Supporting Information

Performance Evaluation of Filter Applications in Fan-coil Units during the 2015 Southeast Asian Haze Episode

Qingliang Cao1, Ailu Chen1, 2, Jin Zhou1, 2, Victor W.-C. Chang1, 2*

1School of Civil and Environmental Engineering, Nanyang Technological University, 50 Nanyang Avenue 639798, Singapore2SinBerBEST Program, Berkeley Education Alliance for Research in Singapore (BEARS), 1 CREATE Way, University Town 138602, Singapore

*Corresponding Author: Tel: +65-6790-4773, Fax: +65-6792-1650, E-mail: wcchang@ntu.edu.sg

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Figure S1. Schematic diagram of the measurement location for particles, room temperature and RH. The sizes of the room, FCUs and the door were scaled according to their real dimensions.

Note: The temperature/humidity probes (as accessories of OPCs) were used to record the temperature and relative humidity (RH) conditions in the indoor and outdoor environments during the monitoring period. A VelociCalc Air Velocity Meter (model 9545-A, TSI Inc., USA) was used for this purpose in the room with the OPS.

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Figure S2. Disaggregation and merging of OPS measurements into three size bins as OPCs’ (Zhou et al., 2015).

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Table S1. Collocation factors of OPCs with reference to the OPS.

Device ID 0.3-0.5µm 0.5-1.0µm 1.0-2.5µmMean STD Mean STD Mean STD

OPC-Outdoor 2.43 0.10 1.78 0.08 2.26 0.12

OPC-A 2.42 0.13 1.35 0.11 1.38 0.09OPC-B 2.29 0.13 1.44 0.11 1.97 0.13OPC-C 1.75 0.13 1.39 0.09 1.73 0.09OPC-D 2.28 0.12 2.42 0.11 1.84 0.10

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Figure S3. Outdoor 1-h and 24-h PM2.5 data in West Singapore reported by Singapore’s National Environmental Agency (NEA, 2016).

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Table S2. Daily average outdoor PM2.5 on 9 monitoring days (NEA, 2016).

Date Average PM2.5 (µg m-3) Haze SeverityClassification

17-Sep 37 Light18-Sep 53 Light19-Sep 88 Moderate20-Sep 42 Light21-Sep 47 Light22-Sep 66 Moderate23-Sep 90 Moderate24-Sep 196 Heavy25-Sep 177 Heavy

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Table S3. Indoor and outdoor RH conditions (daily mean ± standard deviation, %) during the monitoring period.

Date Outdoor Room A Room B Room C Room D Room E17-Sep 55.8±14.3 63.1±4.8 70.4±4.3 52.5±5.3 74.0±1.2 67.7±5.318-Sep 62.3±8.2 71.7±5.9 68.6±6.4 51.6±3.0 75.2±1.4 70.7±5.119-Sep 58.4±11.4a 65.4±1.7 74.3±2.1 51.0±2.9 75.4±1.7 73.0±4.720-Sep N.A.b 74.1±1.1 75.6±1.9 54.3±1.1 75.4±2.1 75.5±2.421-Sep N.A.b 69.5±6.1 73.4±4.7 54.4±3.5 76.1±1.4 76.0±2.222-Sep 71.5±5.1a 74.3±2.7 71.0±6.0 53.0±3.2 74.8±3.4 75.1±2.723-Sep 65.9±14.5 73.0±5.8 71.5±5.4 53.6±1.8 76.9±2.6 72.0±6.724-Sep 57.4±17.4 64.0±3.2 73.3±2.8 52.5±1.1 64.8±1.9 61.4±1.425-Sep 51.6±19.6 63.3±1.9 71.3±4.9 52.9±1.8 62.5±2.0 63.1±3.8

a Based on ~11 hours’ data due to outdoor RH sensor malfunction for the rest of the day.b Data not available due to outdoor RH sensor malfunction all day.

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Table S4. Indoor and outdoor temperature conditions (daily mean ± standard deviation, ̊C) during the monitoring period.

Date Outdoor Room A Room B Room C Room D Room E17-Sep 28.8±1.8 23.6±0.7 24.4±0.5 23.4±1.1 23.4±0.8 24.6±0.718-Sep 29.4±1.8 23.5±1.2 24.6±0.9 23.3±1.3 23.5±0.9 23.9±0.719-Sep 29.5±2.1 23.2±0.3 23.9±0.2 24.0±1.5 23.1±0.2 23.8±0.520-Sep 28.5±1.6 23.6±0.1 23.8±0.2 23.7±0.3 23.4±0.4 23.4±0.321-Sep 28.6±1.8 23.6±1.4 24.2±0.6 24.0±1.6 23.5±0.8 23.4±0.322-Sep 28.3±1.3 24.0±0.7 24.3±0.8 24.5±1.2 23.6±1.0 23.5±0.423-Sep 28.2±1.9 23.4±1.4 24.2±0.7 23.9±1.1 23.3±0.9 23.6±1.024-Sep 28.2±1.8 23.9±1.3 23.1±0.3 24.3±0.5 23.9±0.2 23.3±0.325-Sep 28.7±2.2 24.2±0.7 23.5±0.6 23.9±0.2 24.5±1.1 24.1±0.8

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Figure S4. Instant chilled water energy (kW h) during a three-hour period under the filter application scenario of. (a) control status; (b) F25; (c) F65; (d) F85; (e) F95.

Figure S4 depicts the instant chilled water energy during a three-hour period. Since

temperatures of supply and return water both varied in small ranges, instant volumes of

chilled water played the most decisive role in determining the instant chilled water energy.

Figure S4 in fact reflects the instant cooling activities under each scenario. As shown, each

cooling session usually started with a relatively high chilled water amount, slipped to a lower

level shortly, kept stable for several minutes and stopped the chilled water supply. The

cooling sessions can be characterized by the frequency, the session duration, and the average

instant energy level. It is clear that the F25 scenario had a lower cooling frequency and also a

lower average instant energy level than the control scenario, leading to lower chilled water

energy than the control. The latter three filter scenarios were characterized by large

fluctuations of instant energy levels and irregular cooling session intervals without fixed

pattern. But it can be seen that the F85 scenario had the shortest cooling session durations,

which explains why F85 scenario resulted in the lowest energy consumption.

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