Contributions from local emission sources and the potential long-range transport were analyzed using the PM compositions and the diurnal variations in relation to local source activities
Trang 1Effects of local, regional meteorology and emission sources on
mass and compositions of particulate matter in Hanoi
Environmental Engineering and Management, School of Environment, Resource and Development, Asian Institute of Technology, Thailand
h i g h l i g h t s
< Variation in PM mass and composition were analyzed with meteorology and emission
< Reconstructed mass and PMF model were used for source identification and apportionment
a r t i c l e i n f o
Article history:
Received 2 October 2011
Received in revised form
26 April 2012
Accepted 3 May 2012
Keywords:
Fine particulate matter
EC/OC
Diurnal variation
Meteorology
Source apportionment
Hanoi
a b s t r a c t Intensive monitoring for PM mass and composition was conducted during December 2006eFebruary
High 24 h PM levels were observed at low wind speeds when a stagnating ridge governed over Northern Vietnam Diurnal variations of PM mass and composition, analyzed using 4 h-samples, reflected the
diesel traffic (10%), residential/commercial cooking (16%), secondary sulfate rich (16%), aged seasalt mixed (11%), industry/incinerator (6%), and construction/soil (1%) Contributions from local emission sources and the potential long-range transport were analyzed using the PM compositions and the diurnal variations in relation to local source activities, location of local sources, winds and air mass HYSPLIT
Ó 2012 Elsevier Ltd All rights reserved
1 Introduction
Atmospheric particulate matter (PM) is the most notable air
pollution problem in developing Asian cities (Gupta et al., 2006;
Tsai and Chen, 2006;Hopke et al., 2008;Kim Oanh et al., 2006) PM
gets increasing attention because of the adverse effects on human
health, the atmosphere and climate (Bond et al., 2004) The extent
of these effects depends on PM size and compositions Fine parti-cles measured as PM2.5(particles with the aerodynamic diameter
2.5mm) can penetrate the lungs deeper than large particles, hence are more damaging (Pope et al., 2009)
Systematic PM records, however, remain limited in Asian developing countries, especially for PM2.5 Until recently, available national monitoring networks focused more on the total suspended
PM (TSP) but at present PM10 (particles with the aerodynamic diameter 10mm) is also monitored routinely in many countries (HEI, 2010) Available data show high levels of PM in Asian cities that often exceeded the respective national ambient air quality standards (NAAQS) In particular, PM2.5and PM10normally exceed
* Corresponding author.
E-mail address: kimoanh@ait.ac.th (N.T Kim Oanh).
1 Current address: Petrovietnam Overseas Exploration Production Operating
Company, Hanoi, Vietnam.
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Atmospheric Environment 78 (2013) 105e112
Trang 2the 24 h WHO air quality guidelines by a factor of two or more
(Hopke et al., 2008;Kim Oanh et al., 2006)
In Vietnam, in the past only TSP was measured and over a short
sampling period (e.g 1 h) but recently PM10mass is continuously
monitored in some urban areas by a network of automatic stations
Data on PM2.5have been made available through research projects
(Hien et al., 2001, 2002;Kim Oanh et al., 2006) and commonly show
high levels in large cities In Hanoi, the capital city of Vietnam,
higher PM levels are observed during the dry season which can be
explained by high emission strength and limited atmospheric
dispersion (Kim Oanh et al., 2006) Some data on PM2.5mass and
composition have been reported earlier (Hien et al., 2004; Kim
Oanh et al., 2006) but mainly based on 24 h PM sampling This
paper presents the results of more time-resolved PM10and PM2.5
monitoring, with 4 h averaging time (6 samples per day), conducted
at a mixed site in Hanoi during winter In addition to the water
soluble ions and element compositions that were partly reported in
early studies, this paper also provides the EC and OC data The
chemical composition of size-segregated atmospheric particles was
used to quantitatively assess contributions from sources using the
reconstructed mass approach and the Positive Matrix Factorization
(PMF) receptor model (Paatero and Tapper, 1994)
2 Monitoring program design
2.1 Study area
The monitoring was conducted during the period of December
2006eFeburary 2007 in the Hanoi capital city of Vietnam Then the
Hanoi Metropolitan Region (HMR) comprised of 7 inner districts
and 5 suburban districts with a total area of around 921 km2and
the population of 3.3 million (GSO, 2006) Since 2008, the HMR has
been expanded to cover a total area of 3348 km2and the population
of over 6.7 million (GSO, 2009) Hanoi is located in Red River delta,
about 100 km from the East Vietnam Sea, and has a tropical climate
