Stations In Singapore Research Articles

Acoustic phenology of tropical resident birds differs between native forest species and parkland colonizer species

Most birds are characterized by a seasonal phenology closely adapted to local climatic conditions, even in tropical habitats where climatic seasonality is slight. In order to better understand the phenologies of resident tropical birds, and how phenology may differ among species at the same site, we used ~70 000 hours of audio recordings collected continuously for two years at four recording stations in Singapore and nine custom‐made machine learning classifiers to determine the vocal phenology of a panel of nine resident bird species. We detected distinct seasonality in vocal activity in some species but not others. Native forest species sang seasonally. In contrast, species which have had breeding populations in Singapore only for the last few decades exhibited seemingly aseasonal or unpredictable song activity throughout the year. Urbanization and habitat modification over the last 100 years have altered the composition of species in Singapore, which appears to have influenced phenological dynamics in the avian community. It is unclear what is driving the differences in phenology between these two groups of species, but it may be due to either differences in seasonal availability of preferred foods, or because newly established populations may require decades to adjust to local environmental conditions. Our results highlight the ways that anthropogenic habitat modification may disrupt phenological cycles in tropical regions in addition to altering the species community.

A novel rainfall forecast model using GNSS observations and CAPE

Precipitable water vapor (PWV) and zenith total delay (ZTD) are highly correlated indicators used for forecasting rainfall effectively. These two factors are widely used when establishing qualitative and quantitative rainfall forecast models. Another indicator, however, convective available potential energy (CAPE), is also highly correlated with the occurrence of rainfall, but has not been combined with PWV or ZTD in previous studies. Therefore, a novel rainfall forecast model based on support vector regression that combines CAPE and Global Navigation Satellite System (GNSS)-derived PWV is proposed in this paper. Moreover, annual, seasonal scales, and hourly autocorrelation for predictors were also considered, and wavelet coherence (WTC) was introduced to further reveal the correlation relationships between CAPE, PWV and rainfall in both time and frequency domains. Hourly PWV, CAPE, temperature (T) and rainfall over a time span of three years were collected from four stations in Singapore and Taiwan to evaluate the performance of the proposed model. The root mean square error (RMSE) and mean absolute error (MAE) between the measured and forecasted rainfall were calculated for each year and averaged. Subsequently, the average RMSE and MAE were determined for the annual seasonal rainfall. The annual average RMSE value was 0.71 mm and the average annual seasonal RMSE value was 0.69 mm. The annual average MAE value was 0.21 mm and the annual seasonal was 0.22 mm. The average correlation coefficient (R2) for the annual value was 0.94 and the annual seasonal R2 value was 0.93. These results indicate that the forecast accuracy at the seasonal scale is basically consistent with annual scale. Additionally, a measure called comparable RMSE (CRMS) was introduced to evaluate the forecasting accuracy across all grades of rainfall. This analysis showed that the accuracy of moderate rainfall (MR) and heavy rainfall (HR) forecasts is similar, but slight rainfall (SR) forecasts are the most accurate among seasons and schemes.

Influence of Socio-Demography and Operating Streetscape on Last-Mile Mode Choice

This study investigated how personal and operational factors (travel distance and streetscape) influence traveler mode choice decisions for the last-mile home-bound trip stage from rail transit stations. Personal factors include the socio-demography of travelers, and attributes of the streetscape include the built environment (degree of areal development), prevalence of cycling, availability of short-range transport modes, and walking/cycling infrastructure. Interviewers randomly intercepted pedestrians to administer a mode choice survey at five rail transit station exits and engaged all available cyclists at bicycle parking areas in the vicinity of stations in Singapore. A multimodal logit regression model revealed a significant relationship between the last-mile home-bound trip maker’s mode choice with factors of age, gender, travel distance between transit station and destination, number of cyclists along adjacent links surrounding transit stations, number of feeder bus services to destination, availability of private vehicle, and household income. The calibrated model was applied to compute the probability of walking, cycling, and taking a feeder bus for the last-mile home-bound trip maker from a transit station. This study provides useful information for improving the efficiency and connectivity of first/last-mile mobility in a multimodal transport network.

