This paper presents a study of haze in Singapore
caused by biomass burning in Southeast Asia over the 6-year period from 2010
to 2015, using the Numerical Atmospheric-dispersion Modelling Environment
(NAME), which is a Lagrangian dispersion model. The major contributing source
regions to the haze are identified using forwards and backwards model
simulations of particulate matter.
The coincidence of relatively strong southeast monsoonal winds with increased
biomass burning activities in the Maritime Continent create the main
Singapore haze season from August to October (ASO), which brings particulate
matter from varying source regions to Singapore. Five regions are identified
as the dominating sources of pollution during recent haze seasons: Riau,
Peninsular Malaysia, South Sumatra, and Central and West Kalimantan. In
contrast, off-season haze episodes in Singapore are characterised by unusual
weather conditions, ideal for biomass burning, and contributions dominated by
a single source region (different for each event). The two most recent
off-season haze events in mid-2013 and early 2014 have different source
regions, which differ from the major contributing source regions for the haze
season. These results challenge the current popular assumption that haze in
Singapore is dominated by emissions/burning from only Indonesia. For example,
it is shown that Peninsular Malaysia is a large source for the Maritime
Continent off-season biomass burning impact on Singapore.
The results demonstrate that haze in Singapore varies across year, season,
and location and is influenced by local and regional weather, climate, and
regional burning. Differences in haze concentrations and variation in the
relative contributions from the various source regions are seen between
monitoring stations across Singapore, on a seasonal as well as on an
inter-annual timescale. This study shows that even across small scales, such
as in Singapore, variation in local meteorology can impact concentrations of
particulate matter significantly, and it emphasises the importance of the scale
of modelling both spatially and temporally.
Introduction
Haze caused by biomass burning is a significant issue throughout Southeast
Asia. Biomass burning occurs naturally across the world but is being
accelerated by human activities and interests. Clearing forest for
plantations by burning is a quick and easy way to open up and fertilise the
soil; however, it is also a process that is difficult to control. The
emissions from these fires can have massive and detrimental impacts far from
where the original fires were lit. Biomass burning is a global phenomenon. It
is an ancient practice as well as a natural process which modifies the
Earth's surface . The haze from biomass burning
impacts human health , crops, climate, biodiversity, tourism,
and agricultural production , and also aviation and marine
navigation through visibility degradation
. Over recent decades the impacts of
biomass burning have been felt in increasing degree in Southeast Asia and in
Singapore .
Though haze occurs in Singapore
, it
is not caused by activities within Singapore. Rather it is a transboundary
problem caused by biomass burning across the wider region (see
Fig. for a map of the region), which typically occurs
during distinct burning seasons .
Scientific studies such as , as well as the popular press,
often attribute peatland destruction and related haze in the region to
Indonesia . However, the haze cannot be attributed to
only one region or country alone. To mitigate this, the Association of
Southeast Asian Nations (ASEAN) haze agreement has been formed between the
Southeast Asian nations to reduce haze and mitigate the related impacts using
a scientific approach . Through the ASEAN,
science-based mitigation has been attempted, but many lives are still lost
every year due to haze caused by biomass burning . The Met
Office (MO) and the Meteorological Service Singapore (MSS) have previously
established a haze forecast system to predict haze in Singapore
. This study advances the previous work to improve our
understanding of haze and the underlying causes by analysing and attributing
haze events of the recent past to their sources. The work focuses on
Singapore due to the availability of air quality observations with high
spatial and temporal resolution for recent years.
The weather and climate in Singapore and hence the transport of smoke from
biomass burning are dominated by monsoon periods and influenced by the
variations of the El Niño–Southern Oscillation (ENSO), which modifies
temperatures in the central equatorial pacific
. Meteorologically, the
year in Singapore is split into four seasons, with two monsoon seasons
separated by two inter-monsoon seasons. The northeast monsoon season is
generally from December to early March and dominated by northeasterly winds.
The first inter-monsoon period follows from late March through May, and then the
southwest monsoon is from June through September, with air in Singapore
generally arriving from a southeastern direction. The second inter-monsoon
period is October and November . Between
years, there is large variability in the onset of the monsoon over Mainland
Southeast Asia . Generally, the inter-monsoon periods
are characterised by light and variable winds, influenced by land and sea
breezes with afternoon and early evening thunderstorms .
The later inter-monsoon period is often wetter than the earlier inter-monsoon
period . The weaker winds during the
inter-monsoon periods lead to air arriving in Singapore originating from the
countries immediately west of and surrounding Singapore
(Fig. ).
Previous studies have shown the importance of ENSO in relation to reduction
in convection and precipitation over the Martime Continent (MC) and
corresponding increase in haze in Southeast Asia
. The ENSO conditions have
varied significantly during the 6-year period of our study (2010–2015).
During 2010, the conditions transitioned from a moderate El Niño to a
moderate La Niña lasting through 2011. From 2012 to 2014 the ENSO
conditions were neutral transitioning to very strong El Niño conditions in
2015, which lasted into 2016 .
The combination of variation in ENSO and
anthropogenic land-use changes leads
to considerable inter-annual variation in biomass burning and related
emissions of particulate matter (PM) in Southeast Asia. Biomass burning in
the region can be divided into seasons that relate to the monsoon periods:
February, March, and April (FMA) are dominated by burning in Mainland
Southeast Asia; during May, June, and July (MJJ) burning starts in northern
Sumatra and traverses southward; and August, September, and October (ASO)
are characterised by burning in
Kalimantan and, in general, there is little burning in November, December,
and January (NDJ) . From annual weather reports by MSS
, unusual weather events
from 2010 to 2015 and related haze events are linked. In 2010 a prolonged
Madden–Julian Oscillation (MJO) dry phase caused a dry October, creating
ideal conditions for biomass burning in the region and related haze in
Singapore. Year 2011 began as an ENSO neutral year transitioning to La
Niña, with dry conditions in early September and prevailing low-level winds
bringing PM10 to Singapore from biomass burning in central and southern
Sumatra. During the southwest monsoon of 2012, an MJO dry phase created dry
and ideal haze conditions in September. In June 2013 a typhoon
coincided with major atmospheric emissions from peat
fires in Southeast Asia . In 2014 Singapore experienced
haze during another intense MJO dry phase and drought, described by
. Year 2015 was the joint warmest year (with 1997 and
1998) and second driest year on record. ASO 2015 saw the worst haze in recent
history in Singapore ,
caused by southwesterly/southeasterly winds and fires in southern and central
Sumatra and southern Kalimantan. Fire carbon emissions over maritime
Southeast Asia in 2015 were the largest since 1997 .