with 4 seasons During winter and early spring, the area is under
the influence of northeast monsoon while during summer the
influence of southeast monsoon is pronounced
The sampling site (20.983N; 105.784E) was located in the
Thuong Dinh industrial zone, Hanoi city (Fig S1, supplementary
information) The PM samplers and meteorology equipment were
placed on the rooftop of a building (15 m above the ground) of the
Hanoi University of Science (HUS) and about 100 m from the
heavily travelled Nguyen Trai road (Fig S1, SI) This road, 60 m wide
with 6 auto-vehicle and 2 non-auto vehicle lanes, is the main
southwestward transport route from the Hanoi city The Thuong
Dinh industrial zone is the largest among those located on the right
bank of the Red River in HMR There are also several large
univer-sities in the area which contribute to the high traffic flow in this
street (Truc and Kim Oanh, 2007) Main air pollution emission
sources in this area include road traffic, residential cooking,
industrial activities and construction activities Surrounding
agri-cultural areas also contribute emission from agroresidue field
burning to the site but this is a seasonal emission source, e.g
intensive rice straw field burning is observed around June and
November each year In peri-urban areas around the site, solid
waste open burning is also intensive when weather is dry
2.2 Sampling and analytical methods
One Andersen dichotomous sampler (dichot) for simultaneous
fine (PM2.5) and coarse (PM10 e2.5) fractions, two MiniVol Samplers
(minivol) for PM2.5and PM10, one Sibata 30 L sampler for PM10, and
one portable weather station (GroWeather Davis) were deployed
The meteorology conditions (temperature, wind speed and
direction) were recorded every 30 min during sampling Before sampling all PM samplers were calibrated to obtain the recom-mendedflow rates (15.03 L min1forfine and 1.67 L min1for coarse fractions by dichot; 5 L min1for Minivols, and 30 L min1 for Sibata 30 L) The entire sampling period was divided into two sub-periods: the routine 24 h sampling, 23 December 2006e7 January 2007, when 15 pairs of 24 h PM10 and PM2.5 samples were collected and the intensive sampling, 12 Januarye11 February
2007, when 92 pairs of 4 h PM samples (6 samples per day) were collected Detail on the sampling equipment and onfilters used is given inTable S1, SI
The QA/QC of the sampling and analysis were following the same procedures described inKim Oanh et al (2009) Thefilter conditioning and pre-weighing (using a microbalance) were done
at the Asian Institute of Technology (AIT) Quartzfilters were pre-fired at 550C for about 6 h to remove any carbonaceous/organic
pollutants After sampling, each quartzfilter was put in a Petri dish and kept in a separate airtight bag The samples were refrigerated at HUS and were transported (in an ice box) to the AIT laboratory in Thailand for subsequent analyses Immediately after collected, each mixed cellulose estersfilter was cut into 2 equal parts One part was kept in a tube containing Milli-Q water and refrigerated while stored in HUS to minimize the loss of volatile ions At AIT the tube content was extracted and analyzed for water soluble ions (Naþ,
NHþ4, Kþ, Mg2þand Ca2þ; Cl, NO3 and SO24) by Ion Chromatog-raphy (IC) The other part was refrigerated in HUS and upon arriving
at AIT was analyzed for elements (20 species) using the Inductively Coupled Plasma-Optical Emission Spectroscopy (ICP-OES) The quartzfilters were first used for mass determination at AIT and then sent to the University of Illinois at Urbana Champaign for EC/OC analysis by a Sunset Analyzer (thermal-optical transmittance method, the NIOSH 5040 protocol) Blank reference filters were weighed before and after real samples on each weighing day to ensure that thefilter mass was unchanged over time under the weighing conditions (US EPA, 1998) At least one trip blank was made for a composition group (EC/OC, ions and elements) in each sampling sub-period The blankfilters (brought to the site but were not exposed during a sampling event) were stored and transported together with the samples On average, the ion levels in the (mixed cellulose) blanks were below 1.5mg perfilter for Kþ, Mg2þand Naþ, and in the range of 3e4mg perfilter for Cl, SO2
4 , NO3, NHþ4 and
Ca2þ The element content in the mixed cellulose blanks was below 0.03mg perfilter for most of the analyzed species, except for Fe, Si and Mg that was 0.2e0.5mg perfilter and Ca of 3mg perfilter OC content in the quartz blanks was 1.4e3.7 mg per cm2 while EC blanks were close to zero The composition results were all blank corrected Note that a denuder was not used hence a certain loss of easily volatile nitrate particles was possible However, the loss during sampling was expected to be minimized due to the moderate ambient air temperatures during the period, 13e20C,