Air quality in underground metro station commuter platforms in Singapore: A cross-sectional analysis of factors influencing commuter exposure levels

The metro is a major component of public transport systems in urbanized cities and may expose commuters to indoor air pollutants. We examined how commuter exposure to indoor air pollutant levels varied with the characteristics of underground metro stations. We carried out a national study of 41 commuter platforms from 39 randomly selected underground stations in Singapore. We used generalized linear models to assess the variability of air pollutant concentrations with station characteristics. We computed commuter-weighted pollutant-specific station rankings to inform mitigation priority. PM2.5 (mean = 24.1 μg/m3) and PM10 (mean = 26.9 μg/m3) concentrations across commuter platforms were generally lower than those found outdoors while platform CO2 levels (mean = 608.1 ppm) were 1.7 times those outdoors. More stations exceeded the WHO 24-h mean exposure guidelines values for PM2.5 (n = 30, 73%) than for PM10 (n = 4, 10%). PM2.5, PM10 and CO2 levels were lowest in stations on the Circle, North-East and Downtown lines. PM2.5 and PM10 concentrations at platforms were positively associated with their outdoor concentrations. Mean CO2 concentration levels at platforms were the highest during commuter rush hours. Longer train arrival intervals were associated with lower CO2 levels. PM2.5 and PM10 levels in stations were negatively associated with the depth of the commuter platforms. Commuter volumes influenced the priority ranking of mitigation measures. Underground metro stations exhibited high variability and dependence on station characteristics as well as outdoor ambient pollutant levels. Our study findings suggest that any required improvements in ventilation and station design should be focused on PM2.5 exposure reduction and prioritized according to both indoor air pollutant levels and commuter volumes to provide the largest extent of commuter benefits.

Short-term rainfall forecast model based on the improved BP\u2013NN algorithm

The existing methods have been used the Zenith Total Delay (ZTD) or Precipitable Water Vapor (PWV) derived from Global Navigation Satellite System (GNSS) for rainfall forecasting. However, the occurrence of rainfall is highly related to a myriad of atmospheric parameters, and a good forecast result cannot be obtained if it only depends on a single predictor. This study focused on rainfall forecasting by using a number of atmospheric parameters (such as: temperature, relative humidity, dew temperature, pressure, and PWV) based on the improved Back Propagation Neural Network (BP–NN) algorithm. Results of correlation analysis showed that each meteorological parameter contributed to rainfall. Therefore, a short-term rainfall forecast model was proposed based on an improved BP–NN algorithm by using multiple meteorological parameters. Two GNSS stations and collocated weather stations in Singapore were used to validate the proposed rainfall forecast model by using three years of data (2010–2012). True forecast (TFR), false forecast (FFR), and missed forecast (MFR) rate were introduced as evaluation indices. The experimental result revealed that the proposed model exhibited good performance with TFR larger than 96% and FFR of approximately 40%. The proposed method improved TFR by approximately 10%, whereas FFR was comparable to existing literature. This forecasted result further verified the reliability and practicability of the proposed rainfall forecasting method by using the improved BP–NN algorithm.

Projections of Future Climate Change in Singapore Based on a Multi-Site Multivariate Downscaling Approach

Estimates of the projected changes in precipitation and temperature have great significance for adaption planning in the context of climate change. To obtain the climate change information at regional or local scale, downscaling approaches are required to downscale the coarse global climate model (GCM) outputs to finer resolutions in both spatial and temporal dimensions. The multi-site, multi-variate downscaling approach has received considerable attention recently due to its advantage in providing distributed, physically coherent downscaled meteorological fields for subsequent impact modeling. In this study, a newly developed multi-site multivariate statistical downscaling approach based on empirical copula was applied to downscale grid-based, monthly precipitation, maximum and minimum temperature outputs from nine global climate models to site-specific, daily data over four weather stations in Singapore. The advantage of this approach lies in its ability to reflect the at-site statistics, inter-site and inter-variable dependencies, and temporal structure in the downscaled data. The downscaling was conducted for two projection periods (i.e., the 2021–2050 and 2071–2100 periods) under two emission scenarios (i.e., representative concentration pathway (RCP)4.5 and RCP8.5 scenarios). Based on the downscaling results, projected changes in daily precipitation, maximum and minimum temperatures were examined. The results show that there is no consensus on the projected change in average precipitation over the two future periods. The major uncertainty for precipitation projection comes from the GCMs. For daily maximum and minimum temperatures, all downscaled GCMs project an increase of average temperature in the future. These change signals could be different from those of the original GCM data, both in magnitude and in direction. These findings could assist in adaption planning in Singapore in response to emerging climate risks.