Haze concentrations in Singapore vary throughout the 6-year period from
2010 to 2015. Even though biomass burning contributes to (low) PM10
concentrations in Singapore throughout large parts of the year, some peaks in
the PM10 observations can be explained by haze almost exclusively. In
the 6-year period, haze occurs almost annually during the season of ASO,
known as the haze season (see Fig. ). Haze events
occurring during other periods of the year are referred to as off-season or
atypical haze. In 2013 and 2014 two unique atypical haze events occurred in
June and in FMA, respectively .
These events caused extremely high PM10 concentrations in
Singapore.
Several previous studies have looked at attributing air pollution for
different regions. Source attribution can be performed both through modelling
and by looking at observations of air pollution in detail. For example,
carried out a source attribution study of air
pollution in the United Kingdom (UK) using observations to distinguish
between local and regional emissions, whereas estimated
the sources of annual emissions of particulate matter from the UK and the
European Union (EU) by using the Numerical Atmospheric-dispersion Modelling
Environment (NAME) model to look at threshold exceedances and episodes.
Attribution studies have been performed using Eulerian models such as the
Goddard Earth Observing System atmospheric chemistry model (GEOS-Chem), the
Community Multiscale Air Quality Modeling System (CMAQ), and the Weather
Research and Forecasting Model–Sulfur Transport and dEposition Model (WRF-STEM) to study both Asia and the Arctic
sometimes in combination with flight campaigns to better
constrain the emissions. Lagrangian models have also been used in combination
with observations by . Combinations of Eulerian and
Lagrangian models and Eulerian models and
observations have been used to assess whether
low-visibility days were caused by fossil fuel combustion, biomass burning,
or a combination of the two. In Southeast Asia,
used an Eulerian model to study haze and estimated emissions through a
bottom-up approach. Source attribution for studies of biomass-burning-related
degradation of air quality and visibility in Southeast Asia has also been
applied by , who used the WRF model to study the
sensitivity of the results to different met data and emission inventories.
also used observations and a chemical mass balance
receptor model to compare the chemical composition of total suspended
particulate matter on haze and non-haze days during a haze event in 2006.
The aim of this study is to investigate the spatial variation of haze across
Singapore through source attribution. This includes the variation in
concentration and the contributing source regions at different sites across
Singapore. This has been achieved by linking meteorology, biomass burning
emissions, and forwards and backwards dispersion modelling to study how the
origin of haze has varied across Singapore during 2010–2015. Fire radiative
power and injection height from the Copernicus Atmosphere Monitoring Service
(CAMS) Global Fire Assimilation System (GFAS, ) and
higher-resolution land-use data from the Centre for Remote Imaging, Sensing and
Processing at the National University of Singapore have been used to
calculate particulate matter with diameter of 10 µm or less
(PM10) emissions from biomass burning in 29 defined source regions in
Southeast Asia (Fig. ). Using the Met Office's
numerical weather prediction (NWP) model to drive the Numerical
Atmospheric-dispersion Modelling Environment, a Lagrangian particle
trajectory model, we are able to attribute the haze arriving in Singapore to
its source region and study the difference between major contributing source
regions at a western and an eastern monitoring station in Singapore.
The model output is evaluated against PM10 observations from the two monitoring stations.
The paper is composed as follows: Sect. describes the
methods used in the study; Sect. presents the results and
evaluation, along with a more detailed study of four recent haze events. The
results and related implications are discussed in
Sect. .
Methods
This section describes the model used, the setup and input used for the
simulations, and the methods used to evaluate the results.
The Numerical Atmospheric-dispersion Modelling Environment
We use a Lagrangian model because of its ability to track emissions and
provide detailed information on source regions at any given location in the
modelling domain. The Numerical Atmospheric-dispersion Modelling Environment
(NAME) III v6.5 is a Lagrangian particle trajectory
model, designed to forecast dispersion and deposition of particles and gases
on all ranges. NAME uses the topography from the relevant meteorological
input and does not resolve buildings or terrain on scales smaller than the
NWP. Emissions in the model are released as particles that contain
information on one or more species. During the simulation these particles are
exposed to various chemical and physical processes. NAME includes a
comprehensive chemistry scheme, but this is not used in this study, as we are
interested only in primary PM.
The only aerosol processes considered here are dispersion and wet and dry deposition of primary PM10.
In NAME the dry deposition is parametrised using the resistance-based deposition velocity and wet deposition is
based on the depletion equation .
The advection is based on the winds obtained from the meteorology provided,
and a random component is added to represent the effects of atmospheric
turbulence. NAME is driven by meteorological data, in this case the Met
Office's operational weather prediction model ,
described below.
The Unified Model
The Unified Model (UM) is the Met Office's operational numerical weather
forecast model. The UM is a global model based on the non-hydrostatic fully
compressible deep-atmosphere equations of motion solved using a semi-implicit
semi-Lagrangian approach on a regular longitude–latitude grid
. Archived analysis meteorology from the global
version of the UM was used to drive NAME. As the UM is an operational model,
the dynamical core and spatial resolution have changed throughout the period,
from ∼40 km over ∼25 km to ∼17 km resolution. However, for
the majority of the study the resolution is constant at 25 km. These
upgrades are described in , and the
relevant changes for dispersion modelling are summarised in
Table . These changes are not expected to have a
significant impact on the results; e.g. no significant differences in the
deposition are seen across the change from instantaneous precipitation and
cloud to 3 h mean data in 2013.
