Analysis of meteorology and emission in haze episode prevalence over mountain-bounded region for early warning

Science of the Total Environment 409 (2011) 2261–2271

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Science of the Total Environment j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / s c i t o t e n v

Analysis of meteorology and emission in haze episode prevalence over mountain-bounded region for early warning Nguyen Thi Kim Oanh ⁎, Ketsiri Leelasakultum Environmental Engineering and Management, SERD, Asian Institute of Technology, P.O. Box 4, Klong Luang, Pathumthani 12120, Thailand

a r t i c l e

i n f o

Article history: Received 29 July 2010 Received in revised form 10 February 2011 Accepted 16 February 2011 Keywords: Synoptic climatological approach Meteorological classification Haze alert Biomass burning PM prediction Northern Thailand

a b s t r a c t This study investigated the main causes of haze episodes in the northwestern Thailand to provide early warning and prediction. In an absence of emission input data required for chemical transport modeling to predict the haze, the climatological approach in combination with statistical analysis was used. An automatic meteorological classification scheme was developed using regional meteorological station data of 8 years (2001–2008) which classified the prevailing synoptic patterns over Northern Thailand into 4 patterns. Pattern 2, occurring with high frequency in March, was found to associate with the highest levels of 24 h PM10 in Chiangmai, the largest city in Northern Thailand. Typical features of this pattern were the dominance of thermal lows over India, Western China and Northern Thailand with hot, dry and stagnant air in Northern Thailand. March 2007, the month with the most severe haze episode in Chiangmai, was found to have a high frequency of occurrence of pattern 2 coupled with the highest emission intensities from biomass open burning. Backward trajectories showed that, on haze episode days, air masses passed over the region of dense biomass fire hotspots before arriving at Chiangmai. A stepwise regression model was developed to predict 24 h PM10 for days of meteorology pattern 2 using February–April data of 2007–2009 and tested with 2004– 2010 data. The model performed satisfactorily for the model development dataset (R2 = 87%) and test dataset (R2 = 81%), which appeared to be superior over a simple persistence regression of 24 h PM10 (R2 = 76%). Our developed model had an accuracy over 90% for the categorical forecast of PM10 N 120 μg/m3. The episode warning procedure would identify synoptic pattern 2 and predict 24 h PM10 in Chiangmai 24 h in advance. This approach would be applicable for air pollution episode management in other areas with complex terrain where similar conditions exist. © 2011 Elsevier B.V. All rights reserved.

1. Introduction Air pollution episodes are the periods with high levels of airborne pollutants, causing a sharp rise in mortality and morbidity. Episode formation depends on both meteorological conditions (e.g. stagnant air) and emission source intensity. Certain topography, such as bowlshaped valleys, is known as episode-prone because it restricts the pollution dispersion. Examples of the famous particulate air pollution episodes are those occurred in Donora of Pennsylvania in 1948 (20 deaths) and in London in 1952 (4000 deaths) as discussed in Fierro (2000). Chiangmai city, also known as the northern capital of Thailand, experiences air pollution haze episodes around March each year. The most severe haze episodes were observed in March 2007 when the whole city was blanketed with haze for a few weeks. Data from the ambient air monitoring stations of the Pollution Control Department (PCD) showed that the 24 h average PM10 level (particles with

⁎ Corresponding author. Tel.: +66 2 524 5641. E-mail address: [email protected] (N.T. Kim Oanh). 0048-9697/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.scitotenv.2011.02.022

aerodynamic diameter below 10 μm) in the city reached the peak of 396 μg/m3 on March 13. The number of respiratory patients increased sharply in March 2007, i.e. the number of respiratory patients recorded from 23 Chiangmai public hospitals in the month (21,336) was much higher than March 2006 (16,718), March 2008 (18,025) and April 2007 (15,826) (CMPHO, 2008). The onset of severe haze episodes such as that in March 2007 is a combination of many factors. A thorough analysis of the underlying causes and development of a predictive model would be necessary for episode management. Warning signals in particular, can be issued for timely responses that will reduce source emission and unnecessary community exposure to high air pollution. Application of comprehensive 3D chemical transport models to predict air pollution episodes would be straightforward and the most desirable in any case. However, the absence of an adequate emission input data with required spatial and temporal distributions does present the greatest obstacle to any air quality modeling efforts for the area. Some complex model systems, such as CMAQ-MM5 (Nghiem and Kim Oanh, 2008) or UAM-V/SAIMM (Kim Oanh and Zhang, 2004), were successfully applied in the region but for general ozone air quality investigation and not for haze episode management

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purposes. These previous studies largely relied on the annual emission inventory data prepared on coarse spatial grids hence are not adequate for the episode simulation which requires, for example, detailed temporal emission resolutions such as daily or even hourly variations. While development of a suitable emission input data for episode prediction in Chiangmai is much desired it requires time and resources to accomplish. In this situation, our study provided an alternative which uses the synoptic climatological approach to screen for the haze prone meteorology conditions that can be the basis for the haze warning, and a statistical model to predict the next day 24 h PM10 for Chiangmai. A similar approach was used successfully in a few previous studies to analyze impacts of weather and climate on air pollution (Heidorn and Yap, 1986; Kalkstein and Corrigan, 1986; Davis and Kalkstein, 1990; Kallos et al., 1993; Berman et al., 1995; Lam and Cheng, 1998; Triantafyllou, 2001; Kim Oanh et al., 2005). Chiangmai City is the capital of the Chiangmai province, and is the second largest city in Thailand after Bangkok. The Chiangmai province has a total area of 20,107 km2 and is centered at coordinates of 18°47′ N and 98°59′ E. Around 15% of its area is a basinshaped flat plain, surrounded by mountain ranges on both the western and eastern sides (Wattananikorn et al., 1995). The basin, where densely populated districts present, is located in the central part of the province (Fig. S1, Supplementary Information—SI). This mountain-valley topographical type limits the dispersion of air pollution and is another challenge for 3D air quality model applications. The climate of Chiangmai is characterized by monsoons. The period from mid-February until the end of May is the transition period from Northeast monsoon (most prevalent in December–January) to Southwest monsoon (most prevalent in July–August). Hottest weather observed during March–April coincided with the presence of intensive thermal lows in the area (TMD, 2003). Similar to other urban areas of developing countries, Chiangmai air quality is influenced by traffic, industries and other urban sources. In addition, the city is surrounded by forest and agricultural fields, hence is subject to significant air pollution from biomass open burning. An emission inventory (EI) conducted by the Pollution Control Department of Thailand (PCD, 2002) for Chiangmai City (Muang district of Chiangmai) estimated the total emission of particulate matter (PM) to be 700 tonnes of which 89% was from forest fires, 5.4% from solid waste burning and 2.3% from agriculture residue field burning. Point sources (industry) contributed only 0.08%, mobile sources 2.6% and other sources 0.56%. Note that this EI was meant for the Chiangmai city alone, not including the surrounding area of the Chiangmai province. There are 2 automatic ambient monitoring stations, one is located in the business center of city at the Yupparaj school and the other is at the City Hall, about 6 km away (Fig. S1, SI). The most remarkable ambient air pollution in Chiangmai is the particulate matter (PM). The hourly PM10 mass concentrations at the 2 sites were recorded using the Tapered Element Oscillating Microbalance (TEOM). No data on fine PM (particles of size below 2.5 μm or PM2.5) mass or PM composition were available in Chiangmai during the episode periods. Thus, only PM10 mass concentrations were used in our analysis. High PM10 is observed every year during the February– April period and the most severe PM10 pollution was observed in March 2007 when the monthly average level was the highest, 330– 400 μg/m3, as compared to the levels of generally below 250–300 μg/ m3 in March of other years (see detail monthly fluctuations during 2001–2008 in Fig. S2, SI). In this study, we consider a haze episode to be the period that had more than seven consecutive days with observed 24 h PM10 levels at the two Chiangmai stations exceeding 120 μg/m3 (Thailand National Ambient Air Quality Standard or NAAQS in short). Based on the criteria, most of March 2007 was classified as the haze episodes.