coupled with the low sampling flow rates and relatively short sampling period (4 h)
3 Results and discussion 3.1 Daily PM levels in relation to meteorology The daily (24 h) mass concentrations of PM2.5and PM10during the sampling period were widelyfluctuating (Fig 1) The 24 h PM2.5
levels were averaged at 76 32 mg m3, ranged from 26 to
143mg m3, i.e all measurements were above 24 h WHO guideline
of 25 mg m3 The 24 h PM10 levels during the period were
98 35mg m3(37e165mg m3) which exceeded the 24 h Vietnam NAAQS of 150mg m3in 2 days and exceeding the WHO guideline (50mg m3) in 27 days (87% of the measurements) The monitoring C.D Hai, N.T Kim Oanh / Atmospheric Environment 78 (2013) 105e112
106
Trang 3period (DecembereFebruary) includes the months with the highest
PM levels in Hanoi (Hien et al., 2002) The range obtained byHien
et al (2002)for DecembereFebruary in 1998e1999 was
compa-rable with our results forfine PM (PM2.2w25e150mg m3) but
higher for PM10(w50e350mg m3)
Our results also show that PM2.5constituted the major fraction
of PM10(PM2.5/PM10w0.76 0.08) which emphasizes the
impor-tance of the combustion emission and/or secondary particles The
regression line between PM2.5and PM10(PM2.5¼ 0.9 PM10 15)
had R2¼ 0.92 indicating a strong correlation which further suggests
that the change in PM10was mostly driven by the change in PM2.5
Consistently high PM levels during thefirst 9 sampling days
(Fig 1) were observed with the highest levels during the New Year
holiday period (31st December and 1st January) In principle, less
activities in the industrial zone and universities (hence less traffic
emission) were expected during the holidays but only on Sunday,
31st December a reduction in PM was observed Examining local
and regional meteorology during the sampling period should help
to better understand the daily PM variations
The weather during the monitoring period was generally dry
with some drizzle and foggy days typically observed during winter
in Hanoi The wind speeds (30-min averaged) measured at the site
were light, ranging from calm to about 2 m s1 Only a few
obser-vations recorded speeds of about 3 m s1 with a maximum of
4.8 m s1seen on 7 February 2007 Daily PM mass during the period
appears to inversely proportional to the local wind speed (Fig 1)
Obviously, lower wind speeds resulted in poor dispersion of air
pollution during the New Year holidays that could offset the
impacts of reduced source activities on the PM levels In the
following week (2e7 January 2007), when wind speeds increased,
lower PM levels were observed Daily windroses were examined
and a summary on wind directions together with major features of
regional synoptic meteorology on the sampling days are given in
Table S2, SI On the regional scale, during winter Northern Vietnam
is frequently influenced by a high pressure ridge extending from
anticyclones centered in Siberia and China When such a ridge
reaches Northern Vietnam the sea level pressure increases,
temperature and relative humidity drop, while wind with more
northerly directions and increased speeds are observed This
principal relationship between the weather conditions and the
synoptic patterns was also observed during the sampling days
From 25th December 2006 to 7thJanuary 2007 the prevalent wind
directions were gaining more Northerly components and
eventu-ally to predominant Northerly during 27the29th The pressure was
the highest during 29e31 December 2006 whereas the surface
temperature dropped to below 20C The synoptic chart (0:00 UTC)
shows a strong ridge over Northern Vietnam (Fig S2,a, SI) The
influence of this stagnating ridge (typically with subsidence and radiative inversions) over Northern Vietnam resulted in stagnant air and high PM levels in the initial monitoring days before 2 January 2007 After that, the ridge was weakening and a low pressure system developed over SEA (Fig S2,b) The wind speed in Hanoi increased (daily average of 1.5 m s1) and remained high until 7th January due to a relatively large pressure gradient observed over the area that induced a better dispersion hence lowered PM levels During 12the15th January, Northern Vietnam was again under the influence of a stagnating ridge (Fig S2,c) and the PM levels were high The levels declined from 16th January onward when SEA was under influence of a low pressure system and wind speed increased (Fig S2,d)
3.2 Diurnal variations of PM and major composition components The 4 h sampling of PM10and PM2.5was conducted, starting from 6:00 am each day to yield 6 samples per day during the intensive sampling, 12the20th January and 05the11th February Fig 2presents the average diurnal variations of PM10and PM2.5
Fig 2 Diurnal variations of PM mass, major components in PM 2.5 and wind speed based on 4 h-sampling in JanuaryeFebruary 2007 (25
Fig 1 24 h PM mass concentrations (25C, 1 atm) and daily average wind speed during the monitoring period.