Global UM model meteorological data for 2013 have been evaluated using
meteorological observations available at four sites across Singapore. The UM
data are interpolated in NAME to obtain wind speed and direction,
temperature, and relative humidity data for each location and an hourly time
resolution. The results show that modelled wind speeds are higher on average
than those observed during 2013, particularly during the monsoon seasons. Wind
speeds are one of the most important factors affecting pollutant levels,
particularly close to strong sources. Although haze in Singapore is
predominantly caused by long-range transport of biomass smoke, the higher
wind speeds in the model may contribute to reducing modelled pollutant levels
below those observed. There are some differences in wind direction between
the model and observations, but the prevailing wind directions are captured
well throughout the year.
Observed ambient temperatures are slightly higher and more variable on
average than the model, although there is good agreement between the model
and observations. Rainfall does not appear well represented with higher
hourly means and more frequent low-intensity events when compared to the
observations, which show less frequent high-intensity rainfall associated
with the convective activity that dominates rainfall within the tropics.
Modelled total monthly rainfall is higher than observed during 2013, which
may decrease modelled PM levels through wet deposition and contribute to the
often negative bias observed in PM10 (see Sect. ). As
discussed in and , the
uncertainties from the meteorological data feed into the dispersion
simulation.
Summary of the changes in the global UM data over the period of this
study relevant to dispersion modelling.
Start dateApprox. horizontalRelevant changeresolution1 January 201040 km20 January 201025 kmHorizontal resolution increase30 April 201325 kmChange from use of instantaneous precipitation and cloud to 3 h mean data15 July 201417 kmHorizontal resolution increaseAir history maps
Air history maps provide a visual indication of where air at a given location
has originated from. This helps to determine the regions that influence the
composition of the air arriving at this location. To construct air history
maps for Singapore, backward (inverse) runs were conducted with NAME, in
addition to the forward simulations with the GFAS biomass burning emissions
(Sect. ). Figure
illustrates the air history map for Singapore for the years 2010 to 2015. For
each day in the 6-year period from 2010 to 2015, a 10 d backrun was
conducted using meteorological input from the UM global model within a domain
of 15.0∘ S–23.0∘ N and 90.0–140.0∘ E
(Fig. ). PM10 was emitted as a tracer from a
receptor site in central Singapore, and model particles were released over
the first 24 h with an emission rate of 1 g s-1. The resulting
concentration values in the 0–2 km layer were output on a 0.1∘×0.1∘ resolution grid and integrated backwards in time for
10 d with a timestep of 10 min. A higher integrated concentration indicates
that more air has passed through a grid cell en route to the receptor site,
compared to a grid cell with a lower concentration. By summing the results
from multiple runs, air history data can be produced for different seasons
and years, as well as the total for the whole period. For each analysis
period, the multiple corresponding 10 d air concentrations were summed for
each grid cell and for the total domain. A percentile value was then
calculated to ascertain the proportion of air influenced by a particular grid
cell vis-à-vis other areas.
Comparison between the inland site and a coastal receptor site showed
insignificant variation, meaning that the central receptor site can be
considered representative for the whole island when averaged over time. The
results of the air history simulations helped inform the decision of domain
size for the forward haze simulations.
NAME forward model simulations
For the attribution, forward NAME runs were conducted using the haze forecast
setup designed by and extending it to year-long haze
simulations. Individual forward simulations
were performed for each of the years from 2010 to 2015 for PM10
for a domain covering 14∘ S–23∘ N and 90–131∘ E
using the GFAS PM10 biomass burning emissions described in
Sect. . Each run was initialised on 1 January and
the simulation ran until 31 December
of the same year. A maximum of 200 million model particles were emitted
during the simulation, and any particles leaving the domain were lost. The
simulations used no boundary conditions and so there was no inflow of
particles from the domain edges. From these simulations, modelled time series
for the two monitoring sites described in Sect. were
produced.
Emissions and source regions
The PM10 emissions used in this study were calculated from the Global
Fire Assimilation System () v1.2 daily gridded fire
radiative power (FRP) and injection height (IH) products, integrated with
high-resolution land-use data and emission factors in an approach aimed at
combining the benefits of the MSS and GFAS v1.2 source approaches described
in . Additionally, the land cover map used has been
updated to the 2015 version by , which now
covers the entire Southeast Asia region, as compared to the earlier 2010
version . The horizontal dimensions of the emissions
were dx= dy=0.1∘, and the material was released at varying
heights based on the GFAS injection height information. Using the Lagrangian
nature of the model, all emissions are tagged with source information to
allow for assessment of contributing source regions and relative
contributions. The choice of the GFAS data set as the basis for the source
calculation was based on the need for daily emissions, as in the operational
setup of , and the good agreement of this with
observations and consistency with the Global Fire Emission Database (GFED)
data set documented previously, e.g. and .
For this study, 29 source regions have been defined to distinguish where the
PM10 from biomass burning originated from (see
Fig. ). The Lagrangian nature of the model enables us
to attribute the PM10 concentrations at specific locations in Singapore
to the individual source regions.
Locations and colour codes used for each of the 29 biomass burning
source region within the domain from 10∘ S to 0∘ N and
from 90 to 130∘ E considered in this study. Singapore is located south of
Peninsular Malaysia and east of Riau. The insert in the bottom left-hand
corner shows the relative location of the two monitoring stations in
Singapore.