2. Methodology 2.1. Meteorological classification This study developed an automatic scheme to classify the meteorological conditions governing over the Northern Thailand during the haze prevalent months (February–April) into homogenous patterns. We used the meteorological data collected from a regional weather station network for the classification, or in other words, the spatial synoptic index (SSI) approach was applied. Given the right selection of meteorological variables, SSI can combine both the air-mass based and the flow pattern (weather type) approaches hence was successfully applied in a number of studies (Davis and Kalkstein, 1990; Kim Oanh et al., 2005). The surface meteorological data for 7:00 LST (00 UTC) in February– April months of 8 years (2001–2008) were collected from 4 stations in Thailand (Chiangmai, Bangkok, Nongkhai and Nakhonpanom) and 3 stations in China (Yibin, Wuhan and Haikou) with the detail on station locations shown in Fig. S3, SI. The surface synoptic charts and the surface meteorological data of the Thai stations were collected from the Thailand Meteorological Department (TMD). The morning vertical temperature profiles were obtained from the University of Wyoming website (http://weather.uwyo.edu/upperair/sounding.html). The meteorological data from stations in China were obtained from the University Corporation for Atmospheric Research (UCAR) website (http://dss.ucar.edu/). As compared to the automatic scheme developed by Kim Oanh et al. (2005), which was applied for almost the same region but for different periods of time (November–January), the following modifications were made in this study which appeared to improve the resulting classification scheme for the study period of February–April. Accordingly, this study considered 2 more weather stations in Northeastern Thailand (Nongkhai and Nakhonpanom) but omitted Lampang of Thailand and Chengdu of China. In addition, the morning mixing height in Chiangmai, determined by the air parcel method (Holzworth, 1971), was used instead of the upper air data at 850 mb used in the previous study. This study also included in the initial set of data the surface wind information in terms of u and v components, as well as the wind speed and wind direction index (WDI). The latter was used to remove the discontinuity of the wind direction (θ) angle at 360°, particularly suitable for the prevalent directions of monsoons (N–NE and SW) in the study area, and was calculated using Eq. (1).  π WDI = 1 + sin θ + 4

ð1Þ

The data processing procedure was basically similar to the previous study (Kim Oanh et al., 2005). First, a trial set of meteorological variables was selected and the principal component analysis (PCA) was conducted on the correlation matrix of the dataset using SPSS (SPSS, 2009). Further, those principal components (PC) with eigenvalue N1.0 (after Varimax rotation) and with at least one loading above 0.5 were retained for the K-means clustering analysis. The selected PCs should collectively explain more than 70% of the original data variance. Next, K-mean clustering classified all the days of February, March and April of 2001–2008 (totally, 714 days) into a certain number of meteorological patterns (3, 4, 5, and 6 were tried). Several trial sets of meteorological variables were examined until the classified patterns, produced by the automatic scheme, appeared consistent with that based on the actual synoptic charts. A set of 18 meteorological variables was finally selected that could be grouped into 3 subsets. The first subset primarily included the local meteorological variables of Chiangmai (with cm index): cloud cover (Cl_cm), dewpoint temperature (Td_cm), morning mixing height (MorningMH_cm), visibility (Vis_cm), wind direction index (WDI_cm), wind speed (Wsp_cm), and sea level pressure (SLP_cm).

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The second subset included variables related to sea level pressure in the selected stations: Bangkok (SLP_bkk), Nakhonphanom (SLP_np), Yibin (SLP_ybC), Wuhan (SLP_whC) and Haikou (SLP_hkC). The third subset comprised of variables related to the pressure horizontal gradients between Chiangmai and other stations: Bangkok, Nongkhai, Nakhonphanom, Wuhan, Yibin and Haikou (PG1_bkkcm, PG2_nkcm, PG3_npcm, PG4_whcm, PG5_ybcm, and PG6_hkcm). It is worth mentioning that the u and v components of the surface wind were not present in the final selected dataset, but the WDI and wind speed were. The high frequency of calm wind observed at 7:00 LST during the study period, i.e. 74%, appeared to be a reason for this automatic exclusion of the u and v components. Note that in a number of calm wind observations the prevalent directions were given, hence the dataset of WDI was more complete than that of the u and v wind components.

to represent the open burning outside Chiangmai. These areas are shown in Fig. 7 and also in Fig. S3, SI. Note that this rectangular area coincided with the forest area of Myanmar that was generally observed with dense hotspots during March 2007. It was also generally the area that air masses pass through before arriving at Chiangmai, as shown later. Five-day backward trajectories (http://www.arl.noaa.gov/ready/ hysplit4.html) were obtained for each day during the severe haze period of March 1–April 4, 2007 when PM10 levels were high in Chiangmai (Fig. 1). This back trajectory analysis is commonly used to identify the regional and long range transports of air pollution (Begum et al., 2005; Pongkiatkul and Kim Oanh, 2007; Grivas et al., 2008). In our study, the trajectories were determined starting at 7:00 LST (00 UTC) and 500 m height AGL over the Chiangmai meteorological station (WMO ID 48327 at 18°47′ N and 98° 59′ E).