C.D Hai, N.T Kim Oanh / Atmospheric Environment 78 (2013) 105e112 107
Trang 4mass and major ionic components in PM2.5 High PM levels
observed in the morning rush hours (6:00e10:00) and evening
rush hours (18:00e22:00) suggested higher contributions of
primary particles especially the road traffic Note that diurnal
variations were clearly seen for PM2.5while for the coarse fraction
(PM10 e2.5is the gap between PM10and PM2.5inFig 2) there was
only a small reduction in the early morning samples (2:00e06:00)
This is because these two fractions were originated from different
sources/processes The coarse particles are normally emitted
directly from industrial and construction activities, and/or
wind-blown dust while fine particles are either directly emitted from
combustion sources (primary particles) or formed in the
atmo-sphere (secondary particles) (Chow, 1995)
The diurnal variations of EC and OC also followed the variations
of PM2.5mass with peaks in the morning and evening rush hours
(Fig 2) EC is directly released from incomplete combustion (Bond
et al., 2004), mainly infine PM, and can be used as a marker of this
source OC includes both primary and secondary particles but they
were not segregated in our results presented inFig 2 Sulfate and
nitrate, major secondary inorganic PM (fine fraction), did not show
a clear diurnal variation as compared to PM mass and EC/OC (Fig 2)
Chloride (Cl), in fact, was following the PM mass diurnal pattern
but the peaks were quite small
Daily patterns of primary particles were expected to reflect daily
variations in the local emission.Truc and Kim Oanh (2007)reported
a very high traffic density in the same road with the daily flow on
weekdays of 145,000 vehicles (from 7:00 to 19:00) that had high
peaks (mainly motorcycles and cars) during morning and evening
rush hours The fleet contained 96% motorcycles, 3% gasoline
powered vehicles (taxi, cars, etc.) and about 1% diesel powered
vehicles Two major types of diesel powered vehicles, truck and
bus, were the principal source of EC at the monitoring site The bus
hourly density remained quite stable (about 120e180 buses) during
the daytime period of 5:00e19:00, declined afterward (20e30
buses) and virtually no buses were observed after 22:30 The
truck hourly density was w100 vehicles during the daytime
(8:00e17:00) and reduced to about 30 trucks at night but then with
more heavy duty vehicles In addition, it was observed that cooking
activities for meals at homes and in small restaurants in the
surrounding areas were also coincidentally more intensive during
the morning and evening rush hour periods The daily patterns of
PM mass and EC/OC thus appear to follow the daily patterns of
these two major sources in the study area
Other important factors determining the PM diurnal variations
are related to the meteorology (mixing height, wind, etc.) Average
wind speed was the highest (1.8 m s1, Fig 2) around noon
(10:00e14:00), which to some extent may lower PM levels at this
sampling time (in addition to reduced emission strength)
However, the second highest wind speed (1.6 m s1) was observed
during the morning rush hours (6:00e10:00) when maximum PM
and EC/OC were observed suggesting that the enhanced dispersion
by the wind could not offset the increased emission strength In principle, the vertical mixing also changes during a day, better around noon and poorer late at night, especially for winter when radiative inversion frequently exists in Hanoi (Hien et al., 2002) Thus, low levels of PM and EC/OC in the afternoon (14:00e18:00) may be attributed to both the enhanced mixing and reduced emission strength (a shorter averaging period is desirable as the evening rush hours in principle start at around 17:00) Note that PM and EC/OC levels in the samples collected at night and early morning (22:00e02:00 and 2:00e6:00) were still lowest in a day which confirmed the predominant influence of diurnal variations
of the local emission
Sample-wise PM mass and EC/OC variations (Fig S3 and S4, SI) showed two highest EC/OC peaks, both in the Friday evening (18:00e22:00), January 12 (EC ¼ 7.3 mg m3, OC¼ 56 mg m3) and February 9 (EC¼ 8.8mg m3, OC¼ 44mg m3) To examine further the weekdayeweekend fluctuation, the diurnal variations for different days of a week were analyzed (Table S3, SI) The-highest 4 h average EC level (18:00e22:00) was on Friday (5.9 3.7mg m3), followed by Sunday (4.5 2.5mg m3) and the lowest were on other working days (MondayeThursday), 2.4 0.5mg m3 OC levels largely followed the EC patterns High traffic density on Friday and Sunday evenings, related to personal trips from and to Hanoi for the weekend activities, may partly explain these high PM levels Overall, the diurnal patterns of EC and
OC on different days of a week were similar to the PM2.5pattern but generally different from the PM10 e2.5pattern The fact that fine particles contain EC and OC emitted from combustion and that majority of EC and OC in our samples were found in PM2.5explains the similarity in their diurnal patterns However, the number of monitoring days was still small (3 Saturdays, 2 Sundays, 3 Fridays, and 8 other working days) to properly characterize the weekdayeweekend variations Additional data are required for the purpose, including the time-resolved air pollution, meteorology and source activities (i.e traffic counts, cooking pattern) during different days of a week
3.3 Daily PM composition 3.3.1 EC/OC
The 24 h average of EC and OC levels in PM2.5are presented in Fig 3which show the same trend as PM presented inFig 1 Note that EC/OC data were available for all 92 PM2.5samples as compared to only 30 PM10 e2.5samples, accordingly EC/OC were estimated for only
30 PM10samples In PM2.5, the average EC levels during the sampling period were 2.7 1.5mg m3(range of 24 h levels: 1.5e4.9mg m3) and OC were 18.3 11.9 (10e39mg m3) In PM10the EC levels were 3.0 1.7 mg m3 (1.5e5.8 mg m3) and OC levels were 21.5 12.3mg m3(11e43mg m3) Majority of EC (about 89 4%)
0 10 20 30 40 50
17 Jan (Wed) 1
06 Feb (Tue) 07 Feb
C.D Hai, N.T Kim Oanh / Atmospheric Environment 78 (2013) 105e112 108
Trang 5and OC (90 4%) was associated with PM2.5 Overall, EC constituted
only a small fraction of PM2.5mass (3.4 1.1%) while OC share was
significant (24.4 7.3%) PM10 e2.5contained only a low level of EC
(0.5 0.3mg m3) and OC (2.2 1.3mg m3) The ratio between EC
and total carbon (TC¼ EC þ OC) in PM2.5also varied during the