Observations and performance metrics
Some 20 air quality observation sites are located across Singapore. Of these,
one eastern and one western station have been chosen to explore transboundary
PM10 concentrations across the main island of Singapore. In this
analysis, the western station, Nanyang Technological University (NTU;
1.34505∘ N, 103.6836∘ E), is located relatively close to
the industrial western part of Singapore. The eastern station, Temasek
Polytechnic (TP; 1.34506∘ N, 103.9304∘ E), is placed next
to TP but is also near open fields and a water reservoir. The location of the
two sites in Singapore can be seen in the insert of
Fig. . The National Environment Agency of Singapore
measures hourly PM10 at these and other sites using beta attenuation
monitoring. In this technique air is drawn through a size-selective inlet
down a vertically mounted heated sample tube to reduce particle bound water
and to decrease the relative humidity of the sample stream to prevent
condensation on the filter tape. The PM10 is drawn onto a glass fibre
filter tape placed between a detector and a 14C beta source. The
beta beam passes upwards through the filter tape and the PM10 layer. The
intensity of the beta beam is attenuated with the increasing mass load on the
tape resulting in a reduced beta intensity measured by the detector. From a
continuously integrated count rate the mass of the PM10 on the filter
tape is calculated.
The following analysis is based on hourly PM10 observations and modelled
time series at the two selected monitoring stations. Annual and seasonal pie
charts showing the percentage contribution from each source region at each
monitoring station have been produced to capture the spatial variation of
biomass burning across the island, e.g.
Figs. c–c. During the period considered,
several haze events occurred in Singapore. To evaluate the model results,
four performance metrics have been calculated. These evaluate the model
performance at the two monitoring stations, for each year and selected
seasons in each of the six years with available observations. The metrics
considered are the Pearson correlation coefficient (R), i.e. the
correlation between the model and observations used to get an indication of
the match between patterns in the modelled and observed time series; the
modified normalised mean bias (MNMB) which assesses the bias of the forecast
and can have values between -2 and +2; the
fractional gross error (FGE) which gives the overall error of the model
prediction and is limited between 0 and +2; and finally a factor of 2 (FAC2), which gives an indication of
the fraction of the model results that fall within a factor of 2 of the
observations . Because the emissions used are at a
daily resolution compared to the hourly observations of PM10, a gap or
mismatch in the timing of peak concentrations between modelled results and
observation time series is possible. Biases between modelled time series and
the observations are expected as some fires will be missed due to the fact
that they are too small for the satellites to register, and the extent and/or
duration of the other fires are over- or underestimated due to cloud cover
.
Results
This section presents the results based on the modelling setup described in
Sect. above. Air history maps show where the air arriving
in Singapore has travelled through, and looking at the emissions provides
information on when and where the largest emissions in the region occur.
Using hourly PM10 observations we evaluate our model output before using
the results to address the research questions posed in Sect. .
Four events are studied in more detail in the final subsections of this
section; these are outlined in Table .
Overview of the four haze events studied in detail below. FMA:
February, March, April; MJJ: May, June, July; ASO: August, September,
October.
Air history map for 2010–2015, showing where air arriving in
Singapore during this period originated from. Each shading shows the relative
contribution of air to the central receptor site in Singapore in percent
integrated over the atmospheric column from 0 to 2 km.
The air history map in Fig. shows that most air
arriving in Singapore has travelled from either northeastern or southeastern
directions, illustrating the two monsoon seasons experienced in Singapore
(see Fig. for air history maps summed over
the period for each of the individual seasons). The northeastern component of
the bifurcation in the wind pattern is representative of the northeast
monsoon in FMA (Fig. a), and the southeastern
fork shows the southeast monsoon period during ASO
(Fig. c). During the six years represented by
the figure, significant variation occurs during the individual years
(Fig. ). In 2010 winds were quite weak and the
air arriving in Singapore mainly came from a northeasterly direction and did
not show the expected fork from the two monsoon seasons
(Fig. a). This means that the air impacting
Singapore that year mainly traversed through countries and regions very near
to or east of Singapore, e.g. the Philippines, Peninsular Malaysia, Riau, and
Riau Islands. The air history map for 2011
(Fig. b) shows a clear bifurcation, with air
arriving from the northeast and southeast, as expected from the two monsoon
seasons. The air arriving in Singapore is therefore likely to have originated
from Vietnam, Cambodia, all areas of Kalimantan, Java, and the island of
Sumatra including Riau. During 2012 the northeasterly wind component was
significantly weaker than average. Also, a small northwesterly component is
visible in the air history map (Fig. c). This
means that air was mainly coming from the expected directions given the
monsoons in the region with a small additional northwesterly component, so
most air arriving in Singapore travelled through Peninsular Malaysia or the
island of Sumatra including Riau. During 2013, the same general pattern as
2012 is seen but with stronger northeasterly and westerly components and a
somewhat weaker southeasterly component when the air history maps show a very
small region of influence for the MJJ season of 2013. The majority of air
arriving in Singapore had travelled only over Peninsular Malaysia or Riau.
During other seasons of this year the air in Singapore arrived from as far
away as Vietnam and the Philippines (Fig. d).
Year 2014 was characterised by strong northeasterly and southeasterly
components, both of which were stronger than those for 2013, and a stronger
southeasterly component compared to 2012
(Fig. e). The air history map for 2015
(Fig. f) shows a strong northeasterly component
and the strongest southeasterly component of all six years; these winds
brought air from Peninsular Malaysia, Riau and Riau Islands, Sumatra,
Kalimantan, Sulawesi, Java, and the Lesser Sunda Islands to Singapore.
Analysis of the annual biomass burning PM10 emissions
(Fig. ) shows that there is a bimodal pattern in the
seasons/months with significant burning and also in the dominant source
regions. This finding is similar to that of ,
though we see differences in the contributing source regions and temporal
distribution. The most significant difference between the six years is in the
magnitude of burning – note the different scales of the vertical axis in
Fig. . Overall, 2015 and 2014 were the years with
the highest and second highest annual (∼6.7×106T and ∼4.2×106T, respectively) and monthly (∼2.7×106T,
October 2015; and ∼1.1×106T, March 2014) emissions. Years 2010 and
2011 saw the lowest annual emissions (∼2×106T), though 2010
saw the third highest emissions when looking at individual months (∼8.5×105T, March). Years 2012 and 2013 saw fairly similar emissions (∼2.5×106T), which supports the fact that emissions are lower during La
Niña and ENSO neutral conditions.
