Why so hazy in singapore 2017

Get the app. Singapore experiences varying levels of air pollution throughout the year, and Singapore air pollution has been shown to pose a significant risk to public health. Studies have shown that there is a link between high Pollution Standards Index PSI levels and an increased short-term risk to mortality. The NEA provide hour PSI readings with a rolling average for the past 24 hours to help residents manage their own exposure to air pollution.

Among global capital cities in , Singapore ranked as more polluted than Taipei However, this is not the source of the infamous and intermittent haze that has at times engulfed the island, making international headlines. The island of Singapore is surrounded by other island states, including Malaysia and Indonesia, which have large areas of rainforest. In Indonesia in particular, large areas of the forest are often cleared for agriculture, usually by using slash-and-burn practices.

Vast swathes of rainforest can be cleared in this fashion — the World Bank reported that 2. Transboundary smoke has caused a haze of varying severities in Singapore in , , and Poor visibility in Singapore is not always due to transboundary smoke and can often be due to high volumes of water vapour in the air. In Singapore adopted more stringent measures on petrol vehicle emissions by adopting the Euro VI standards, set by the European Union previously they had only adhered to Euro IV standards.

A haze of the magnitude of has not yet repeated itself. Lee Min Kok. Share gift link below with your friends and family. Link Copied! Copy gift link. Sign up or log in to read this article in full. Sign up. The remaining four events ASO , , , and Sect. 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.

Figure 4 Modelled PM 10 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, top row to bottom row.

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 PM 10 concentrations are due to biomass burning. During the six years, the most notable atypical haze events occurred in June and in February, March, and April Though 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 haze event was caused by a typhoon coinciding with intense burning in Riau Fig. The air history map for MJJ in Fig. 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.

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.

Figure 5 This figure shows results for PM 10 for MJJ 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 In early , 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.

In general the region of influence for covered an area reaching far to the northeast and slightly southeast of Singapore and was much larger than for MJJ Fig. 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. Figure 6 This figure shows results for PM 10 for FMA 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 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.

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 and This season saw the largest southeasterly region of influence for air arriving in Singapore during the 6-year period, with air and PM 10 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.

In spite of the large annual variation Fig. A3 in the major contributing source regions between the two monitoring stations, the difference between the relative contributions at the two stations for ASO is insignificant. Figure 7 This figure shows results for PM 10 for ASO 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 The results for ASO Fig.

The event lasted approximately 2. Figure 8 This figure shows results for PM 10 for ASO 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 Common for both ASO and ASO are the relatively large regions influencing PM 10 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. In addition to the four events discussed in detail above, events also occurred during the expected haze seasons in ASO , , and , as well as during FMA The other two ASO events, in and , were fairly similar to the events of and 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 , with Riau, Peninsular Malaysia, and Cambodia as major contributing source regions.

Of the seasons with the most significant haze events e. Of the four years , , , with haze events during ASO, saw the largest region of influence.

Of the two years with events during FMA and the winds were generally from a northeasterly direction and was, again, the year influenced by the largest source region. For seasons with southeasterly winds, but not during ASO, e. Our results, presented in Fig. Kalimantan, Sarawak, Sabah, and Brunei. 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.

In this study we have used the atmospheric dispersion model, NAME, to attribute PM 10 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 PM 10 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 PM 10 from biomass burning from the region are not always correlated with PM 10 concentrations in Singapore. Yet the modelled results confirm that the highest PM 10 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.

A2 ; and 3 climate, especially the variations in ENSO, which is also in line with the findings by Reid et al. In previous similar studies it has been assumed that the same emission inventory can be used for different years Kulkarni et al.

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 Kelly et al. 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 Hertwig et al. 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 PM 10 and we are only modelling primary PM 10 emissions from biomass burning.

Other sources of PM 10 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 PM 10 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 PM 10 in Singapore. 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 , , , and , the largest emissions were seen from Central Kalimantan, South Sumatra, Jambi, and also West Kalimantan.

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.

PM 10 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. 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. Haze comes from burning across Southeast Asia, making it a transboundary issue for the whole region.

As an extension of the current study it would be interesting to gain insight into the seasonality and the relative magnitude of PM 10 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 Reid et al. 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. The backruns shown were conducted from a receptor site in central Singapore. Figure A2 Air history maps for the years to , showing where air arriving in Singapore during each year originated from.

For FMA; see Fig. 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. Hence, the 1-hour PM2. Use this for immediate activities like going for a jog. Year All Haze Situation Update 4 October 04 Oct Singapore, 4 October — Widespread thundery showers fell over Singapore in the morning and early afternoon today.