, scatter, and reflect solar radiation; affect cloud formation; and subsequently affect the atmospheric visibility and climate forcing (UNEP and WMO 2011). In Thailand, about 44 % of forest fire events occurred in the northern provinces (Forest Fire Control Division (FFCD) 2011). The highest frequency was observed during the dry period, lasting from February to April, which contributes to high pollution levels in the area during these months (Kim Oanh and Leelasakultum 2011). The most severe haze episodes were observed in March 2007 when daily PM with size below 10 μm (PM10) reached the highest peak of 396 μg/m3 and the maximum hourly CO reached 4 ppm. The hospital records showed a sharp increase in the number of respiratory patients in March 2007 as compared to the same month in other precedent years (Chiang Mai Provincial Public Health Office (CMPHO) 2008). Analysis of the land-use map and the satellite fire hotspots showed that about 80 % of the fire hotspots during the episode appeared over the forest area while the remaining 20 % were over the agricultural area (Kim Oanh and Leelasakultum 2011). This study focused on the satellite monitoring of PM and CO in Northern Thailand. Data provided by the Measurement of Pollution in the Troposphere (MOPITT) were used for the CO assessment while the aerosol optical depth (AOD) provided by the Moderate Resolution Imaging Spectroradiometer (MODIS) was used for PM. In general, satellite data offer a great opportunity to provide the global and regional measurement of tropospheric gases and aerosol over a relatively long period (Edwards et al. 2006; Engle-Cox et al. 2012). Specifically, in this mountainous study area, where the current ground monitoring network is still sparse, the use of such satellite data would provide

valuable information for remote sites. However, there are uncertainties in using the satellite data to characterize the ground-level air pollution. These uncertainties are influenced by a number of parameters that vary significantly across the globe and include, among others, wind directions, cloud cover, aerosol concentration and vertical distribution, surface characteristics, and the atmospheric temperature profile (McKernan et al. 2001; Engle-Cox et al. 2012). Consequently, this study first compared the satellite-based data for CO and AOD with the available ground-based monitoring concentrations of CO and PM10, respectively. In the second step, a mapping tool was used to explore the spatial distribution of CO and PM plumes from selected forest fire episodes using satellite data.

Methodology Study area The northern part of Thailand is one of the five regions of the country, covering an area of 93,690 km2 and bordering Myanmar and Laos (Fig. 1). Most of the area is covered by forest, while agricultural activities and residential area cover about 30 % of the total area, as seen from the land-use map (Fig. S1, Supplementary information, SI). The climate of Northern Thailand is influenced by southwest and northeast monsoons. The southwest monsoon normally starts in mid-May to midOctober producing the wet weather, while the northeast monsoon usually begins in mid-October to midFebruary producing cold and dry weather over the region (Kim Oanh et al. 2005). The period from midFebruary until the end of May is the transition period, and the hottest weather is observed during March– April, which is known as the local summer. During this subperiod, most forest fire events are observed in the study area (Kim Oanh and Leelasakultum 2011). Northern Thailand consists of mountain-valley topography aligning with north–south hill ridges (Tiyapairat 2012). Such topographical characteristics restrict horizontal dispersion and enhance the air pollution buildup in valleys, especially during the winter period when the study area is under the influence of a high-pressure ridge, coinciding with the northeast monsoon, and associated temperature inversions can trap emissions close to the ground and result in high levels of pollutants (Kim Oanh et al. 2005).


Fig. 1 The study area with the positions of the nine PCD monitoring stations spotted

The monthly average of CO and PM10 monitored by the Pollution Control Department (PCD) network in Northern Thailand (locations indicated in Fig. 1) is often higher during the period from January to April. Extreme forest fire events are also observed during these months (PCD 2012). Data collection and analysis Ground monitoring data Hourly CO and PM10 concentration data were collected from February to April between 2008 and 2010 from nine PCD ambient air monitoring stations in Chiang Mai, Chiang Rai, Lampang, Maehongson, and Nan provinces (locations are in Fig. 1). Each ground monitoring station was equipped with a nondispersive infrared (NDIR) gas analyzer to record hourly CO. The hourly concentrations of PM10 were measured using two methods: the tapered element oscillating microbalance (TEOM) and beta ray absorption. These two equivalent methods are specified in the Thailand Ambient Air Standards, and they have been assessed for the data comparability (PCD 2013). The QA/QC procedure applied for the PCD stations is well established (http://infofile.pcd.go.