Regional PM10 biomass burning emissions, calculated based on
GFAS fire radiative power and injection height and emission factors described
in Sect. , for each of the six years from 2010 to 2015,
summed over each month. Colours for each source region for all years are
listed below the plots. Note the different scales on the y axis (units:
tonnes emitted per month).
Over the six years, the highest emissions were generally seen during El
Niño years and the drought of 2014. This makes sense as the majority of the
fires are expected to be anthropogenic, and dry weather provides ideal
conditions for initiating and maintaining burning . looked at fire seasons
and saw that there is anti-correlation between the seasonal variation of fire
emissions and that of rainfall, which is likely to be because underground
peatland burning may not be immediately extinguished by precipitation. This
also supports other papers, e.g. , who looked at
fire/smoke seasons during the period 2004–2009 and found burning peaked from
June to October and February to March, with the most burning during
September–October.
Observations of PM10 in Singapore from 2010 to 2015 show an overall
background concentration during months of little or no burning of between
23 and 29 µgm-3 at the two monitoring stations. These values fit
well with those determined in other studies for Singapore. For example,
estimated background concentrations for PM10 to
be around 30 µgm-3, based on the 2013 haze episode.
In general, both background and peak concentrations vary between NTU and TP.
Following the approach of we assume a constant background
of 25 µgm-3 for the PM10 observations at both sites and
subtract this value from the observation time series.
Subtracting a constant background from the observations does not give the
exact contribution of PM10 from biomass burning alone because it does
not remove all contributions from all other sources. However, it does give an
indication of the periods with increased PM10 concentrations due to
biomass burning. This is not an attempt to perform an attribution of the
observed PM10 concentrations in Singapore, as the observations, even
with the subtracted background concentration, still include contributions
from sources other than biomass burning. However, the observations minus the
constant background compared to the modelled time series provides an
indication of the performance of the model and through that the quality of
the input used for the modelling. Using the modelled time series and the
related source region information we are able to attribute the PM10
contribution in Singapore originating from biomass burning in Southeast Asia
to the respective source regions.
Because we are intentionally leaving out sources of PM10 other than
biomass burning and there is uncertainty in the biomass burning emissions, we
cannot expect perfect scores from the valuation metrics presented in
Tables and . In the
present study a significant haze event has been defined as any period lasting
more than 1 week with modelled hourly PM10 concentrations from biomass
burning reaching 50 µgm-3 or above at least at one of the two
monitoring stations. Concentrations below 10 µgm-3 are
considered negligible in terms of haze events.
For years like 2013, which was dominated by one extreme haze event, the
correlation between the modelled time series and the observations is very
high (0.79 and 0.80 at NTU and TP, respectively; see
Table ), whereas the correlations for 2010, 2011, and
2012 are very low, which is likely to be due to the low biomass burning
PM10 emissions and few haze events. In general it can be seen from the
MNMB that the model under-predicts, even when taking a constant background
value of 25 µgm-3 into account. This makes sense as the
background in reality cannot be assumed to be constant. We know that we are
not capturing all fires, which will lead to a negative bias, and there are
further uncertainties in emissions, as well as the NWP and dispersion models. It
should be expected that not all model results fall within a factor of 2 of
the observations, and it is not surprising that the fractional gross error is
around 40 %. It is worth noticing that the FAC2 for all years is high
(between 0.76 and 0.87), and in general the FAC2 values for the individual
events are also very good. When comparing the scores to other studies such as
(R=0.91–0.95, FAC2 =0.24–0.89) and
(R=-0.33–0.92), it is important to keep in mind that
even though the scores presented in Tables and
are relatively lower (specifically R) our
statistics are calculated for a 3-month period and other studies are for
shorter periods focused only on air quality and haze days. Also, for the
results presented here the FAC2 values are mostly better than those of
and . In the results below, the estimated
background value of 25 µgm-3 has been subtracted from all
observations. The time series and pie charts are based on results from the
forward NAME simulations.
Statistics for PM10, for both the western (NTU) and eastern
(TP) monitoring stations and all years. The background concentration of
25 µgm-3 is subtracted from the observations for all stations
for all years. The metrics considered are the Pearson correlation coefficient
(R), the modified normalised mean bias (MNMB), the fractional gross error
(FGE), and a factor of 2 (FAC2).
Statistics for PM10, for both the western (NTU) and eastern
(TP) monitoring stations, for selected 3-month haze seasons. The background
concentration of 25 µgm-3 is subtracted from the observations
for all stations for all seasons. The metrics considered are the Pearson
correlation coefficient (R), the modified normalised mean bias (MNMB), the
fractional gross error (FGE), and a factor of 2 (FAC2).
Looking at PM10 concentrations at the two monitoring sites based on the
forward simulations (Fig. ), five years (all but
2013) have haze during ASO and three years (2011, 2013, and 2014) have some
haze in FMA. Year 2013 is the only year with significant haze in June, although
the years from 2012 to 2015 all experience some additional PM10 from
biomass burning in June. When comparing concentrations between the two
stations it can be seen that the concentrations are higher at the western
monitoring station (NTU) most of the time. Exceptions to this occurred during
March 2011 and 2014. Of the haze events that occurred from 2010 through 2015,
some were insignificant (e.g. during FMA 2010, 2012, 2013, and 2015, and MJJ
2012 and 2014), i.e. lasting less than a week and with biomass burning
PM10 concentrations below 50 µgm-3. Some were
significant but showed very little variation between monitoring stations (ASO
2010, MJJ 2013, FMA 2011 and 2014) (Sect. ). The remaining
four events (ASO 2011, 2012, 2014, and 2015) (Sect. ) were
significant events with variation in the main contributing source regions at
the two monitoring stations. Common for all four events is that they occurred
during the haze season in ASO during the southeast monsoon, when the winds
are the strongest for the region and the air history maps show the largest
region of influence for air arriving in Singapore.