Parallel shuttle bus service design for planned mass rapid transit shutdown: The Singapore experience

19 MRT stations in Singapore experienced a two full-day shutdown as planned in order to upgrade the infrastructure of aging mass rapid transit (MRT) lines. A large bus fleet, running entirely parallel to MRT lines, was deployed to ferry passengers between affected stations, which however results in prolonged congestion and queue of buses near terminals. Rail transit systems in many metropolitan areas are threatened because of aging infrastructure. This study, motivated by the Singapore case, proposes a novel parallel shuttle bus service design (PSBSD) problem that addresses route design, terminal selection, berth allocation, and bus deployment to minimize inconvenience caused to passengers, which is seldom addressed in previous studies. We further develop a mixed-integer nonlinear programming (MINLP) model for the PSBSD problem. In order to solve the non-convex MINLP model optimally, we put forward an effective decomposition method, which comprises (i) the generation of candidate shuttle bus routes through a brute-force search for semi-route and a terminal selection procedure and (ii) the deployment of buses and assignment of terminal berths for selected shuttle bus routes by using an optimization-based approach. Numerical experiments were conducted using data sets from the MRT closure incident in Singapore. The results demonstrate that the decomposition method is capable of finding optimal solutions. Useful insights are also obtained from testing the models under different scenarios.

Study on land use characteristics of rail transit TOD sites in new towns—taking Singapore as an example

ABSTRACT In order to understand the land use characteristics of the rail transit station sites in new towns of high-density Asian city, this study selected six rail transit stations in Singapore as the research objects. Through the GIS spatial analysis, the study analyzes the rail transit sites in the new town centers and private residential areas, as well as the similarities and differences between Urban TODs and Neighborhood TODs, with consideration to two spatial structure forms of Singapore’s new towns. The study finds out that the rail transit station sites in Singapore’s new town centers have gradually formed a specific TOD land use model for Asian transit village. The urban center hierarchy and the new town development concept have led to the difference in land use characteristics of the TOD stations. It is suggested that the mixed-use development of the TOD station area should be tailored to local conditions and a certain proportion of public service facilities, parks and open spaces should be guaranteed, to achieve sustainable development of land economy, environment and society.

On adding and removing sensors in a solar irradiance monitoring network for areal forecasting and PV system performance evaluation

Abstract With an increasing number of PV systems of various scales, real-time irradiance monitoring at every system location becomes costly. Therefore, using a suitable network of sensors has practical relevance. Setting up monitoring stations is often a stepwise procedure, where a batch of sensors can be installed or removed each time. In this paper, I outline the procedure for irradiance monitoring network redesign by deriving criteria for expanding and reducing the network. A monitoring network with 24 stations in Singapore is used in the empirical part of the paper. In addition, a nonparametric covariance estimation technique which handles both anisotropy and heterogeneity is promoted.

The Relationship of Stratospheric QBO with the Difference of Measured and Calculated NmF2

The relationship between stratospheric QBO and the difference (ΔNmF2) between NmF2 calculated with IRI-2012 and measured from ionosondes at the Singapore and Ascension stations in the equatorial region was statistically investigated. As statistical analysis, the regression analysis was used on variables. As a result, the relationship between QBO and ΔNmF2 was higher for 24:00 LT (local time) than 12:00 LT. This relationship is positive in the solar maximum epoch for both stations. In the solar minimum epoch, it is negative at 24:00 LT for Ascension and at 12:00 LT for Singapore. Furthermore, it was seen that the relationship of the ΔNmF2 with both the easterly and westerly QBO was negative for all solar epochs and every LT, at Ascension station. This relationship was only positive for solar maximum epoch and 12:00 LT, at Singapore station.

Negative Externalities of Rail Noise and Housing Values: Evidence from the Cessation of Railway Operations in Singapore

The governments of Malaysia and Singapore reached a landmark agreement in May 2010 to end the operations of nearly 80‐year‐old railway lines and stations in Singapore. In our study, the cessation of the railway services operated by Keretapi Tanah Malaya (KTM), a firm owned by the Malaysian government, with effect from July 1, 2011 is used in a quasi‐experiment design to test the effects of the removal of train noise externalities on real estate values. Based on the nonlanded private housing transactions data from January 2005 to June 2013, we find that average prices for houses located within a 400‐m boundary from the railway lines increased by 3.5% relative to prices for houses located outside the 400‐m boundary after the cessation agreement has been announced. The removal of train noise externalities increases housing prices in the affected area by 13.7% on average in the postcessation period of the KTM railway services. Realized economic benefits associated with the railway services cessation were estimated at S$0.36 billion based on houses sold in the post cessation period of the KTM railway services.