th/air/). For this study, the PCD stations were grouped into two categories by using the population density criteria given by the Thailand’s Municipality Act. Accordingly, two stations in Chiang Mai were categorized as the urban background site and the rest were categorized as the rural background sites (in Chiang Rai, Maehongson, Lampang, and Nan). In Thailand, the ambient air quality standards for CO include 1- and 8-h averages while those for PM10 include 24-h and annual averages. The 24-h PM10 concentrations are calculated from hourly measurement data from 00:00 to 24:00 LST and are directly available in the PCD database. Additionally, for the purpose of this study, the 3-h average concentrations (CO and PM10) were also calculated from hourly measurements for the period of 09:00–12:00 LST, which corresponds to the passing time of Terra satellite (10:30 LST) over the study area. The daily (24 h) CO levels were also produced from hourly measurements and used in the comparison with the daily data of other considered parameters. To obtain general airflow, daily surface wind direction data at 10:00 LST, recorded at 10 m above the ground for the Chiang Mai International Airport, were acquired from the Air Resources Laboratory by NOAA (http://ready.arl.noaa.gov/READYamet.php). The cloud


cover data at 10:00 LST were obtained from the Thai Meteorological Department. Satellite data The MOPITT data, Version 4, Level 2, and MODIS aerosol collection 5 MOD4_L2 products stored in the specialized Hierarchical Data Format (HDF) format of the National Aeronautics and Space Administration’s (NASA’s) Earth Observing System (EOS) Mission were downloaded and extracted by the HDF Viewer. The MOPITT CO total column (molecules per square centimeter) and CO mixing ratios (parts per billion by volume) at the surface (1,013 mb) were available at 22×22km2 horizontal spatial resolutions. MOPITT on board Terra satellite takes approximately 3 to 4 days for a global coverage (Drummond 1992). The satellite crosses the study area at 10:30 LST. The MODIS AOD at 550 nm, reported at 10×10-km2 spatial resolutions, is available over the study area on a daily basis, at 10:30 LST (Terra) and 13:30 LST (Aqua). The MOPITT (http://esoweb.larc.nasa.gov/ PRODOCs/mopitt/table_mopitt.html) and MODIS (http://ladsweb.nascom.nasa.gov/data/search.html) data during the months of intensive forest fires were extracted for the study area (18.10–20.06 E, 97.82–100. 93 N). Accordingly, the data were collected for the period from February to April of 2008, 2009, and 2010 (totally for 9 months, with 266 data points of MOPITT and 599 data point for MODIS). As the MOPITT retrieval is only performed for clear-sky conditions, in this study, cloud screening was not done for the MOPITT data set. Additional cloud screening was applied for the MODIS data set, but there was not much difference seen in the correlation coefficients between AOD, with and without cloud screening, and PM10. This is perhaps due to the generally low cloudiness observed in the dry months in the study area when forest fires are intensive, as reported by Kim Oanh and Leelasakultum (2011). The available data for MODIS fire hotspots including latitude and longitude, brightness, scan, and track (1×1-km2 resolution) were gathered from the Fire Information for Resource Management System (FIRMS) for the northern provinces (Chiang Mai, Chiang Rai, Maehongson, Lampang, and Nan). The active fire locations detected by Terra and Aqua MODIS were reported two times per day. The locations of forest fire hotspots were identified by overlaying the fire hotspots and land-use map. Forest fire

hotspots data were used to determine relationships between the number of hotspots and air pollution concentrations during the selected episodes and to identify the origin/source of pollution plumes in the spatial distribution analysis. Forest fire episode selection The selection of the episodes for in-depth analysis of the forest fire plume dispersion was done using both PM10 and CO levels measured at the PCD stations. The criteria of the selection were (1) 24-h PM10 concentrations were above the Thailand’s national ambient air quality standard (120 μg/m3) and (2) 1-h CO was above the 95 percentile of the 9-month data series at each station. In addition, the numbers of hotspots were also considered in conjunction with land-use map to ensure that the forest fires were the potential cause for high CO and PM10 levels in the study area. Accordingly, the periods with high daily PM10 and 1-h CO observed at most of the stations for more than two consecutive days simultaneously with dense hotspots shown over the forest areas were selected for the plume dispersion analysis. Data analysis The data sets used for the analysis had different spatial resolutions. MOPITT CO pixel is approximately 22× 22 km2 and MODIS AOD pixel is 10×10 km2, whereas the ground-based measurements are point-based. The temporal resolution was also different; ground data were the hourly to daily average for CO and PM10 while that for satellite was a snapshot. Therefore, a comparison between the satellite data and ground-based measurement is not always straightforward. In this study, a 30× 30-km2 search radius around each PCD monitoring station (center of search area) of MOPITT CO and MODIS AOD is used. All MOPITT CO product and MODIS AOD data that fell within the search radius around a ground monitoring station were collected. This means that the point-based ground monitoring data were actually considered as the 30×30 km2 averaged, which may subsequently introduce uncertainty in the linear regression analysis. Before use, the data series for PM10, CO, and hotspots, respectively, were scrutinized for possible outliers. Generally, those data points which are beyond four times the standard deviation of the series average can be statistically considered as outliers