Modelled PM10 time series (red line) with observations (black
line) at each of the two monitoring stations west (NTU, left) and east (TP,
right) for the six years with observations available, 2010 (top row) to 2015
(bottom row). A constant background concentration of 25 µgm-3
has been subtracted from the observations and any resulting negative values
have been removed.
Not all peaks in the observations coincide with biomass burning due to real
PM levels also containing anthropogenic and other biogenic species. However,
most peaks in the modelled time series coincide with peaks in observations
indicating that the highest PM10 concentrations are due to biomass
burning.
Atypical haze
During the six years, the most notable atypical haze events occurred in June
2013 and in February, March, and April 2014. Though 2013 was generally a year with
weak winds and average burning, the month of June was very unique, both in
terms of meteorology and burning (Fig. ). The June 2013 haze
event was caused by a typhoon coinciding with intense burning in Riau
(Fig. ). The air history map for MJJ in
Fig. shows that, during this weather event, there was a
small source region with air arriving in Singapore from Peninsular Malaysia,
Riau Islands, and Sumatra including Riau. This is the only year of this 6-year period with significant burning in June, though in general the annual
emissions are neither especially high nor low. In June about 98 % of the
modelled PM10 emissions reaching the two monitoring stations in
Singapore were from Riau. Although the peak concentrations observed at NTU
were lower than those of the modelled time series, overall the concentrations
are fairly similar during the event.
This figure shows results for PM10 for MJJ 2013: pie charts for
the western (NTU) (a) and eastern (TP) (b) monitoring
stations showing major contributing source regions, (c) the regional
map highlighting only the major contributing source region, and (d)
the air history map showing where the air arriving in Singapore originated
from in MJJ 2013. The “Other” category in the pie charts is from sources
which individually contribute less than 1 %.
In early 2014, a drought coincided with air arriving in Singapore from a
northeasterly direction and intense burning in the whole region giving the
second highest emissions of the 6-year period. This resulted in unexpected
haze in Singapore in FMA (Fig. ). The months with the
largest emissions were March and February, which were dominated by emissions
from Riau, Laos, Myanmar, Thailand, Cambodia, Peninsular Malaysia, and West
Kalimantan (Fig. d). In general the region of
influence for 2014 covered an area reaching far to the northeast and slightly
southeast of Singapore and was much larger than for MJJ 2013
(Fig. ). During FMA the winds brought air from Peninsular
Malaysia, Riau, Riau Islands, and the Philippines to Singapore. In spite of
the larger emissions from Riau, Laos, Myanmar, Thailand, and Cambodia, the
mainly northerly wind direction resulted in the haze in Singapore being
caused mainly by emissions from Peninsula Malaysia. The event lasted for
about 3 months total and was dominated by emissions from Peninsular
Malaysia, which contributed over 90 % of the haze at both monitoring
stations, with smaller contributions from Riau, Cambodia, Vietnam, and Riau
Islands.
This figure shows results for PM10 for FMA 2014: pie charts for
the western (NTU) (a) and eastern (TP) (b) monitoring
stations showing major contributing source regions, (c) the
regional map highlighting only the major contributing source region, and (d)
the air history map showing where the air arriving in Singapore
originated from in FMA 2014. The “Other” category in the pie charts is from
sources which individually contribute less than 1 %.
Common for these two atypical haze events is little variation in the source
regions across the monitoring stations, most likely due to the atypical and
different meteorological conditions and the clear dominance of one source
region.
ASO – southeast monsoon season haze
As mentioned previously, the southeast monsoon season occurs during ASO and
coincides with almost annual haze episodes. The two most recent episodes with
highest concentrations were in 2014 and 2015. In addition to the haze event
in FMA 2014 discussed above, another haze event occurred in 2014 during ASO
(Fig. ). This season saw the largest southeasterly region of
influence for air arriving in Singapore during the 6-year period, with air
and PM10 from biomass burning pollution arriving in Singapore from
Peninsular Malaysia, Riau, Riau Islands, Kalimantan, Java, and the Lesser
Sunda Islands during a period of average biomass burning emissions. During
the 2 months of September and October the major contributing source regions
to PM10 concentrations in Singapore were Central Kalimantan, South
Sumatra, and West Kalimantan (Fig. e). ASO is the
expected haze season; however, this is also one of the seasons with the
highest number of significant contributing source regions: South Sumatra,
Central Kalimantan, West Kalimantan, Bangka–Belitung, Riau, Riau Islands, and
the Lesser Sunda Islands (up to 2000 km from Singapore). In spite of the
large annual variation (Fig. ) in the major contributing
source regions between the two monitoring stations, the difference between
the relative contributions at the two stations for ASO 2014 is insignificant.
This figure shows results for PM10 for ASO 2014: pie charts for
the western (NTU) (a) and eastern (TP) (b) monitoring
stations showing major contributing source regions, (c) the
regional map highlighting only the major contributing source regions, and (d) the
air history map showing where the air arriving in Singapore
originated from in ASO 2014. The “Other” category in the pie charts is from
sources which individually contribute less than 1 %.
The results for ASO 2015 (Fig. ) show a large, though
seasonally “normal”, region of influence, which coincided with extreme
emissions. In ASO the southeasterly monsoon winds brought air from Peninsular
Malaysia, Riau Islands, Sumatra including Riau, Kalimantan, Sulawesi, Java,
and the Lesser Sunda Islands. During this season the largest contributing
regions were Central Kalimantan, South Sumatra, and West Kalimantan. The
event lasted approximately 2.5 months in ASO 2015, during which the biggest
variation between the two monitoring stations was seen both for 2015 and for
any season with significant burning. The most significant source regions at
the western and eastern monitoring stations (NTU, TP) were South Sumatra
(38.22 %, 21.82 %), Central Kalimantan (31.19 %, 41.45 %),
Bangka–Belitung (11.32 %, 13.64 %), West Kalimantan (6.64 %, 9.41 %),
and Jambi (6.53 %, 5.98 %).