2.2. Monthly fluctuation in emission strength

3. Results and discussion

March 2007 was characterized by the exceptionally high PM10 levels in Chiangmai (Fig. S2, SI). Abnormally high emission source strength in the month was hypothesized as one of the causes of the air pollution episodes. In the lack of detailed activity data and emission factors to develop an EI, this study only examined the monthly variations in emission intensity for 2007 using available emission proxy/surrogate data. Major emission sources (traffic, industries, and biomass open burning) in 2007 were considered for the 6 Northern provinces including Chiangmai and five surrounding provinces (Mae Hong Son, Tak, Lamphun, Lampang, and Chiang Rai). Collected data consisted of the monthly petroleum wholesale from the Department of Energy Business (proxy of traffic emission), productivity of industries from the questionnaire survey (proxy of industry emission), burning pixels of 500 m resolution from Web Fire Mapper (proxy of open burning), crop harvest areas (proxy of crop residue field burning) and the harvest time of major crops (rice, maize, sorghum, bean, and cassava) from the Office of Agriculture Economics. These surrogates were the best available data, hence even though they were not the real emission they were considered for the relative monthly emission intensity analysis. The study by Duprey (1968), for example, also suggested that emissions from vehicles can be determined on the basis of gasoline sales in a given study area. A more detailed survey was conducted for major seasonal industries in the Northern Thailand including rice mills, dried crop/ fruit manufacturers, brick kilns, and tobacco curing. Note that only “type 3” industries (with more than 50 mechanical horsepower or labors) as classified by the Department of Industrial Work (http:// www.diw.go.th) were included in the survey. Over 300 questionnaires were sent by post but only around 50 responses were received. Consequently, telephone interviews and field visits were further made to gather necessary information representing these major seasonal industries in the study area.

3.1. Synoptic meteorology patterns

2.3. Daily emission and trajectory analysis To get information on the daily open burning, we examined the active fire locations using data from the Web Fire Mapper (http://maps.geog.umd.edu/). Our study selected to use a detection confidence below 50% to be a criterion to exclude false positive identification of fires. An active fire, is also referred to as a hotspot, approximately represents the center of a 1 km pixel flagged as containing one or more actively burning hotspots/fires. These hotspots are detected by the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument, on board of NASA's Aqua and Terra satellites. We used the daily hotspot counts for the circular area of 150 km radius surrounding Chiangmai to represent the local fires and those counted on the rectangular area to the west of Chiangmai (16°00–25°8′ N; 92°37′–97°37′ E), with an area of 557 × 1009 km2,

The PCA, conducted on the final set of 18 meteorological variables, produced the first five PCs that collectively explain 81% of the variance in the raw dataset. Table 1 presents the loadings of meteorological variables on each PC, values N0.5 were considered significant and bolded. The first PC, explaining 22.1% of the data variance, represented the regional pressure field influencing Thailand as seen from the high positive loadings of the sea level pressure at two weather stations in the central China (Yibin and Wuhan) and the horizontal pressure gradients between those stations and Chiangmai, respectively. The second PC, which explains around 21.9% of the data variance, had high loadings of the sea level pressure in Chiangmai, Bangkok, Nakhonpanom of Thailand and Haikou of China (located at about the same latitude of Chiangmai, Fig. S3). The pressure gradient between Bangkok and Chiangmai (PG1) had a high negative loading. The second PC, thus, mainly represented the pressure field at lower latitudes, i.e. around Northern Thailand. The third PC explained 18% of the variance and was linked with the horizontal pressure gradient field. High loadings of variables representing pressure gradients between Chiangmai and the northeast stations of Thailand (Nongkhai and Nakhonpanom) and Haikou of China, respectively, were obtained on this PC. The fourth PC explained around 11% of the data variance, and had high positive loadings of cloudiness, dewpoint temperature, morning mixing height, and visibility measured in Chiangmai. Thus, this PC was related to moisture aloft and stability conditions of the local air mass. We further note that visibility links to both PM pollution and meteorology, and it appeared to be a relevant parameter retained in the final dataset. Wind direction index and wind speed at Chiangmai had high loadings on the fifth PC, which explained around 8% of the data variance. Hence this PC would be linked to dynamic characteristics of the local air mass. K-means clustering technique was applied on the component score matrix (714 days, 5 PC) and produced four clusters corresponding to four synoptic patterns. They are distinguished from each other in terms of the mean values of meteorological variables (Table 2) and typical synoptic charts (Fig. 2). Pattern 1 is characterized by the highest values of cloudiness, mixing height, dewpoint and visibility in Chiangmai. The highest moisture content with updraft conditions associated with this pattern would enhance the vertical dispersion of air pollution. Considerable high and variable wind speed (1.1 ± 1.4 m/s, second to pattern 3) indicates also a good horizontal dispersion. The inserted windrose in Fig. 2, constructed based on 7:00 LST data of 8 years taken from the Chiangmai weather station, shows that the wind directions are mainly northerly to easterly with the highest frequency of NE while calm wind consisted of 55%. As seen in Table 2, the pressure gradient between Chiangmai and Wuhan was the lowest, followed by the

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Fig. 1. Levels of PM10 identified as haze episode period at Yupparaj (city) and City Hall (suburban) stations during February–April.

values measured between Chiangmai and Yibin. The average pressure gradient between Chiangmai and Bangkok is negative. Synoptic charts of this pattern typically show a weakening high pressure over China with the maximum pressure in the central China of around 1018 mb. The Intertropical Convergence Zone (ITCZ) is observed across the Southeast Asia. Several thermal lows are seen over East Asia. Additionally, a stationary front line is often seen in the Southeast part of China. This pattern has the lowest frequency of occurrence (11%) and is mostly observed in April (Table 3). Pattern 2 is characterized by the lowest sea level pressure at four stations of Thailand and Yibin of China (Table 2). The pressure gradients between Chiangmai and the Northeast stations of Thailand and Haikou of China are the lowest or second lowest. Low dewpoint (second lowest) indicates a dry weather. Low wind speed, 0.2 m/s (second lowest), indicates less horizontal dispersion of pollutants. Windrose for Chiangmai at 7:00 LST shows over 90% of calm conditions and the rest of mostly northerly weak wind (Fig. 2). Sky

is clear with a minimum cloudiness (1/10) and lowest morning mixing height. These all indicate stable conditions that enhance the build-up of high air pollution levels. The synoptic charts of this pattern typically show a vast thermal low with several cells observed over the East, Southeast Asia and central China. In most cases, a thermal low is observed over Northern Thailand. A ridge from the high pressure in the Northeastern China extends toward Southeast Asia and the Pacific Ocean (Fig. 2). This pattern has the highest occurrence frequency during the 8 year period (49%) and it is mainly observed in March and April (Table 3). Pattern 3 is observed with the highest wind speed (1.9 m/s) and the lowest percentage of calm conditions (24%). Wind directions are most variable in this pattern with the highest frequency of westerly to southwesterly winds. All other meteorological variables presented in Table 2 are the second highest. High average values of cloudiness, morning mixing height, dewpoint and visibility indicate good ventilation conditions over Chiangmai. On synoptic charts, a vast