(Hair et al 1998). However, in our study, the high values of CO and PM10 appeared together in several episodes at the regional scale, when large numbers of hotspots were also observed. Accordingly, they would rather suggest influences of forest fires, hence were not removed from the regression analysis. Only on 1 day (18 March 2010) when regional CO and PM10 were high in all stations, but the number of hotspots was low over the northern provinces which may be related to the uncertainty of the satellite observation. Note that the cloudiness in the study area at 10:00–13:00 LST on 18 March 2010 was around 3/10, hence may not be a main reason for the uncertainty. Accordingly, the hotspot data point on that day was excluded from the statistical analysis. The relationships between satellite monitoring data and ground monitoring data for each of selected pollutants (CO and PM10) as well as between these pollutants were determined. In order to simulate the spatial distribution and transportation of the smoke plume during a selected forest fire episode, several thematic layers were considered, i.e., Northern Thailand border, MOPITT CO total column, MODIS AOD, forest fire hotspot, and wind direction data. These layers were overlaid using the kriging interpolation technique (Rajab et al. 2011).

Results and discussion Regression analysis The counts of forest fire hotspots in five provinces acquired for nine dry months of 2008–2010 show an overall increase from 2008 to 2010 in all provinces (Table S1, SI). The highest number of hotspots was found in Maehongson (over 6,000 in the period), followed by Chiang Mai (around 5,000), Nan, and then Chiang Rai, while the lowest was detected in Lampang (nearly 2,200). Note that only the hotspots detected in the forest areas of each province were considered in this study; hence, these were attributed to the forest fires (not agricultural fires). The correlations between the daily counts of forest fire hotspots (over the northern provinces) and the 24-h CO and PM10 levels measured at the PCD stations, respectively, are summarized in Table 1. In all provinces, the correlations (R) between daily PM10 and hotspots and those between daily CO and hotspots generally appeared in similar ranges, except for Chiang Mai

which had a significantly higher R value for 24-h PM10 (0.56) than for 24-h CO (0.37, Table 1). R values between the three-hourly ground-based CO (9:00–12:00, covering the passing time of MOPITT and MODIS sensor on Terra satellite over Thailand at 10:30 LST) and MOPITT CO at the surface were higher (0.36–0.71) than those with the MOPITT CO total column (0.35– 0.59), which was anticipated. This suggests that the MOPITT CO surface mixing ratios are more representative of the ground measured CO levels which in turn reflect the influence of local emission sources. Likewise, the MOPITT CO total column data may better reflect the regional emission sources and may therefore also be used to track the regional transport of air pollution. The MOPITT total CO column was correlated better with the AOD, also the columnar by nature (0.69–0.89), than with MOPITT CO surface (0.42–0.71). The correlation between 3-h PM10 and AOD was also reasonable (0.50–0.73) and was in the higher range of that reported for PM10, from 0.34 to above 0.6 in different places in the world (Dinoi et al. 2010; Song et al. 2009). Further, the good correlations between MOPITT CO and MODIS AOD suggest similar sources (forest fires) and processes (such as local and regional transport) of CO and PM10 in the study area. It should be noted that beside forest fire emissions, there were other important emission sources contributing to CO and PM10 levels in this region such as industry, transport, residential cooking, agroresidue burning, and so on. In all considered relationships in Table 1, the lowest R values were always obtained for the stations in Lampang and the second lowest were generally for Chiang Mai. In principle, the rural areas may have CO and PM10 concentrations influenced more by forest fires (represented by the number of hotspots) than the urban areas because other sources (traffic) may dominate in crowded cities like Chiang Mai. Despite the fact that Lampang was classified as a rural area, the lowest R values were obtained for this province. This may be associated with the presence of the country’s largest coal-fired power plant (Mae Moh), which may predominantly contribute stack/point emissions and related industrial emissions. The MODIS AOD for Lampang was excluded from the analysis because of the direct influence of this nonforest fire PM emissions related to this power plant (Bashkin 2003). The light extinction coefficients of the aerosols emitted from the power plant may be different from those emitted from the forest fires, which are the focus of this study. The number of

Author's personal copy Environ Monit Assess Table 1 Correlation coefficients between ground-monitored PM10 and CO with the satellite data (dry months, 2008–2010) Province

Daily CO vs. hotspota

Daily PM10 vs. hotspota

Between satellite data and three-hourly (09:00–12:00 LTS) pollution levels 3-h CO vs. MOPITT CO total

3-h CO vs. MOPITT CO surface

3-h PM10 vs. AOD

0.53 (N=187) 0.75 (N=48)