monitoring period with lower EC/TC ratios (0.11e0.12) observed
during 12the16thJanuary and the highest ratios (w0.15e0.2) during
7the9thFebruary (Fig S5, SI) In principle, the diesel exhaust would
have a high ratio (EC/TCw0.7,Kim Oanh et al., 2010) while biomass
open burning would have a lower ratio (w0.15 for rice straw field
burning,Kim Oanh et al., 2011) The EC and OC absolute levels as well
as EC/TC ratios appeared to change with the main wind directions
despite of the overall low wind speeds When northerly winds (from
residential and commercial areas to the site, seeFig S1, SI) were
observed, e.g 12e16 January (Table S2, SI), higher EC and OC levels
were obtained but lower EC/TC ratios The cooking in homes and
restaurants and other related activities in the residential area located
just to the north of the site may be the influencing factor Winds were
southerly and southeasterly during the rest monitoring period that
brought in more fresh traffic emission from the road to the site hence
during this period higher EC/TC ratios were obtained although the
levels of EC and OC were generally lower
3.3.2 Ionic species
The results of 8 water soluble anions are summarized inFig 4for
thefine (PM2.5) and coarse fraction (PM10 e2.5) The highest levels
were found for SO24, NO3, and NHþ4, predominantly in the fine
fractions, indicating significant contributions of secondary
inor-ganic particles Local sources of the gas precursors of these particles
may include the vehicle exhausts (SO2, NOx), residential cooking in
the study area with smoky coal briquettes (SO2), as well agriculture
activities (NOx, NH3) Cland Kþwere also found at higher levels in
thefine fraction as compared to the coarse fraction that may be
linked to biomass burning Naþand Clmay link to seasalt including
fresh table salt from cooking Hanoi is located about 100 km west to
the Bac Bo (Tonkin) Gulf and the S-SE winds may bring in the aged
seasalt particles through the regional transport High levels of Ca2þ
observed in the coarse fraction probably link to construction
activ-ities around the site as well as soil/road dust
The ion balance was constructed for the QA/QC purpose using
the sum of anions and sum of cations in the molar concentration
unit (equivalents) For PM2.5, the regression line between anions
and cations had a slope of 0.97 (more anions or slightly acidic) and
R2¼ 0.79 while that for PM10has a slope was 1.1 (slightly alkaline)
and R2¼ 0.62 (Fig S5, SI)
3.3.3 Element composition
The results of the element analysis are summarized inFig 5
Major elements in PM10 e2.5included Ca (highest, reaching above
2 mg m3), Si, Fe and Al which indicate the contribution from
soil/road dust and construction activities PM2.5 contained
noticeable levels of most detected elements but mostly below 0.5mg m3in the high level group and below 0.2mg m3in the low level group (Fig 5) which indicate a combustion origin Lead (Pb) was quite significant (w0.17 mg m3) and was mainly found in
PM2.5, also suggesting a combustion origin The Pb level was well below the Vietnam 24 h NAAQS (1.5mg m3) and that was expected following the successful phase out of leaded gasoline in the country
in 2001 (ESMAP, 2002)
3.4 Source apportionment of PM 3.4.1 Reconstructed PM mass The reconstructed mass was done using 8 mass groups following the method presented previously (Kim Oanh et al.,
2006) to provide information on major contributing sources These included (K-biomass ¼ K0.6 Fe), crustal (oxides of crustal¼ 1.16 (1.9 Al þ 2.15 Si þ 1.41 Ca þ 1.67 Ti þ 2.09 Fe)), organic matter (OM¼ 1.4 OC), soot (EC), seasalt (2.54 Naþ), NHþ
4,
NO3 and SO24 for secondary organic particles, and trace metals (remaining analyzed elements) as presented inTable 1 The calcu-lated mass in principle should be lower than the measured mass because not all the components were analyzed However, due to uncertainties related to sampling and analytical methods, a few
PM2.5samples (7 samples) had more than 100% mass explained hence they were excluded from the statistics presented inTable 1 For the coarse fraction, all samples had mass explained below 100%
On average, the portion of measured mass explained by the 8 groups was 78 11% for PM2.5 and 61 11% for PM10 e2.5 The
K-biomass was used to identify the presence of biomass burning smoke in the PM and it was not the absolute contribution of the biomass burning It was excluded from the sum of the mass explained to avoid double counting because K was already included
in the “trace elements” group The K-biomass group was more significant in PM2.5 than PM10e2.5 as most of PM emitted from biomass burning is expected to be in thefine fraction (Kim Oanh
et al., 2011)
The crustal group was predominantly found in the coarse frac-tion indicating a significant contribution from soil/road dust and construction activities The seasalt group, estimated based on the content of Naþand Clhence indicating“fresh” seasalt, was small
in both fractions The trace elements (remaining analyzed elements) group was generally found at low levels and more in the fine PM (than the coarse PM fraction) and may be linked to
a combustion origin
3.4.2 Source apportionment of PM2.5by PMF The PMF receptor model (Paatero and Tapper, 1994), used in this study, assumes non-negativity of the factors, both loadings (source contributions) and scores (source profiles) PMF2, applicable for two-dimensional arrays, was applied for the PM2.5source appor-tionment using the composition data of 92 PM2.5samples taken during the intensive sampling sub-period that had complete EC,
OC, ions and element composition data Due to the lack of EC/OC data in a large number of coarse PM samples the PMF analysis was not done for this fraction The measurement uncertainty of each species was estimated following the method presented in Kim Oanh et al (2009)
PMF was run with Fpeakranged from0.6 to þ0.6 and Fpeak¼ 0.5 produced seven source factors with the most explainable source profiles The contributions of these source factors to PM2.5(Fig 6) listed in the reducing order included: 1) secondary mixed (local)
PM (31 15 mg m3), 2) residential/commercial combustion (12 10mg m3), 3) secondary sulfate rich (LRT) (12 11mg m3), 4) aged seasalt mixed (9 7 mg m3), 5) traffic (diesel) (8 5 mg m3), 6) industry/incinerator (4 5 mg m3), and 7)
Fig 4 Water soluble ions concentrations in fine and coarse PM (whiskers represent
one standard deviation).