Table 1 Loading on five components with Varimax rotation. Variable

PG5_ybcm PG4_whcm SLP_ybC SLP_whC SLP_cm SLP_bkk SLP_np SLP_hkC PG1_bkkcm PG3_npcm PG2_nkcm PG6_hkcm Cl_cm Td_cm MorningMH_cm Vis_cm WDI_cm Wsp_cm Eigenvalue % Variance % Cum var.

Principal component 1

2

3

4

5

Communalities

0.9402 0.9337 0.8700 0.8552 0.1161 0.0375 0.1965 0.3853 −0.2515 0.2338 0.1767 0.5193 0.0232 −0.3093 −0.0185 −0.1112 −0.0015 −0.0007 3.97 22.08 22.08

−0.0049 0.0286 0.3476 0.3838 0.9723 0.8754 0.8106 0.7411 −0.6873 0.1320 0.2181 0.3001 0.1863 −0.2823 0.0566 −0.0802 −0.0324 −0.0195 3.94 21.89 43.96

0.1692 0.1582 0.2021 0.1918 0.1466 0.3124 0.5374 0.5067 0.3523 0.9273 0.9024 0.6885 0.1349 0.1725 −0.0048 −0.1840 −0.0653 0.0147 3.15 17.53 61.49

−0.0585 −0.1464 −0.0554 −0.1313 −0.0107 −0.0873 −0.0004 −0.0444 −0.1887 0.0171 0.0784 −0.0623 0.7575 0.6861 0.6653 0.6087 0.0633 0.2930 2.05 11.38 72.87

0.0212 −0.0226 0.0196 −0.0187 0.0025 −0.0078 0.0174 0.0163 −0.0271 0.0339 0.0712 0.0242 0.1273 0.0836 0.0066 0.0262 −0.8934 0.8130 1.49 8.29 81.16

0.9165 0.9197 0.9220 0.9330 0.9804 0.8730 0.9848 0.9567 0.6960 0.9333 0.9044 0.8382 0.6435 0.6829 0.4463 0.4239 0.8074 0.7475

Remarks: The high loadings (N0.5) are set in bold. PG1_bkkcm, PG2_nkcm, PG3_npcm, PG4_whcm, PG5_ybcm, PG6_hkcm are the pressure gradients between Chiangmai and Bangkok, Nongkhai, Nakhonphanom, Wuhan, Yibin, and Haikou, respectively. SLP_ybC, SLP_whC, SLP_cm, SLP_bkk, SLP_np, SLP_hkC are the sea level pressures at Yibin, Wuhan, Chiangmai, Bangkok, Nakhonphanom and Haikou, correspondingly. Cl_cm, MorningMH_cm, Td_cm, Vis_cm, WDI and Wsp_cm are the cloud cover, morning mixing height, dewpoint, visibility, wind direction index and wind speed in Chiangmai. Locations of these stations are shown in Fig. S3, SI.

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Table 2 Average meteorological variables for four-synoptic patterns. Variables

PG1_bkkcm (mb) PG2_nkcm (mb) PG3_npcm (mb) PG4_whcm (mb) PG5_ybcm (mb) PG6_hkcm (mb) SLP_ybC (mb) SLP_whC (mb) SLP_cm (mb) SLP_bkk (mb) SLP_np (mb) SLP_hkC (mb) Cl_cm MorningMH_cm (m) Td_cm (°C) Vis_cm (m) WDI Wsp_cm (m/s)

Synoptic patterns 1

2

3

4

−0.5 ± 0.9 0.9 ± 1.3 1.3 ± 1.4 5.3 ± 5.5 3.9 ± 6.5 3.1 ± 2.1 1015 ± 6.8 1016.3 ± 6.1 1011 ± 2.3 1010.5 ± 2.0 1012.3 ± 2.9 1014.1 ± 3.8 5.6 ± 2.1 473 ± 165 20.6 ± 1.7 9950 ± 2400 1.6 ± 0.4 1.1 ± 1.4

0.4 ± 0.7 0.6 ± 0.9 1.2 ± 1.0 8.2 ± 6.1 5.4 ± 6.3 3 ± 2.2 1014.7 ± 6.3 1017.5 ± 6.2 1009.3 ± 1.9 1009.7 ± 1.6 1010.5 ± 2.0 1012.3 ± 3.1 1 ± 1.2 330 ± 45 16.5 ± 3.2 6930 ± 2530 1.7 ± 0.1 0.2 ± 0.5

0.1 ± 1.0 1.9 ± 1.8 2.4 ± 1.9 9.6 ± 6.4 7.7 ± 6.9 5.1 ± 3.1 1018.6 ± 8.4 1020.4 ± 8.0 1010.9 ± 3.0 1011 ± 2.5 1013.2 ± 4.1 1015.9 ± 5.2 2.4 ± 2.5 347 ± 80 17.2 ± 3.0 7190 ± 2350 0.3 ± 0.5 1.9 ± 0.8

−0.3 ± 0.9 2.7 ± 1.5 3.3 ± 1.6 13.5 ± 5.2 11 ± 5.2 7.2 ± 2.2 1024.1 ± 4.5 1026.6 ± 5.1 1013.1 ± 2.3 1012.8 ± 2.1 1016.5 ± 2.3 1020.3 ± 2.4 2 ± 2.1 332 ± 47 15.8 ± 2.7 6340 ± 2420 1.7 ± 0.1 0.1 ± 0.5

See the remarks in the footnote of Table 1.

thermal low with several cells is observed over the East and Southeast Asia. The high pressure area over China is weakened (max 1020 mb). As compared to pattern 1, no ITCZ is observed over the Southeast Asia. Instead, a few low pressure cells with frontal systems are observed over the Pacific Ocean off the coast of Southeast China (Fig. 2). Pattern 3 is not frequently observed (13%) and is rather well distributed over the three months.