MOPITT CO total vs. AOD

MOPITT CO surface vs. AOD

Chiang Mai

0.37 (N=265) 0.56 (N=265) 0.42 (N=69)

0.60 (N=74)

Lampangb

0.37 (N=265) 0.34 (N=265) 0.37 (N=76)

0.36 (N=84)

Chiang Rai

0.58 (N=178) 0.65 (N=178) 0.35 (N=56)

0.42 (N=56)

0.66 (N=114) 0.81 (N=46)

0.55 (N=47)

Maehongson 0.54 (N=177) 0.62 (N=177) 0.59 (N=63)

0.68 (N=63)

0.73 (N=108) 0.89 (N=51)

0.71 (N=54)

Nan

0.71 (N=26)

0.50 (N=58)

0.60 (N=41)

0.59 (N=89)

0.58 (N=89)

0.46 (N=26)

–

–

0.69 (N=35)

0.42 (N=54) –

a

Values of N given in parentheses are the number of data points

b

Lampang area may be affected by the emission of PM from Mae Moh power plant; hence, the AOD data were not analyzed

hotspots was also the lowest in this province as mentioned above. For illustration, Fig. 2 presents selected scatterplots for Maehongson which generally had higher correlation coefficients between the satellite parameters and the ground-based monitoring data (Table 1). Selected plots for other provinces are given in Fig. S2, SI. In general, the number of data points generated by MOPITT during the study period was still small as compared to the MODIS AOD. Overall, the good correlations between ground monitoring CO and PM10 data with the relevant satellite parameters can be further used to explore the potential use of the satellite data for air pollution monitoring in the Northern Thailand. In particular, the good correlations also support that the satellite MODIS AOD and MOPITT CO total column data can be used to investigate the CO and PM plume dispersion during selected forest fire episodes.

Spatial distribution of CO and AOT plumes during forest fire episodes Forest fire episode selection Two forest fire episodes were selected for the plume dispersion analysis, episode 1 (25–27 February) and episode 2 (5–8 March 2009), which satisfied the above-mentioned selection criteria. The time series of daily CO and PM10 concentration in rural and urban monitoring sites, respectively, and the total counts of forest fire hotspots in the five northern provinces of Thailand during the period from of 24 February–10 March 2009 are shown in Fig. 3. The period of 1–3

March was not classified as an episode because of generally lower pollution levels. Based on the criteria, the days of 10–14 March 2009 were also episodic (Table S3, SI), but because no MOPITT data were available for these days, they were accordingly excluded from the plume dispersion analysis. In episode 1, 24-h PM10 started rising in 24 February 2009 and exceeding the ambient air quality standard in both urban and rural background sites. Concordantly, the number of forest fire hotspots in the study area also increased. During this episode, daily CO concentrations also started rising in 24 February in every station. The highest 24-h CO concentrations, 1.5 ppm, were recorded in urban sites, which were above the 95 percentile of the data series. Daily CO concentrations in the rural sites were almost the same or higher than urban sites and the levels varied concordantly with the counts of forest fire hotspots which suggested the influence of the forest fire emission. Episode 2, 5–8 March, had 24-h PM10 in urban and rural sites well exceeded the ambient air quality standard every day. The variation in daily levels of the pollutants corresponded well with the variation in the number of forest fire hotspots, which also confirmed the influence of forest fires on the air quality in Northern Thailand.

Forest fire plume dispersion pattern during selected episodes In order to determine the dispersion pattern of CO and aerosol plumes during the forest fire episodes, the MOPITT CO total column and MODIS AOD were used, respectively. The MOPITT CO measurement is less


Maehongson

Maehongson 600 400 200 0 0

200 400 24h PM 10 (µg/m 3)

Maehongson

600

0.0

0.5

1.0

1.5

2.0

2.5

200 100 0 0.5

1.0

1.5

2.0

2.5

AOD

number of forest fire hotspot and daily PM10 concentration (µg/m 3 )

MOPITT CO Total (mole/cm2)

3h PM10

300

350

0 0.0

2.0

4.0

6.0

800

y = 134.9x + 334.34 R= 0.71

600 400 200 0 0.0

1.0

2.0

3.0

Maehongson y = 171.12x + 58.125 R = 0.73

400

Fig. 3 Forest fire hotspots (bars), daily PM10, and CO in rural and urban background stations during the selected episode, 25–28 February and 5–9 March 2009

200

AOD

Maehongson

sensitive to the boundary layer conditions (Edwards et al. 2004) than the MODIS AOD because the average CO lifetime (a few months) is much longer than the aerosol lifetime (a few days to a few weeks). Therefore, on the local scale, AOD is considered a more reliable indicator of the impact of forest fires than CO. However, the MOPITT CO is a more suitable indicator for the regional