This figure shows results for PM10 for ASO 2015: pie charts for
the western (NTU) (a) and eastern (TP) (b) monitoring
stations showing major contributing source regions, (c) the
regional map highlighting only the major contributing source regions, and (d) the
air history map showing where the air arriving in Singapore
originated from in ASO 2015. The “Other” category in the pie charts is from
sources which individually contribute less than 1 %.
Common for both ASO 2014 and ASO 2015 are the relatively large regions
influencing PM10 concentrations in Singapore and the variation in major
contributing source regions at the two monitoring stations. This is also the
case for other years with burning and related haze during this season (e.g.
2011 and 2012).
In addition to the four events discussed in detail above, events also
occurred during the expected haze seasons in ASO 2010, 2011, and 2012, as
well as during FMA 2011. The ASO event in 2010 was, except for significantly
lower magnitude, fairly similar to the MJJ event of 2013, with an unusually
small source region for the season and at least 90 % of PM10
concentrations arriving at both monitoring stations in Singapore originating
from Riau. The other two ASO events, in 2011 and 2012, were fairly similar to
the events of 2014 and 2015 with contributions from the expected southeast
monsoon region, a high number of contributing source regions at the two
monitoring stations, and variations in major contributing source region
between the two stations. The remaining event of the period was during FMA
2011, with Riau, Peninsular Malaysia, and Cambodia as major contributing
source regions. Of the seasons with the most significant haze events (e.g.
MJJ 2013, FMA 2014, ASO 2014, and ASO 2015) in Singapore, the air history
maps show that the region of influence for Singapore generally covers the
largest area during ASO when air is coming from southeasterly directions. Of
the four years (2011, 2012, 2014, 2015) with haze events during ASO, 2014 saw
the largest region of influence. Of the two years with events during FMA
(2011 and 2014) the winds were generally from a northeasterly direction and
2014 was, again, the year influenced by the largest source region. For
seasons with southeasterly winds, but not during ASO, e.g. 2012 MJJ, the
region of influence is relatively small compared to that of ASO. Our results,
presented in Fig. , confirm the findings of other
studies such as , who determined the source region for
Singapore to be mainly Sumatra and Borneo (i.e. Kalimantan, Sarawak, Sabah,
and Brunei). also saw that the biggest emitters
include South Sumatra and South Kalimantan, showing that spring emissions
mainly originate from Cambodia, Laos, Myanmar, Thailand, Vietnam, and on
occasion Peninsular Malaysia, whereas autumn burning is seen in Central
Kalimantan, Jambi, South Sumatra, West Kalimantan, and to a lesser extent
Aceh and East Kalimantan. Emissions from Riau vary significantly throughout
the years and individual months, though there are emissions from Riau in most
months during most years, which is consistent with the emissions shown in
Fig. .
Conclusions
In this study we have used the atmospheric dispersion model, NAME, to
attribute PM10 concentrations in Singapore caused by biomass burning to
their source region. In order to gain a deeper understanding of the causes of
haze in Singapore we have compared air history maps, showing where air
arriving in Singapore originates from, with modelled and observed PM10
concentrations at two monitoring stations located at a western and an eastern
location, respectively. For those two monitoring stations we have also
compared the difference between relative contributions from all of the source
regions.
The yearly and seasonal variations in emissions of PM10 from biomass
burning from the region are not always correlated with PM10
concentrations in Singapore. Yet the modelled results confirm that the
highest PM10 concentrations in Singapore coincide with haze caused by
biomass burning. The results show that haze in Singapore is impacted by
(1) burning emissions under human influence (e.g.
Fig. ); (2) the weather through the monsoon and
related winds (Fig. ); and (3) climate,
especially the variations in ENSO, which is also in line with the findings by
. In previous similar studies it has been
assumed that the same emission inventory can be used for different years
, and some attribution studies even
used the same meteorology when studying different years .
Our findings demonstrate that this is not sensible for biomass burning due to
the inter-annual variability of both meteorology and emissions, which can be
extremely high both spatially and temporally .
For the four haze events focused on here, there is variability in the
correlation between the modelled and observed time series, with the best
correlations seen for haze events where the emission sources are close to
Singapore. As discussed by , uncertainty in these
results originates from the emissions and the meteorology. For the former,
the uncertainties result from the fact that the emissions used here are based
on one daily snapshot of FRP and IH, and though some attempts are made to
resolve issues with missing fire emissions caused by the lack of transparency
of clouds the data will naturally be incomplete. At the same time, hourly
emissions are calculated based on this one daily snapshot adding a temporal
resolution that the data do not provide, which also means that peak
concentrations will not always be captured in the model simulations. The
meteorology provides another significant source of uncertainty, as is usually
the case in atmospheric modelling. When considering the resolution of the
analysis meteorology used here and the size of Singapore, it is clear that
there will be unresolved features in both topography and in the meteorology
and hence in the dispersion modelling. However, the differences we see
between the two sites show that we are starting to capture this scale.
Uncertainties in the NWP data such as elevated wind speeds and too-frequent
and too-low-intensity precipitation will disperse the pollutants further and
wash out more than should be, resulting in lower modelled concentrations.
These uncertainties naturally have a larger impact over longer travel
distances, which is reflected in our statistics. It should also be kept in
mind that the observations are measuring all PM10 and we are only
modelling primary PM10 emissions from biomass burning. Other sources of
PM10 include sea salt, dust, secondary organic aerosol, emissions from
industry, local and transboundary road traffic, and domestic heating,
not all of which are constant throughout the year. Some of the varying
difference between observed and modelled time series is likely to be due to
these many other sources of PM10 in Singapore. However, in spite of
these uncertainties our results show that we are able to model dispersion of
particulate matter from biomass burning in Southeast Asia and the resulting
haze in Singapore with reasonable confidence.