Pattern 4 is characterized by the highest sea level pressure in all stations in Thailand and China (Table 2). The pressure gradients between three weather stations in China and those in the Northern Thailand are also the highest showing the intensity of the high pressure system in China (max 1040 mb). A strong stationary ridge dominates over the Southeast Asia (Fig. 2). The 100% of calm wind in Chiangmai at 7:00 LST is a distinguished feature of this pattern in

Fig. 2. Examples of regional surface synoptic charts at 07.00 LST (00 UTC) with inserted windroses: (I) pattern 1 (30 April 2007), (II) pattern 2 (13 March 2007), (III) pattern 3 (27 March 2007), and (IV) pattern 4 (1 February 2007).

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Table 3 Frequency of four synoptic patterns in February–April of 2001–2008 (days). Month

Synoptic pattern

February March April Total days

Total days

1

2

3

4

Unclassified

3 22 50 75

82 131 138 351

29 34 33 96

111 58 16 185

1 3 3 7

226 248 240 714

79 ± 41 249 156 22

89 ± 42 236 183 49

– – – –

– – – –

24 h PM10 statistics (based on a combined set of measurement data from 2 stations) Average (μg/m3) Absolute maximum (μg/m3) 95th percentile (μg/m3) PM10 N 120 μg/m3 at any station (days)

40 ± 15 107 74 0

97 ± 54 396 234 98

addition to the clear sky and dry air (second lowest dewpoint). The stable atmosphere associated with anticyclone systems (inversion, weak wind) limits the pollutant dispersion both in vertical and horizontal directions. This pattern occurs quite often (26%) and is mainly observed in February when the study area is under the influence of the Northeast monsoon. 3.2. PM10 levels in different synoptic meteorology patterns The statistics of 24 h PM10 in Chiangmai for the February–April months of 2001–2008 show significant difference between the 4 patterns (Table 3 and Fig. 3). The highest 24 h PM10 level was observed in pattern 2 (97 ± 54 μg/m3), second highest was in pattern 4 (89 ± 42 μg/m3), followed by pattern 3 (79 ± 41 μg/m3) and the lowest was for pattern 1 (40 ± 15 μg/m3). The 24 h PM10 maximum and 95th percentile value observed in pattern 2 (396 μg/m3 and 234 μg/m3) were clearly the highest among all patterns. The number of days exceeding the 24 h PM10 NAAQS of 120 μg/m3 at any station was also ranked from pattern 2 (98 days of total 351 days, 28%), to pattern 4 (49 days of total 185 days, 26%), followed by pattern 3 (22 days of total 96 days, 23%) and none was in pattern 1 (0 day of total 75 days). High levels of PM10 in pattern 2 and pattern 4 are in line with their association with stagnant air conditions discussed above. In particular, clear sky, light wind, low mixing height, and low dewpoint observed in these patterns could enhance the formation of ground-based

radiative inversion. In principle, in pattern 4 this ground-based inversion would combine with the subsidence inversion typical for a high pressure system to further restrict the vertical dispersion of air pollution. Vertical temperature profiles, shown in Fig. 4, indicate an intensive inversion layer extending to above 900 m high on March 13 and 14, 2007 (pattern 2), and February 1, 2007 (pattern 4) while a much weaker morning inversion was observed on April 29, 2007 (pattern 1). Thus, in terms of stagnant air conditions, patterns 2 and 4 seem to be quite similar. However, the surface and upper air temperatures in pattern 2 (observed in hot months of March and April) are higher than pattern 4 (mainly in February), as expected. Most strong haze episode days in 2004 and 2007 were identified as outliers in pattern 2 while none of the March haze episode days was seen in pattern 4 (Fig. 3). Meteorologically, both pattern 2 and pattern 4 appeared to associate with high pressure ridges extending from China toward the Southeast Asia but the ridge in pattern 4 was stronger with the higher sea level pressure recorded at all the stations (Table 2). This is because pattern 4 appeared earlier (mainly in February) while pattern 2 appeared later, in March–April. The distinguished high PM10 in pattern 2 coinciding with its prevalence in March and April (Table 3) suggests that, besides meteorology, other major factors such as source emission strength may contribute to the high air pollution episodes during these months. We examined this hypothesis with an in depth analysis for the March 2007 haze episode. 3.3. Emission and meteorology in March 2007 haze episode

400

24-h PM10 levels, µg/m3

Mar 13, 2007

300

Mar 12, 2007 Feb 27, 2004 Mar 26, 2004 Mar 10, 2004

200

100

Apr 08, 2004

0 1

2

3

4

Pattern Fig. 3. Box plots of the average value of daily PM10 concentrations from two Chiangmai monitoring stations in different synoptic patterns, Feb–April of 2001–2008. The horizontal line across the box indicates the 50th percentile (the median), the lower and upper edges (hinges) are the 25th and 75th percentiles. Whiskers show the range of values that fall within 1.5 box-lengths (1.5 times the inter-quartile range). Open circles (○) indicate mild outliers (more than 1.5 times from the upper edge) and asterisk (*) is the extreme outlier (above 3 times from the upper edge).

3.3.1. Monthly fluctuation in local emission in March 2007 Monthly fractions of major sources of the six Northern provinces surrounding Chiangmai, determined based on the emission proxies, are shown in Fig. 5. In the year of 2007, Chiangmai, the second largest city in Thailand, had a significantly higher traffic volume than other five surrounding provinces. The average number of daily vehicles flowing on highways in Chiangmai in 2007 was around 900,000 which were about 3 times higher as compared to other provinces (Department of Highway, 2009). Therefore, the traffic emission would be a main local contributing source to the high baseline air pollution in the city. However, the traffic emission would not expect to have a significant monthly variation. As a way of illustration, the monthly petroleum wholesale was almost constant throughout the year. The severe haze month of March 2007 had only 8% of the petroleum wholesale which suggested that the episodic high PM10 levels would not directly link to an abnormally high traffic emission. The industrial emissions in the area may be strongly seasonal. Most of the industries in Northern Thailand are agro-industries, of which the majorities (over 7300) are food and beverage industries (Thao et al., 2008). The results of the survey for 10 (out of 87) rice mills, 25 (out of 118) dried crop/fruit manufacturers, 4 (out of 60 tobacco curing stations/companies), and 7 (out of 44 brick making industries) in the study area revealed that these industries used