400

Maehongson

3h CO (ppm)

0.0

600

3h CO (ppm)

y = 6E+17x + 2E+18 R = 0.59

6E+18 5E+18 4E+18 3E+18 2E+18 1E+18 0

y = 70.925x + 313.12 R= 0.68

800

y = 2E+18x + 2E+18 R = 0.89

8E+18 6E+18 4E+18 2E+18 0

0.0

1.0

2.0

3.0

AOD

and long-range transport of forest fire emissions. The combination of these two instruments would be useful for describing the forest fire plume evolutions (Edwards et al. 2004). During episode 1, large values of the MOPITT CO total column (about 3.2×1018 mol/cm2) were observed over Northern Thailand. Figure 4 presents the plume on total forest fire hotspot PM10 at Rural site CO at Rural site

PM10 at Urban site CO at Urban site

300 250 200 150 100 50 0

Date

2.0 1.8 1.6 1.4 1.2 1.0 0.8 0.6 0.4 0.2 0.0

daily CO concentration (ppm)


MOPITT CO surface (ppbv)

y = 0.7991x - 13.597 R = 0.62


Daily number of hotspot

Fig. 2 Relationships between satellite monitoring parameters and ground-based CO and PM10 in Maehongson rural area

Author's personal copy Environ Monit Assess 137(1.4) N

151(1.7)

119 (0.7)

244(1.2)

E

max. wind speed 4.3 m/s

Fig. 4 MODIS AOD (gray contours, interval of 0.2), MOPITT CO total column (molecules per square centimeter) (areas darkened with scales), surface wind vectors (10 m, at 10:00 LTS), and forest fire hotspots (white spots) at 10:00 LST, 26 February 2009. Note: The location of a city is marked with a star (CM Chiang Mai,

MHS Maehongson, LP Lampang, CR Chiang Rai, Nan Nan). Inserted numbers are PM10 levels in micrograms per cubic meter and in parentheses are CO levels in parts per million at the PCD stations (no data at Nan station on the day)

26 February when the highest CO total column (more than 4×1018 mol/cm2) was detected in Lampang and Nan provinces. For a better view, the corresponding color figure is also presented in Fig. S6, SI. The MODIS AOD values were also high, 0.8–1.0 over Nan on the same day. Dense hotspots were detected over the forest areas of the northern provinces. The prevalent southwesterly wind transported the smoke plumes (clearer shown by MOPITT CO than MODIS AOD) northeastward. The 24-h CO and PM10 levels measured at the ground stations were also higher where larger MOPITT and AOD data, respectively, were observed. However, limited data points of the ground monitoring would not allow the development of dispersion patterns of the plume which also confirmed the advantages of the satellite data. In episode 2, on 7 March, a large amount of forest fire hotspots was detected over the northern provinces. Generally, higher CO total column and AOD values than episode 1 were observed over the region. The maximum CO total column was 4.4×1018 mol/cm2, while AOD was generally in the range of 0.6–1.0 and specifically an AOD value of 1.2 was recorded over Chiang Rai. The highest values of CO total column reached 4.8×1018 mol/cm2 in

Chiang Rai, Nan, and Chiang Mai (Fig. 5 and Fig. S7). Wind speed was lower on 7 March as compared to that on 26 February (Fig. 4) and with more variations in the directions. The prevalent regional wind directions were still southerly to southwesterly but low wind speeds with varying directions circulated the plume over the study area. This situation lead to high air pollution concentrations recorded at ground monitoring stations which were also indicated by higher levels of corresponding satellite (AOD and MOPITT CO) data.

Conclusions High levels of CO and PM10 were recorded in ground monitoring stations in the dry months, February– April, during the period from 2008 to 2010 in Northern Thailand. Correlations between the ground-monitored CO and PM10, respectively, with satellite monitoring data were in reasonable ranges as compared with earlier studies for other places in the world. Ground CO levels were better associated with MOPITT CO surface data (R=0.36–0.71) than with MOPITT total column data (R=0.35–0.59). AOD and PM10 were generally better

Author's personal copy Environ Monit Assess 189(1.5) N

182(2.1)

191(1.1)

148(1.0)

E

max. wind speed 2.1 m/s

Fig. 5 MODIS AOD (gray contours, interval is 0.2), MOPITT CO total column (moles per square centimeter) (areas darkened with scales), surface wind vectors (10 m, at 10:00 LTS), and forest fire hotspots (white spots) at 10:00 LST, 7 March 2009. Note: The location of a city is marked with a star (CM Chiang Mai, MHS