C.D Hai, N.T Kim Oanh / Atmospheric Environment 78 (2013) 105e112 109
Trang 6construction and soil/road dust (1 1mg m3) The scatter plot
between the scaled calculated mass (x) and measured mass (y)
yields a regression equation y¼ 0.97x þ 2.7 with R2¼ 0.64
The profiles of the identified source factors are presented in
Fig S7, SIand the temporal variations in the contributions are in
Fig S8, SI The residential/commercial had more pronounced
contributions during January 12e16, when the local wind was weak
and had the directions of N-NW, and lower contributions during
the rest of the monitoring period when the wind directions
changed to S-SE A populated residential area was located to the
north of the site (Fig S1, SI), food stall/restaurants and commercial
activities were densely distributed along the road and other small
streets inside the residential area Commercial cooking normally
lasted from 4:00 am to late evening hours with the highest
activ-ities between 18:00e22:00 while residential cooking was normally
more intensive in the evening Various fuel types were used for
cooking in the area including LPG and wood fuel at home, and
commonly smoky coal briquettes in food stalls/restaurants The
coal briquettes used for cooking were made of rejected coal
parti-cles which would produce high sulfur and heavy metal emission
Sawdust or peat mixed in the briquettes, and wood fuel would
contribute high levels of Kþ shown in the source profile Low
combustion temperature of the residential/commercial cooking as
well as solid/yard waste burning (observed in dry weather) is
ex-pected to emit higher OC than EC Accordingly, the residential/
commercial combustion had a source profile with specific markers
including high OC and EC (with the ratio EC/OC< 1.0), relative
abundance of NHþ4, sulfate, nitrate, Naþ, Kþ and Cl, and some
elements (Si, Al, Pb, Cr, Mn) Note that Naþand Clpresent in this
source profile may directly link to table salt used in cooking that
may also explain the diurnal Clvariation pattern discussed (Fig 2)
The traffic (diesel) exhaust profile is characterized by high EC with the ratio EC/OC> 1.0 (Fig S7, SI) which is remarkably different from the residential/commercial source discussed above A rela-tively high abundance of V, Zn, Si and Mg2þin this source might be originated from the fuel additives (Kim Oanh et al., 2010) This source was quite stable during the monitoring period but was more pronounced during February 9e11 when the wind (SE, <1 m s1)
was blowing from the road to the site (Fig S1, SI) Diesel powered buses and trucks running in this street were probably the most important sources of fresh PM2.5with high EC to the site A more clear diurnal contribution pattern was also observed for this source (Fig S8, SI) with low emission in the late night and early morning hours (22:00e6:00) when the public buses stopped operation Note that at night heavy trucks were observed traveling in this road and other side streets in the surrounding area hence the truck contribution may be not strongly directional
The secondary mixed PM (local) was identified by a profile with abundance of sulfate, nitrate, ammonium and high OC but with low
EC (Fig S7, SI) Overall, its contribution was quite stable during the sampling period, but showed some dependence on wind speeds (not on wind directions) This source factor had slightly lower contributions when higher wind speeds were observed, e.g January
17e18 and vice versa (Fig S8andTable S2, SI) The HYSPLIT (Draxler and Hess, 1998) 5-day backward trajectories were constructed, starting at 0:00 UTC and 1000 m above the sampling site for each sampling day (Fig S9, SI), and showed that the variations in this source factor contribution were not strongly associated with the air mass types In the dry weather observed during the sampling period
a long atmospheric life of PM was expected that enhanced the mixing and uniform distribution of the secondary PM in the study area This explained why the contributions of this source factor
Fig 6 Percentage of average source contributions to PM 2.5 at Thuong Dinh, Hanoi (based on 4 h sampling, January 12eFebruary 20, 2007).
0.00 0.05 0.10 0.15 0.20 0.25 0.30
0.0 0.5 1.0 1.5 2.0 2.5 3.0
Fe Zn Si Al Ca Mg Ni Pb Sr Ti V Be Cd Co Cr Cu Li Mn Mo Tl
-3)
-3)
Fine fraction Coarse fraction
Low conc species High conc species
Fig 5 Element compositions of fine fraction and coarse PM (whiskers represent one standard deviation).
