Abstract. 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.