Maehongson, LP Lampang, CR Chiang Rai, Nan Nan). Inserted numbers are PM10 levels in micrograms per cubic meter and in parentheses are CO levels in parts per million at PCD station (no data at Nan station on that day)

correlated (R=0.50–0.73) than MOPITT CO and groundmonitored CO, but the ranges of R values were largely overlapped. High correlations were found between AOD and MOPITT total CO column data, with R values of 0.69–0.89, which suggested that the PM pollution and CO pollution in the study area were probably associated with similar sources/processes. Correlation coefficients between CO and PM10 with hotspots, respectively, were higher in rural areas than in urban area showing more influence of forest fires on the rural air quality. Mapping the satellite AOD and MOPITT CO data could track the forest fire plumes during two selected fire episodes. The plume dispersion patterns were also consistent with the prevalent winds in the study area and the location of dense hotspots. More stagnant air with lower winds in one episode may be a reason of its higher pollution levels, indicated by both ground measurements and satellite monitoring data, than the other episode. The results of this study suggested that the MOPITT CO and MODIS AOT data have a good application potential for monitoring CO and PM, especially where the ground monitoring networks are sparse. In particular, forest fire smoke plumes tracked by satellite data, in

combination with predicted meteorological conditions, can be used to issue early warnings so that appropriate actions can be taken to protect people, their properties, as well as downwind forests from fire damages. To further improve the correlations between ground monitoring data and satellite data, the vertical profiles of satellite data should be incorporated and longer data series should be used for the regression analysis. Acknowledgments The authors would like to thank the Pollution Control Department (PCD) of Thailand for providing the air quality data. The National Aeronautics and Space Administration (NASA) are especially acknowledged for the readily available MOPITT data, MODIS data, and fire data (from the Fire Information for Resource Management System (FIRMS)). The authors also wish to thank the NOAA Air Resources Laboratory (ARL) for the wind data. Air quality team at AIT is specially thanked for their assistance during the course of this study.

References Bashkin, V. N. (2003). Environmental chemistry: Asian lessons. Dordrecht: Kluwer.

Author's personal copy Environ Monit Assess Bates, T. S., Kelly, K. C., Johnson, J. E., & Gammon, R. H. (1995). Regional and seasonal variations in the flux of oceanic carbon monoxide to the atmosphere. Journal of Geophysical Research, 100, 23093–23101. Chiang Mai Provincial Public Health Office (CMPHO) (2008). Chiang Mai Provincial Public Health Office, Strategy Division. http://www.chiangmaihealth.com/cmpho_web. Accessed May 2012. Dinoi, A., Perrone, M. R., & Burlizzi, P. (2010). Application of MODIS products for air quality studies over Southeastern Italy. Remote Sensing of Environment, 2(7), 1767–1796. doi: 10.3390/rs2071767. Drummond, J. R. (1992). Measurements of pollution in the troposphere (MOPITT). In J. Gille & G. Visconti (Eds.), The use of EOS for studies of atmospheric physics (pp. 77–101). New York: North Holland. Edwards, D. P., Emmons, K., Hauglustaine, D.A., Chu, D. A., Gille, J.C., Kaufman, Y.J., et al. (2004). Observations of carbon monoxide and aerosols from the Terra satellite: Northern Hemisphere variability. Journal of Geophysical Research, 109(D24202). doi:10.1029/2004JD004727. Edwards, D.P., Emmons, L. K., Gille, J. C., Chu, A., Attié, J.-L., Giglio, L. et al. (2006). Satellite-observed pollution from southern Hemisphere biomass burning. Journal of Geophysical Research, 111(D14312). doi:10.1029/2005JD006655. Engel-Cox, J., Huff, A., Kanabkaew, T., & Kim Oanh, N. T. (2012). Satellite tools for air quality management with focus on particulate matter. Chapter 4, In N. T. Kim Oanh (Ed.), Integrated air quality management: Asian case studies. CRC Press: Boca Raton, Florida. Forest Fire Control Division (FFCD) (2011). Fire statistics. http:// www.dnp.go.th/forestfire/2546/firestatistic%20Th.htm. Accessed 15 Aug 2011. Hair, J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (1998). Multivariate data analysis (5th ed., pp. 164–165). Upper Saddle River: Prentice-Hall. Heath Effect Institute (HEI) (2002). HEI Perspectives, understanding the health effects of components of the particulate matter mix: progress and next step. http://pubs.healtheffects.org/ getfile.php?u=244. Accessed 20 Sep 2012. Jesús S.-M.-A., Ernst S., Guido S., Andrea C., Peter S., Giorgio L., et al.(2012). Comprehensive monitoring of wildfires in Europe: the European Forest Fire Information System (EFFIS). In J. Tiefenbacher (Ed.), Approaches to managing disaster—assessing hazards, emergencies and disaster impacts (pp. 87–108). InTech. http://www.intechopen.com/ books/approaches-to-managing-disaster-assessing-hazardsemergencies-and-disaster-impacts/comprehensivemonitoring-of-wildfires-in-europe-the-european-forest-fireinformation-system-effis. Accessed Oct 2013. Khalil, M. A. K., & Rasmussen, R. A. (1984). Carbon monoxide in the Earth’s atmosphere: increasing trend. Science, 224, 54–56.