Table 1
Summary of reconstructed mass for fine and coarse fraction based on 4 h PM
sampling,mg m3.
Parameters PM 2.5 (n ¼ 85) PM 10e2.5 (n ¼ 85/30) b
Max Min Av std Max Min Av std
K-biomass a 3.2 0.0 0.9 0.8 2.2 0.0 0.1 0.3
Crustal 5.4 0.38 2.2 1.1 13 0.6 4.8 2.5
Soot 4.8 0.79 2.2 0.9 1.6 0.1 0.5 0.5
NHþ4 19 0.02 7.9 4.8 2.8 0.02 0.9 0.7
NO3 17 0.02 6.9 3.8 6.4 0.02 1.7 1.8
SO24 39 7.2 17 8.1 4.8 0.02 1.2 1.3
Seasalt 2.8 0.04 1.2 0.8 8.1 0.04 1.8 1.8
Trace metals 3.1 0.31 0.8 0.5 1.0 0.05 0.3 0.2
Mass explained, % 99 39 78 11 93 36 61 16
a K-Biomass is not included in the calculated mass to avoid double counting.
b
C.D Hai, N.T Kim Oanh / Atmospheric Environment 78 (2013) 105e112 110
Trang 7were more depending on local wind speeds than on air mass types
(with characteristic wind directions) To distinguish this source
factor from the secondary sulfate appeared later it is named the
secondary PM (local) although the precursors of this secondary PM
may have both local and regional/LRT emission origins
The“industry/incinerator” contribution was identified by higher
levels of several elements (Mg2þ, Fe, Pb, Zn, Al, Cr and Mn) in the
source profile This source factor had a relatively small contribution
(6%) and high temporal variations (Fig S7, SI) A few peaks were
observed at late evening and early morning hours and were in fact
higher on Saturdays and Sundays Several industrial activities were
located on the opposite side across the road, but in principle should
not have a high contribution at late hours and on weekend It was
noted that there was a hazardous hospital waste incinerator (stack
height of 20 m) located about 10 km to the NW of the monitoring
site that may explain higher contributions from this source during
12e16 January (NW-N wind predominant) In addition, there is
a waste recycling village (Trieu Khuc,Fig S1, SI) located about 2 km
to the SW of the site which often had waste burning in the late
evening hours Further investigation is still required to explain the
detailed source activities of this factor
The aged seasalt (mixed) was identified by higher levels of Naþ,
Mg2þ, as well as nitrate and sulfate in the profile It contributed
about 11% of PM2.5mass at the site Examining the HYSPLIT 5-day
backward trajectories during the period, confirmed that the aged
seasalt (mixed) contribution was higher during the days when
a long marine pathway (15e16, 18 January and 5e7 February) was
observed (Fig S9, SI) The coastal line of the East Vietnam Sea is
about 100 km away from the site During the transport to the site,
the marine air masses likely have Cldepleted (Lee et al., 1999), i.e
replaced by nitrate and sulfate Likewise, various secondary and
primary air pollutants were also picked up and brought to the site
hence this aged seasalt mixed was not the same as the (fresh)
seasalt calculated by reconstructed mass (Table 1) Overall, this
source factor was likely associated with the regional/LRT air
pollution that had a marine pathway
The secondary sulfate rich PM (LRT), with a distinguished high
abundance of sulfate, contributed about 16% to PM2.5 mass As
compared to the secondary mixed PM (local) this source factor had
a low level of nitrate but more abundance of Kþ, Mg2þand NHþ4 The
temporal variation (Fig S8, SI) showed higher contributions when
relatively strong winds were recorded at the site, i.e during 17e19
January and 11 February The backward trajectories showed that
this source factor had higher contributions when the air masses
arriving at the site with a longer continental pathway, i.e moving
along the coastal line of Southern China on 13th January and 17th
January, or with a continental pathway over Northern Vietnam and
Southern China, or arriving from the west neighboring countries/
territories (9e11 February) This source factor was likely linked to
the LRT of sulfate-rich PM that was originated from continental
territories on the pathway of air masses before arriving to the site
The contribution from soil/construction activities to the site was
identified by a source profile with a high abundance of Ca, Si and Al
(Fig S7, SI) This source contributed only 1% of PM2.5mass at the
site As expected, there was no wind direction dependent and a few
peaks (Fig S8, SI) may be linked to drier weather conditions when
more intensive construction activities and soil/road dust released
3.4.3 Discussion: contributions from local emission sources and
LRT
For the purpose of the air quality management it is always of
interest to estimate the contribution from the local sources and the
LRT air pollution but this is a challenging task, especially when only
monitoring data are available This is because PM locally emitted or
formed in a study area is mixed with those of regional and LRT
origin The precursors are also both locally and regionally emitted
At the monitoring site, the most likely part of PM having association with the regional and LRT origins included the secondary sulfate rich (LRT) and aged seasalt mixed, that collectively contributed about 27% of PM2.5 mass This was estimated using the source profiles, knowledge of local sources and their variations, the vari-ations in the source contributions in relation to local meteorology (wind) and pathways of air masses Nevertheless, it is important to note that not the entire contributions of these two source factors (27%) could be attributed to the LRT because the presence of PM of local origins in the mixture was anticipated