The effect of the stratospheric QBO on the neutral density of the D region

<p>A multiple regression model, which defines relationship between two variables, is used to perform a statistical analysis of the relationship between the stratospheric QBO and the neutral density of the D region (N<sub>n</sub>D) at altitudes of 75 km and 90 km for Singapore station. While performing the analysis, the solar maxima and solar minima epochs of the sun for 21st, 22nd and 23rd solar cycles (SCs) are taken into account. Before applying the model for the statistical analysis of the relationship, the stationary of the variables is investigated by using the unit root test. The relationship between the variables is also investigated by using the co-integration test. The relationship between N<sub>n</sub>D measured at 75 km altitude and QBO obtained at altitude of 10 hPa is observed that it is positive for solar maximum epoch at 21st and 23rd SCs and for solar minimum epoch at 21st SC and is negative at the other epochs. The relationship between N<sub>n</sub>D measured at 90 km altitude and QBO is observed to be negative at both the solar maxima expect for solar maximum of 23rd SC and the solar minima epochs. The relationship between variables is positive for both phases (east and west) of QBO. Thus, QBO leads to a statistical change in the N<sub>n</sub>D. It may also give rise to changes on the ion chemistry of the D region.</p>

Negative Externality of Rail Noise and Housing Values: Evidence from the Cessation of Railway Operations in Singapore

The governments of Malaysia and Singapore reached a landmark agreement in May 2010 to end the leases for the nearly 80-year-old railway lines and stations in Singapore, which were operated by Keretapi Tanah Malaya (KTM), a firm owned by the Malaysian government, with effect from July 1, 2011. This study uses the cessation of KTM railway services in a quasi-experiment design to test real estate capitalization effects of the removal of train noise externalities. Based on the non-landed housing transactions data in Singapore from January 2005 to June 2013, we find that houses located within 400 meters from the railway lines increased by 3.5% on average relative to houses located outside the 400 meters boundary after the announcement of the agreement. The removal of train noise externalities increases the prices of houses in the affected area by 13.7% on average in the post-cessation of KTM railway services. Realized economic benefits associated with the cessation of the railway services were estimated at S$0.36 billion based on houses sold in the post-KTM period.

Computationally efficient prediction of canopy level urban air temperature at the neighbourhood scale

The Urban Weather Generator (UWG) is a simple and computationally efficient model that predicts canopy level urban air temperature using meteorological information measured at a reference weather station. An evaluation of an improved version of the model, which accounts for different urban morphologies and building usage distributions within a city, is presented in this paper. Calculated urban air temperatures are compared with measurements from a network of weather stations in Singapore, representing a range of land uses, morphological parameters and building usages. The comparison shows a satisfactorily performance of the model for all weather conditions and for different reference weather stations. Singapore is located in a hot and humid climate where vegetation plays a critical role in climate regulation, the urban morphology is very heterogeneous and air-conditioning systems are generally used throughout the year. This makes Singapore an interesting case study in order to analyse the potential and limitations of the model. The study shows that the model can be applied to different climates and urban configurations to obtain an estimation of the Urban Heat Island (UHI) effect. However, the simplifications and assumptions of the model prevent it from capturing very site-specific microclimate effects.

Investigations on coefficient of variation of extreme wind speed

The uncertainty of extreme wind speeds is one key contributor to the uncertainty of wind loads and their effects on structures. The probability distribution of annual extreme wind speeds may be characterized using a classical Gumbel Type distribution. The expression that establishes the relationship between the extreme wind speeds at different recurrence periods and the corresponding coefficients of variation is formulated, and its efficacy is validated. The coefficients of variation are calibrated to be about 0.125 and 0.184 according to defined Chinese and US design specifications, respectively. Based on the wind data of 54 cities in China, 49 meteorological stations in the US, 3 stations in Singapore, the coefficients span intervals of (0.1, 0.35), (0.08, 0.20) and (0.06, 0.14), respectively. For hurricanes in the US, the coefficients range approximately from 0.3 to 0.4. This convenient technique is recommended as one alternative tool for coefficient of variation analyses in the future revisions of related codes. The sensitivities of coefficients of variation for 49 meteorological stations in the US are quantified and demonstrated. Some contradictions and incompatibilities can be clearly detected and illustrated by comparing the coefficients of variation obtained with different combinations of recurrence period wind data.

Validation of the IRI-2012 model with GPS-based ground observation over a low-latitude Singapore station