Kim Oanh, N. T., & Leelasakultum, K. (2011). Analysis of meteorology and emission in haze episode prevalence over mountain-bounded region for early warning. Science of the Total Environment, 409(11), 2261–2271. Kim Oanh, N. T., Piromyaporn, C., Ekbordin, W., & Wangwongwatana, S. (2005). Meteorological pattern classification and application for forecasting air pollution episode potential in a mountain-valley area. Atmospheric Environment, 39, 1211–1255. Koe, L., Arellano, A. F., & McGregor, J. L. (2001). Investigating the haze transport from 1997 biomass burning in Southeast Asia: its impact upon Singapore. Atmospheric Environment, 35, 2723–2734. Lamarque, J. F., Edwards, D. P., Emmons, L. K., Gille, J. C., Wilhelmi, O., Gerbig, C., et al. (2003). Identification of CO plumes from MOPITT data: application to the August 2000 Idaho-Montana forest fires. Geophysical Research Letter, 30(13). doi:10.1029/2003GL017503. McKernan E., Yurganov L., Tolton B.T., & Drummond J.R. (2001). MOPITT validation using ground-based IR spectroscopy. http://www.atmosp.physics.utoronto.ca/mopitt/sarcof. pdf. Accessed 24 Sep 2011. Pollution Control Department (PCD) (2012). Haze pollution situation in the northern region of Thailand on March 14, 2012. Resource document. Air Quality and Noise Management Bureau. http://aqnis.pcd.go.th/webfm_send/1482. Accessed 9 Apr 2012. Pollution Control Department (PCD) (2013). Manual for standard measurement methods of particulate matter in ambient air. Resource document. Air Quality and Noise Management Bureau. http://infofile.pcd.go.th/air/DustinAmbient.pdf?CFID= 17690991&CFTOKEN=61119767. Accessed 22 Oct 2013. Pope, C. A., III, Ezzati, M., & Dockery, D. W. (2009). Fineparticulate air pollution and life expectancy in the United States. New England Journal of Medicine, 360, 376–386. Rajab, J. M., Tan, K. C., Lim, H. S., & MatJafri, M.Z. (2011). Investigation on the carbon monoxide pollution over Peninsular Malaysia caused by Indonesia forest fires from AIRS daily measurement. In F. Nejadkoorki (Ed.), Advanced air pollution (pp. 115–136). InTech. http://www.intechopen. com/books/advanced-air-pollution. Accessed 18 Dec 2012. Song, C.-K., Ho, C.-H., Park, R. J., Choi, Y.-S., Kim, J., Gong, D. Y., et al. (2009). Spatial and seasonal variations of surface PM10 concentration and MODIS aerosol optical depth over China. Asia-Pacific Journal of Atmospheric Sciences, 45(1), 33–43. Tiyapirat, Y. (2012). Public sector responses to sustainable haze management in Upper Northern Thailand. Environment Asia, 5, 1–10. United Nations Environment Program (UNEP) and World Meteorological Organization (WMO). (2011). Integrated assessment of black carbon and tropospheric ozone. Nairobi: United Nations Environment Program.

Supplementary information

Satellite monitoring for carbon monoxide and particulate matter during forest fire episodes in Northern Thailand Manlika Sukitpaneenit1 and Nguyen Thi Kim Oanh 1,*

Table S1: number of forest fire hotspots in Northern Provinces, dry season of 2008-2010. Province

Area (km2)

Population

Chiang Mai

20107

Chiang Rai

11678

Number of forest fire hotspots

1,640,479

2008 1202

2009 1623

2010 2135

Total 4960

1,198,218

536

740

1041

2317

Maehongson 12681

242,742

1739

2019

2288

6046

Lampang

12534

761,949

737

744

711

2192

Nan

11472

476,363

434

582

1781

2797

Table S2. Levels of 24h PM10 and CO during the period from 20 February to 15 March, 2009 Date

24h PM10 (µg/m3) urban rural 135 86 96 77 74 75 77 85 95 111 153 155 137 135 129 134 164 174 185 188 133 144 123 129 118 160 133 177 167 202 189 237 163 207 183 209 178 244

20-Feb 21-Feb 22-Feb 23-Feb 24-Feb 25-Feb 26-Feb 27-Feb 28-Feb 1-Mar 2-Mar 3-Mar 4-Mar 5-Mar 6-Mar 7-Mar 8-Mar 9-Mar 10-Mar