Likewise, the secondary mixed PM (local), contributing about 40% PM2.5mass, may also have a part associated with the LRT origin However, intensive emission from local sources, for example traffic and cooking, may contribute significantly to air pollution, both primary PM and gaseous precursors for secondary PM, in Hanoi As motorcycles have high daily driving activities and scattered over the city (Phuong, 2009), their contributions would also be widely scattered Lack of a source profile of the gasoline powered vehicles prevents from estimation of this source contribution Preliminary results of the emission inventories for Hanoi, using the Interna-tional Vehicle Emissions modeling, show that in 2008 the total motorcyclefleet in Hanoi contributed 2.4 kt PM10(Phuong, 2009) which was about 15% of the PM10 emission estimated from the diesel bus fleet alone in 2010 (Trang, 2011) Thus, the gasoline vehicles may be more important in the contribution of the gaseous precursors for secondary PM formation than directly to the primary
PM in the city An important local source that is observed to dras-tically increase around the city is the rice straw field burning However, the burning is mainly in June and November after the harvest of two rice crops, respectively, which was not covered by our monitoring
Hien et al (2004) conducted the PMF modeling for fine and coarse PM samples collected during 1999e2001 in Hanoi for 3 types of air mass backward trajectories Majority of the backward trajectories in our study period (Fig S9a & b, SI) can be roughly classified into Type 2 (Northeasterly) ofHien et al (2004) The authors identified 6 contributing source factors to fine PM for this air mass type (LRT, local burning, soil dust, local secondary aerosol, marine aerosol and Cl-depleted aerosol) These are largely equiva-lent to our identified source factors except for the traffic (diesel) exhaust emission that was not identified in their study With the available EC/OC data and element composition for the PMF input our study can identify the contribution from traffic (diesel) emis-sion, about 10% of PM2.5mass Also our study revealed that the LRT was likely to contribute a smaller part of PM2.5pollution than the local emission The difference between these two studies was large and may be partly explained by the rapid urbanization and motorization in Hanoi during the 7e8 year gap Note that the average PM2.5data in our study period was 76 32mg m3that was about 2 times of the measured levels for Type 2 (43mg m3) inHien
et al (2004)
4 Conclusions High PM levels were observed at a mixed site in Hanoi during the dry winter period with thefine fraction (PM2.5) contributed the majority (76%) to PM10 The daily PM2.5levels were found to closely link to local wind speeds and directions (relative to local emission sources) and regional synoptic meteorology suggesting that both local sources and LRT contributed to the high PM levels at the site Diurnal variations in PM2.5, EC, OC and EC/TC were similar and can
be largely explained by thefluctuations in local source activities The coarse fraction (PM10-2.5) was less fluctuated during a day Reconstructed mass served as useful tool in identifying potential C.D Hai, N.T Kim Oanh / Atmospheric Environment 78 (2013) 105e112 111
Trang 8source factors for subsequent PMF analysis, although for a site
located 100 km away from the sea like this, the estimated (fresh)
seasalt group may not necessarily relate to the sea spray PMF
identified seven source factors for PM2.5that can be explained using
the knowledge on local emission sources and their variations, wind
and HYSPLIT air mass backward trajectories The major source
factors included the secondary mixed PM (largely with local
origins), local emission (primary PM from cooking, traffic and
industry/incinerator) and LRT (secondary sulfate rich and aged
seasalt mixed) For PM10 e2.5, the reconstructed mass suggested that
soil/road dust and construction activities were the main sources
Overall, the LRT was likely to contribute a smaller part of PM2.5
mass (about 27% have an association with LRT origins) measured at
the site while the majority appears to originate from local
emis-sions The secondary PM (local) contributed a large part of PM mass
(about 40% of PM2.5) hence to improve PM air quality in the city the
control efforts should also focus on the emission of gaseous
precursors Cleaner fuel-cookstove systems in residential/
commercial cooking, cleaner diesel vehicles, as well as emission
reduction for the gasoline vehiclefleet (including the fleet density
reduction) should be addressed Further studies should be
con-ducted for other periods of the year especially during the rice straw
burning months of June and November to reveal the contribution
from this source
Acknowledgment
We would like to acknowledge the AIRPET project (http://www
serd.ait.ac.th/airpet) sponsored by Sida and the ARCP2007-07CMY
project sponsored by the Asia-Pacific Network for Global Change
Research for the partial funding support provided to cover the
consumables for the data collection Dr Hoang Xuan Co at Hanoi
University of Science and Dr Nghiem Trung Dung at Hanoi
University of Technology and their team are specially thanked for
their great assistance extended during the sampling period in
Hanoi Colleagues at UIUC are highly appreciated for the generous
support for EC/OC analysis
Appendix A Supplementary information
Supplementary information related to this article can be found
online atdoi:10.1016/j.atmosenv.2012.05.006
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