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Fig. 4. Vertical temperature profile of haze-prone pattern (pattern 2) on March 13, 14 and other patterns: pattern 1 on April 29, and pattern 4 on February 1.

various fuels (rice husk, firewood, corncob, and lignite) and could generate high air pollution emissions. Among them, tobacco-curing and brick making industry have higher productivity during the dry season. Higher productivity of these two surveyed industries in the 6 provinces in March 2007 (Fig. 5), especially the tobacco curing industry (38% of the annual total), may contribute to higher ambient PM10 during the month. In addition, the data on the fuel oil (commonly used in industry) wholesale in the six provinces that also shows a higher consumption during February–April in 2007, consisted of 41% of the annual total sale (Ministry of Energy, 2007). Thus, a higher emission intensity of other industries in March 2007 should also be expected. A very important seasonal source of air pollution is the biomass open burning. The 6 Northern provinces are largely covered by forest and agricultural lands. In particular, the Chiangmai province has 78% of the area covered by forest with frequent forest fires during the dry season. The burned forest area, based on the ground record data, for the whole Northern region during 2004–2008 was the highest in 2004 (107 km2) and the second highest in 2007 (73 km2) (MONRE, 2008). The preliminary EI by PCD (2002) for the Chiangmai city alone in 2000, mentioned above, also estimated the highest contribution from

forest fire to SPM emission (624 tonnes or 89%). In this study, we examined the monthly variation of the emission contribution from open burning in the 6 provinces using the MODIS burning pixel data from the Web Fire Mapper which shows the highest value in March 2007 (62%) and the second highest in February (31%). The field burning of agricultural residues, which has become routine among Northern farmers, is another source of biomass open burning emission in the study area. The agro-residue fires are shortlived hence may not be adequately captured by MODIS as hotspots. To better reveal the monthly fluctuation of the field burning source we hereby used the monthly harvested paddy area as an additional indicator for the burned amount of rice straw, a major type of field burned agro-residues in the study area. The harvested paddy area of the second rice was the highest in April (35%) and March (27%) which also coincided with the period of high PM10. In general, agriculture residue field burning may also contribute significantly to high PM10 during the March 2007 haze episode. Overlaying the daily hotspots and land use maps of the Northern Thailand using ArcView GIS for the period of March 9–15, 2007, i.e. the period of the highest numbers of daily hotspots, indicated that 80% of hotspots were detected in the forest areas and 20% were in the agriculture areas (mixed swidden

PM10, µg/m3

Fraction 1.00 0.90 0.80 0.70 0.60 0.50 0.40 0.30 0.20 0.10 0.00 1

2

3

4

5

Traffic (petroleum wholesale) Brick (productivity) Major rice (harvest area) Average PM10, µg/m3

6

7

8

9

10

11

200 150 100 50 0 -50 -100 -150 -200 -250 -300 -350 -400 -450 -500 12

Tobacco (productivity) Biomass burning (burning pixels) Second rice (harvest area)

Fig. 5. Monthly fraction of the total emission from different sources in 2007 based on emission proxy in 6 Northern provinces.

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cultivation, paddy field, and corn plantation). Thus, this source should be properly accounted for in any future effort to develop an EI for the area. Overall, the most remarkable increase of emission in March 2007 was from biomass open burning followed by seasonal industries. The larger pollutant emission from the local sources would be one of the main factors leading to the episodic haze in the month. Further, the regional air pollution transport from the upwind region to the study area should be examined. The analysis of daily hotspot counts, meteorology and air mass back trajectory was made to qualitatively address the regional transport.

to the air pollution levels in a given day. Meteorology patterns represent the synoptic scale conditions that link to local and regional stagnant conditions as well as the air mass pathway (regional transport) which all are important in the formation of haze episodes in Chiangmai. It its noted that the simple tool used in this study (back trajectory and hotspot counts in different areas) would only be able to provide a qualitative estimate of local and regional source contributions to air quality in the study area. Comprehensive 3D chemical transport models would be required for a quantitative analysis.

3.3.2. Daily meteorology, trajectory, emission and PM10 level during March 2007 The synoptic patterns for every day during the period from February to April 2007 (Fig. 6) show that pattern 4 dominated at the beginning of February when the PM10 exceeded the NAAQS for a few days. Patterns 2 and 3 were alternating for the rest of February, but the levels were below the NAAQS. Starting from March 1st, pattern 2 dominated and 24 h PM10 levels were consistently high with a peak on March 13 (396 μg/m3 at Yupparaj and 317 μg/m3 at City Hall stations). In fact, all of the episode days in March 2007 belonged to pattern 2. These days were also characterized by the typical five-day backward trajectories which were originated from the west (Bangladesh, India, Myanmar and a few from Andaman seas) then passed Myanmar and the Mae Hong Son province before arriving at Chiangmai (Fig. 7). High PM10 levels in Chiangmai were observed when the air masses took longer time to move over the Southeast Asia continent before arriving to Chiangmai (March 5, 12 and 13). The slow motion of air masses indicates the stagnant conditions that trapped air masses over the dense hotspot areas enhancing the picking-up and circulation of burning smoke in the region. The air mass trajectories are expected to directly link to synoptic scale wind and hence to synoptic patterns. For example, on March 21– 22 when meteorology changed into pattern 4 the air mass trajectories also drastically changed, i.e. passed through the South China Sea, Vietnam and Northeast of Thailand before arriving at Chiangmai, i.e. avoiding the region of dense hotspots on the west. Coincidentally, PM10 levels sharply dropped. The local wind speed at Chiangmai on these days increased (from 0 in the previous days to 2.5 m/s) which enhanced the pollution dispersion. On the synoptic chart, a ridge was extending to the Northern Thailand that brought in the stable ENE wind direction to Chiangmai. Note that from March 23 onward, PM10 was generally low and reached the lowest level at the end of April when pattern 1 took over (Fig. 6). Although pattern 2 dominated since mid-February, extremely high PM10 levels were only observed in March 2007 that should also be linked to the higher local emission intensity in this month as discussed above. No daily emission intensity during March 2007 was available except for the daily fire hotspots. We used the daily hotspot counts within a circle of 150 km radius centered at the Chiangmai weather station and the rectangle to the west of Chiangmai (shown in Fig. 7), mentioned earlier, to examine influences of local and regional open burning emissions. High numbers of hotspots in both circular and rectangular areas were observed daily during March 1–April 4, 2007 but with large fluctuations. On March 21, the number of hotspots reduced significantly while the synoptic pattern changed from 2 to 4 and PM10 dropped sharply. However, on March 5 and 12, the PM10 levels in Chiangmai were high despite low numbers of hotspots observed. These two days were with clear sky at Chiangmai (cloudiness b 2/10) hence the cloud effects on MODIS fire detection would be minimum. The low hotspot counts would most probably indicate less open burning. The pathway of air masses circulating over the dense hotspot areas on these days (Fig. 7) suggested that the air was stagnant regionally and emission would remain for long in the study area. Thus, emission from previous days would also contribute