Daily forest fire hotspot 4 48 8 160 19 67 112 89 225 24 204 30 277 167 196 239 134 376 49

24h CO (ppm) urban rural 1.1 0.8 0.9 0.8 1.0 0.8 1.0 0.8 1.0 0.9 1.5 1.2 1.4 1.2 1.1 1.1 1.5 1.2 1.5 1.2 0.8 1.0 1.1 0.9 0.6 1.0 1.2 1.2 1.7 1.3 1.5 1.6 1.3 1.4 1.6 1.5 1.3 1.7

Remark

11-Mar

300

208

253

1.7

1.8

12-Mar

101

160

251

1.5

1.8

13-Mar

231

200

235

2.1

1.5

episode, no MOPITT

14-Mar

107

165

228

1.3

1.4

episode, no MOPITT

15-Mar

41

70

167

1.0

1.2

non-episode

non-episode non-episode non-episode non-episode non-episode episode episode episode episode non-episode non-episode non-episode non-episode episode episode episode episode episode episode, no MOPITT episode, no MOPITT episode, no MOPITT

Fig. S1 Land use map of the study area in 2008

500

y = 119.82x - 58.185 R = 0.58

400 300 200 100 0 0.0

1.0

2.0

3.0

Maehongson



Chiang Rai

y = 99.252x - 13.966 R = 0.54

500 400 300 200 100 0 0.0

24h CO (ppm)

100

200

24h PM10 (µg/m3)

300

Maehongson Daily number of hotspot


y = 1.028x - 36.223 R = 0.65

0

4.0

6.0

24h CO (ppm)

Chiang Rai 500 400 300 200 100 0

2.0

500 400 300 200 100 0 0

y = 0.7991x - 13.597 R = 0.63

200 400 24h PM 10 (µg/m3)

600

Fig. S2 Levels of 24h ground-based CO (ppm) and PM10 (µg/m3) with the daily number of forest fire hotspots counted in Chiang Rai and Meahongson, February-April (2008-2010)

MOPITT CO Total Column (mole/cm2)

y = 7E+17x + 3E+18 R = 0.42

6E+18 5E+18 4E+18 3E+18 2E+18 1E+18 0 0.0

0.5

1.0

1.5

2.0


Maehongson

Chiang Mai

y = 6E+17x + 2E+18 R = 0.59

6E+18 5E+18 4E+18 3E+18 2E+18 1E+18 0

0.0

2.5

3h ground CO (ppm)


y = 137.28x + 303.15 R = 0.60

800 600 400 200 0

1.0 2.0 3.0 3h ground CO (ppm)


Maehongson

Chiang Mai

0.0

0.5 1.0 1.5 2.0 2.5 3h ground CO (ppm)

y = 70.925x + 313.12 R= 0.68

800 600 400 200 0 0.0

1.0 2.0 3.0 4.0 3h ground CO (ppm)

5.0

Fig. S3 Scatter plots of the 3-hourly ground-based CO (09:00-12:00 LST) with MOPITT CO total column and CO surface mixing ratio (at 10:30 LST) Maehongson

y = 171.12x + 58.125 R = 0.73

300 200

y = 145.12x + 35.87 R = 0.66

400

3h PM10

3h PM10

400

Chiang Rai

300 200 100

100

0

0 0

0.5

1 1.5 AOD

2

2.5

0

0.5

1

1.5

2

2.5

AOD

Fig. S4 Scatter plots of 3-hourly PM10 (µg/m3 ) (09:00-12:00 LST) and MODIS AOD Terra, at 10:30 LTS data (30×30 km2 grid resolution), in February- April 2008-2010, additional cloud screening applied

y = 2E+18x + 2E+18 R = 0.81

7E+18 6E+18 5E+18 4E+18 3E+18 2E+18 1E+18 0 0.0

Maehongson

1.0 2.0 AOD

y = 2E+18x + 2E+18 R = 0.89

6E+18 4E+18 2E+18 0 0.0

1.0

2.0 AOD

800

3.0

y = 111.12x + 399.6 R = 0.55

600 400 200 0 0.0

3.0

Maehongson MOPITT CO surface (ppbv)

8E+18 MOPITT CO Total (mole/cm2)

Chiang Rai MOPITT CO surface (ppbv)


Chiang Rai

1.0

2.0 AOD

3.0

y = 134.9x + 334.34 R= 0.71

800 600 400 200 0

0.0

1.0

2.0

3.0

AOD

Fig. S5 Scatter plots of the MOPITT CO (total column) and the MODIS AOD data on Terra satellite at 10:30 LST during February- April in 2008-2010

Fig. S6 Color version of Fig. 4 (26 February 2009)

Fig. S7 Color version of Fig.5 (7 March 2009)