Emissions from many regions contribute to the concentrations of PM10 in
Singapore. The biggest contributors for the period 2010–2015 are Riau,
Peninsular Malaysia, and South Sumatra, with smaller yet significant
contributions from Jambi, Cambodia, Bangka–Belitung, Riau Islands, Central
Kalimantan, and the Philippines. As Riau and Peninsular Malaysia are the
nearest neighbours to Singapore and given the local wind pattern this could
be expected. Looking at emissions during ASO for the four years with the most
variation across the island (2011, 2012, 2014, and 2015), the largest
emissions were seen from Central Kalimantan, South Sumatra, Jambi, and also
West Kalimantan. For events during FMA, Cambodia, East Kalimantan, Myanmar,
Thailand, and Vietnam showed larger emissions.
We investigated the spatial variation of haze across Singapore and found that
variation in major contributing source regions across Singapore is dependent
on distance to source regions: generally a shorter distance to the source
region will mean less variation in the major contributing source region(s).
We also studied the seasonal variation by looking at four recent events
occurring during different seasons and saw that air arriving from a larger
geographical area often brings more variation in major contributing source
regions. PM10 concentrations at the two monitoring stations vary
significantly in time, both in the observed and modelled time series; from
the modelled data it is possible to attribute the major contributing source
regions. These show that for the two haze events not occurring during the ASO
haze season, the sources are dominated by the same source region at both
sites, though a different site for the two events. For the two ASO haze
events the major contributing source regions at the two monitoring sites are
mainly the same, but their relative contribution differ significantly. These
variations are also correlated with the distance to the source regions and
the season of the haze events.
The NAME model is able to provide insight into variations in major
contributing source regions at a relatively smaller scale than has been done
in previous studies due to its tracking capabilities and the Lagrangian
nature of the model. Although the results struggle to capture the magnitude
of the haze from burning farther from Singapore, due to errors and
uncertainties in the GFAS data and the meteorological input, they show the
potential for gaining a better understanding by using higher spatial
resolution. This work is a first step towards high-resolution air quality
forecasting for Singapore. Whilst a chemical transport model would be
expected to fully capture anthropogenic and secondary particulate
contributions, the inability of this study to capture the magnitude of the
biomass burning concentrations shows that there is a bigger issue with
emissions and potentially also modelled meteorology. Prior to investing in a
full chemical transport model it is important to understand these individual
components in the simulation. This work contributes towards a better
understanding of the biomass burning and air quality in the region and shows
that biomass burning emissions from many different source regions across
Southeast Asia can reach Singapore. Accurately capturing these is essential
for future air quality modelling.
In conclusion, we saw that haze events occur during seasons with both small
and large regions of influence, however, most often during ASO, coinciding
with a larger region of influence and often when higher emissions/increased
burning occurs, resulting in variation in relative contributions from major
contributing source regions across Singapore. The results emphasise the
inter-annual variation between haze events and major contributing source
regions and show that Peninsular Malaysia is a dominant source of
particulate matter from biomass burning for the maritime continent off-season
burning impact on Singapore (Fig. ). For haze to occur in
Singapore, burning is required, but so is dry weather and wind in the
“right” direction. Haze comes from burning across Southeast Asia, making it
a transboundary issue for the whole region. Considering that the distance
from, for example, Kalimantan to Singapore is over 500 km, this study emphasises
the long-range nature of the problem.
As an extension of the current study it would be interesting to gain insight
into the seasonality and the relative magnitude of PM10 from other
contributors such as industry, traffic, and domestic heating in Singapore.
Further, as it is known that biomass burning varies on sub-daily timescales
, and this study has used daily GFAS FRP and IH
for source calculation, in the future it would be interesting to
study the impact of sources based on higher-than-daily resolution. One could
also use post-fire inventories based on burnt area or conduct an inversion
study, running NAME backwards from detection sites to estimate the emissions
in certain areas corresponding to concentrations observed in Singapore and
other locations in Southeast Asia. These results could also be compared to
inventories based on satellite observations to help quantify how much burning
is missing in such inventories.
Code and data availability
The NAME model and data are available by request to the
Met Office; GFAS data are available through the Copernicus Atmospheric Monitoring
Service (CAMS).
Air history maps for each of the four seasons (a) FMA,
(b) MJJ, (c) ASO, and (d) NDJ, averaged over the
years 2010 to 2015, showing where air arriving in Singapore during each
season originated from. The backruns shown were conducted from a receptor
site in central Singapore.
Air history maps for the years 2010 to 2015, showing where air
arriving in Singapore during each year originated from. The backruns shown
were conducted from a receptor site in central Singapore.
Attribution results for PM10 for 2014: major contributing
source regions for the western (NTU) (a) and eastern
(TP) (b) monitoring stations.
Attribution results for PM10 for FMA for years 2010–2013 and
2015: major contributing source regions for the western (NTU) (left) and
eastern (TP) (right) monitoring stations. (For 2014 FMA; see
Fig. .)
Author contributions
ABH performed most of the attribution model simulations, the data analysis,
and wrote the paper in collaboration with CW.
WMC performed the simulations for and the visualisation of the air history
maps. EK performed additional attribution model simulations
and assisted with visualisation and calculation of error metrics. BNC, CG, MCH,
and SYL helped design the model setup and provided feedback on the paper.
Competing interests
The authors declare that they have no conflict of
interest.
Acknowledgements
We would like to acknowledge the National Environment Agency, Singapore, for
supplying us with PM10 observations in this study. We are thankful for
the support from the CAMS GFAS developers in using the GFAS v1.2 emissions
data. We would like to thank the Centre for Remote Imaging, Sensing and
Processing (CRISP) at the National University of Singapore for providing the
250 m resolution 2015 land cover map for Southeast Asia. We are grateful to
the reviewers and co-editor for their valuable and challenging comments,
which have significantly improved this paper.
Review statement
This paper was edited by Ari Laaksonen and reviewed by two
anonymous referees.
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