The meteorological conditions causing haze episode phenomena in Chiangmai belong to synoptic pattern 2. Therefore, identification of the synoptic pattern of a given day is the first step to develop the warning signal for haze. We propose to use the automated meteorological classification scheme developed in this study for the purpose. The scheme requires input of 18 meteorological variables at 7:00 LST (00:00 UTC) from the regional stations (see detail in Table 1). Available meteorological data online can be obtained from TMD website (http://www.tmd.go.th) and Weather Underground website (http://www.wunderground.com). For a future day, the weather forecast data can be used to identify the pattern. To predict the 24 h PM10 in Chiangmai on a particular day (here called the forecast day, meaning the day when the forecast is being made) a stepwise regression model was developed using the data collected in February–April of 2007–2009. Only days of synoptic pattern 2 were considered which provided 100 days for the statistical treatment. The input data consist of all available meteorological variables at 7:00 LST and the hotspot counts on the forecast day, the previous day and the previous 8 days over different circular areas around Chiangmai, i.e. with radii of 50, 80, 100, 150, 200, 250, 300 km and those in the west rectangle, as well as PM10 in the previous day averaged for two Chiangmai monitoring stations. The meteorological data were obtained from the same sources as for the automatic meteorology classification scheme. The hotspot counts were generated by Terra and Aqua satellites at a detection confidence higher than 50% and were obtained from the Web Firm Mapper website (http:// maps.geog.umd.edu/firms/). Note that due to the lack of the daily emission data of other sources we considered only daily hotspot counts to represent the biomass open burning which was one of the most significant sources during the haze episodes discussed above. The stepwise procedure examined all entered variables and removed any variable with F statistic below a significant level. In this study, an F value of 0.15 was selected as the significant level for retention of a variable. Finally, there were only four independent variables remained as shown in Eq. (2) that demonstrated a maximum coefficient of multiple determination (R2).

3.4. Episode warning and prediction model

½PM10  = −4044:53 + 0:568 P PM10 −0:008 Vis−1:948 Hum + 4:258 SLP

ð2Þ where, [PM10] P_PM10 Vis

is the 24 h PM10 (μg/m3) on the forecast day, averaged for 2 stations in Chiangmai; is 24 h PM10 (μg/m3), averaged for 2 stations in Chiangmai, on the previous day; is the visibility (m), Hum is the relative humidity (%), SLP is the sea level pressure (mb) observed in Chiangmai at 07:00 LST on the forecast day.

It can be observed that the daily hotspot counts, which represent the important biomass burning emission source, were automatically dropped out by the stepwise regression analysis. This was most

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Fig. 6. The major features of the haze period in February–April, 2007. Those days discussed in the text (March 5, 12, and march 21, 2007) are underlined.

probably due to a strong correlation between the hotspot counts and other variables appeared in the model, i.e. Vis and P_PM10, hence the hotspot variables are not explicitly appeared in Eq. (2). A good agreement between the observed and predicted values with R2 of 87% and RMSE (root mean square error) of 22.1 μg/m3 was obtained on the 3 year dataset (2007–2009) used for the model development. To evaluate the model robustness, we tested the model performance on an independent dataset for the February–April months during 2004–2006 and 2010 (4 years), which shows an R2 of 81% and RMSE of 23.3 μg/m3. The model performance for the 7 year period is presented in Fig. 8, highlighting the comparison between the observed and estimated 24 h PM10 for the total of 250 days of pattern 2. The extreme high observed values were under-predicted, which is anticipated as the statistical regression models tend to predict means better than the tails of distribution (Kim Oanh et al., 2005; USEPA, 2003). The model prediction skill was compared to that of the simple persistence regression between 24 h PM10 of the previous day (n − 1) and that of the forecast day (n) performed on the same dataset. The persistence regression (shown in Fig. S4, SI) had lower R2 (76%) and

larger RMSE (26.1 μg/m3) as compared to our model. Thus, incorporation of the meteorology variables in Eq. (2) improved our model over the simple persistence analysis. As the primary focus of this study was to provide the PM10 forecast for Chiangmai when the levels are in the “haze” range, i.e., above 120 μg/m3, the category forecast evaluation would be important. The evaluation was to assess the forecast skill in terms of correctly and incorrectly forecasted concentrations above or below a selected threshold, 120 μg/m3 in our study. We used the verification statistics of the categorical forecasts suggested by USEPA (2003): accuracy (A), bias (B), false alarm rate (FAR), critical success index (CSI) and probability of detection (POD). A graphical representation of the variables (a, b, c and d), shown in Fig. 9, presents the numbers of data points within a quadrant that was used to formulate the categorical statistics. The model was able to achieve an accuracy A = 90.4%, B = 1.02, FAR = 14.3%, CSI = 76.4% and POD = 87.6%. The accuracy percentage was higher than the minimum requirement of 90% for the initial implementation of the national air quality forecasting capacity by US EPA (Davidson et al., 2004). Note that this is almost similar to the accuracy of the category forecast of the next day highest hourly

Fig. 7. The 5-day backward trajectory of air masses arriving at Chiangmai during March, 1–April, 4 2007. Trajectories on 5, 12, 13, 21, and 22 March 2007 are highlighted. The highlighted circle (a radius 150 from Chiangmai) and rectangle (western part of Chiangmai) indicate the areas for which the daily hotspot counts were made and are presented in Fig. 6.

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Fig. 8. Observed vs. predicted 24-h PM10 in Chiangmai for tested dataset of days in pattern 2 during February–April of the year 2004–2010 (model development dataset is indicated).

ozone (exceeding 100 ppb) in the central part of Thailand using CMAQ-MM5 by Nghiem and Kim Oanh (2008). Sensitivity analysis was conducted by estimating the change in predicted PM10 levels when an independent variable was increased while others kept unchanged. The forecasted PM10 would change by +25, −11, −9 and +13 μg/m3 if the P_PM10 (20–360 μg/m3), Vis (100–15,000 m), Hum (50–100%) and SLP (980–1020 mb) increased by 10% of its corresponding practical range indicated in the parentheses. The previous day PM10 level appears to be the most influencing variable for forecasting the next day 24 h PM10 in Chiangmai, which supports the discussion above that the stagnant air condition in pattern 2 would retain high pollutant concentrations in the study area for a long time. A haze warning procedure was proposed, which consists of three steps: (1) identification of synoptic pattern of a given day using the automated meteorological classification scheme, (2) issue a warning signal if it would be pattern 2; and (3) predict the PM10 levels in Chiangmai using Eq. (2). Note that the PM10 in the previous day (P_PM10) can be obtained online from the PCD website (http://www.pcd.go.th/AirQuality/ Regional/DefaultThai.cfm). The P_PM10 is the average of hourly

Fig. 9. The categorical evaluation of PM10 prediction for the days of pattern 2 during February–April 2004–2010. “b” and “c” indicate correct forecasts: the predicted values (P) agreed with the observed value (O). “a” and “d” indicate incorrect forecasts, causing “false alarm” and “misses”, respectively.

measurements over 24 h, from 9:00 am LST on the previous day to 9:00 am LST on the forecast day, and is available online at 9:00 am LST on the forecast day. Thus, using independent meteorological observed data at 7:00 LST and the P_PM10 (available at 9:00 LST) the 24 h PM10 forecast can be made at 9:00 LST on the forecast day, i.e. 24 h in advance to the measurement. In other words, the forecast span for PM10 is 24 h. For identification of synoptic patterns, the meteorology forecast data can be used that can help to issue a warning signal well in advance once pattern 2 would be appeared on a given future day. The haze warning-forecast scheme would provide the “haze alert” in advance so that emission reduction measures can be timely implemented. In particular, the open burning of solid waste and agriculture fields, and prescribed forest fires should be eliminated during the period. For the public, the warning can help to avoid unnecessary exposure to episodic high air pollution, i.e. to restrict outdoor activities, especially for the aged and infirm, and children. 4. Conclusions Meteorology and emission are interrelated and both play important roles in the Chiangmai haze episode formation. The synoptic scale meteorology that governs the northern region of Thailand during the February–April period can be classified into four major patterns. Our findings indicate that pattern 2, typically with weak wind and strong inversion, is most likely associated with haze episodes with the highest 24 h PM10 levels observed in Chiangmai during 2001–2008. Pattern 2 dominated during the severe haze episode in March 2007 and when it was replaced by other patterns the PM10 level dropped sharply. Local emission sources including open biomass burning and seasonal industries were also highest during March 2007 while the traffic emission appeared stable over all months of the year. Back trajectory analysis suggested that in pattern 2 the study area was located downwind of a region of dense fire hotspots and air masses typically circulated over this region before arriving to Chiangmai. Thus, in this pattern the contribution of both local and regional emissions would enhance the episodic haze formation. The prediction model, with a satisfactory performance on both development and test datasets, can be used to estimate the 24 h PM10 level in Chiangmai when pattern 2 prevails. The proposed haze warning-forecast scheme would provide the “haze alert” in advance so that measures can be taken to reduce source emission and the public exposure to high air pollution. Further efforts should focus on monitoring for PM10 composition and PM2.5 mass and composition to provide a better insight into the haze formation as well as the potential health effects. A better haze prediction would require a detail EI prepared for the Northern Thailand with adequate temporal and spatial distributions

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that is suitable for the 3D air quality modeling. In the absence of such EI and other detailed air quality data the approach developed in this study presents a suitable alternative and can be used for the air pollution episode management in similar areas of complex terrain. Acknowledgments The authors would like to thank the Pollution Control Department (PCD), Thailand for the ambient air monitoring data; Thailand Meteorological Department (TMD) for meteorological data; Land Development Department (LDD) for land use data; and all companies and industries for participating in the questionnaire survey. Appreciation is extended to the Web FireMapper, the University Corporation for Atmospheric Research (UCAR), and University of Wyoming for hotspot counts record, weather data for China, and vertical temperature profiles in Chiangmai, respectively. Appendix A. Supplementary data Supplementary data to this article can be found online at doi:10.1016/j.scitotenv.2011.02.022. References Begum BA, Kim E, Jeong C, Lee D, Hopke PK. Evaluation of the potential source contribution function using the 2002 Quebec Forest Fire Episode. Atmos Environ 2005;39(20):3719–24. Berman NS, Boyer DL, Brazel AJ, Brazel SW, Chen R, Fernando HJS, et al. Synoptic classification and physical model experiments to guide field studies in complex terrain. J Appl Meteorol 1995;34(3):719–30. Chiangmai Provincial Public Health Office (CMPHO), Strategy Division. Retrieved from http://www.chiangmaihealth.com/cmpho_web/2008. Davidson PM, Seaman N, Schere K, Wayland RA, Hayes JL, Carey KF. National air quality forecasting capability: first steps toward implementation. Presented at the Sixth Conference on Atmospheric Chemistry: Air Quality in Megacities, Seattle, WA, 2004; 2004. Available online: http://ams.confex.com/ams/pdfpapers/70789.pdf. Davis RE, Kalkstein LS. Using a spatial synoptic climatological classification to assess changes in atmospheric pollution concentrations. Phys Geogr 1990;11(4):320–42. Department of Highway. Available online: http://www.doh.go.th/dohweb/hwyorg52100/ index52100.htm2009. Department of Industrial Work (DIW). Available online: http://www.diw.go.th2009. Duprey RL. Compilation of air pollutant emission factors. . U.S. DHEW, PHS, CP, EHSRaleigh, N.C.: National Air Pollution Control Administration; 1968. Publication Number 999-Ap-42. Fierro M. Particulate matter. Available online: http://www.airinfonow.com/pdf/Particulate_Matter.pdf2000. Grivas G, Chaloulakou A, Kassomenos P. An overview of the PM10 pollution problem, in Metropolitan Area of Athens, Greece. Assessment of controlling factors and potential impact of long range transport. Sci Total Environ 2008;389(1):165–77. Heidorn KC, Yap D. A synoptic climatology for surface ozone concentrations in southern Ontario, 1976–1981. Atmos Environ A 1986;20(4):695–703.

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