The ionospheric total electron content (TEC) in the low-latitude Singapore region (geographic latitude 01.37° N, longitude, 103.67° E, geomagnetic latitude 8.5° S) for 2010 to 2011 was retrieved using the data from global positioning system (GPS)-based measurements. The observed TEC from GPS is compared with those derived from the latest International Reference Ionosphere (IRI)-2012 model with three options, IRI-Nequick (IRI-Neq), IRI-2001, and IRI-01-Corr, for topside electron density. The results showed that the IRI-Neq and IRI-01-Corr models are in good agreement with GPS-TEC values at all times, in all seasons, for the year 2010. For the year 2011, these two models showed agreement at all times with GPS-TEC only for the summer season, and for the period 11:00 to 24:00 UT hours (19:00 to 24:00 LT and 00:00 to 08:00 LT) during the winter and equinox seasons. The IRI-2012 model electron density profile showed agreement with constellation observing system for meteorology, ionosphere, and climate (COSMIC) radio occultation (RO)-based measurements around 250 to 300 km and was found to be independent of the options for topside density profiles. However, above 300 km, the IRI-2012 model electron density profile does not show agreement with COSMIC measurements. The observations (COSMIC and GPS) and IRI-2012-based data of TEC and electron density profiles were also analyzed during quiet and storm periods. The analysis showed that the IRI model does not represent the impact of storms, while observations show the impact of storms on the low-latitude ionosphere. This suggests that significant improvements in the IRI model are required for estimating behavior during storms, particularly in low-latitude regions.

The Estimation of Clear Sky Global Horizontal Irradiance at the Equator

We analyse Singapore global horizontal irradiance (GHI) measurements to estimate clear sky GHI in the equator region. The data consists of a one-year period of clear sky GHI at three solar irradiance monitoring stations in Singapore, namely, First Zero Energy House in Singapore (1.3094∘N, 103.9160∘E) located in the east of the country, Solar Energy Research Institute of Singapore (1.3007∘N, 103.7718∘E) in the mid-west, and Nanyang Technology University Intelligence Systems Center (1.3436∘N, 103.6792∘E) in the west. Several empirical clear sky models are considered for a Singapore case study. An regression method is proposed to parameterise the model of choice for Singapore's measured GHI. The developed model is validated using GHI measurements from difference stations over different time periods.

Using the spectral scaling exponent for validation of quantitative precipitation forecasts

This study evaluates the spectral scaling of a heavy rainfall event and assesses the performance of the Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS) model in terms of the multiscale variability of rainfall in the temporal spectral domain. The event occurred over southern Malay Peninsula on 18 December 2006 and was simulated at high resolutions. 10, 5 and 1 min aggregate rainfall data from rain gauge stations in Singapore and sim- ulated rainfall sampled at different evaluation points on 0.9, 0.3 and 0.1 km grids were utilized. The simulated and observed rain rates were compared via Fourier and wavelet analyses. A scaling regime was noted in the observed rainfall spectra in the timescales between 60 min and 2 min. The scaling exponent obtained from the observed spectra has a value of about 2, which may be indicative of the physics of turbulence and raindrop coalescence and might suggest the predominance of a characteristic raindrop size. At 0.9 km resolution, the model rainfall spectra showed similar scaling to the observed down to about 10 min, below which a fall-off in variance was noted as compared to observations. Higher spatial resolution of up to 0.1 km was crucial to improve the ability of the model to resolve the shorter timescale vari- ability. We suggest that the evaluation of dynamical models in the spectral domain is a crucial step in the validation of quantitative precipitation forecasts and assessing the minimal grid resolution necessary to capture rainfall variability for certain short timescales may be important for hydrological predictions.

Aerosol transport model evaluation of an extreme smoke episode in Southeast Asia

Biomass burning is one of many sources of particulate pollution in Southeast Asia, but its irregular spatial and temporal patterns mean that large episodes can cause acute air quality problems in urban areas. Fires in Sumatra and Borneo during September and October 2006 contributed to 24-h mean PM 10 concentrations above 150 μg m −3 at multiple locations in Singapore and Malaysia over several days. We use the FLAMBE model of biomass burning emissions and the NAAPS model of aerosol transport and evolution to simulate these events, and compare our simulation results to 24-h average PM 10 measurements from 54 stations in Singapore and Malaysia. The model simulation, including the FLAMBE smoke source as well as dust, sulfate, and sea salt aerosol species, was able to explain 50% or more of the variance in 24-h PM 10 observations at 29 of 54 sites. Simulation results indicated that biomass burning smoke contributed to nearly all of the extreme PM 10 observations during September–November 2006, but the exact contribution of smoke was unclear because the model severely underestimated total smoke emissions. Using regression analysis at each site, the bias in the smoke aerosol flux was determined to be a factor of between 2.5 and 10, and an overall factor of 3.5 was estimated. After application of this factor, the simulated smoke aerosol concentration averaged 20% of observed PM 10, and 40% of PM 10 for days with 24-h average concentrations above 150 μg m −3. These results suggest that aerosol transport models can aid analysis of severe pollution events in Southeast Asia, but that improvements are needed in models of biomass burning smoke emissions.

Colorimetric Sensor Array for Qualitative Water Analysis

A chemosensor array comprising 45 off-the-shelf colorimetric dyes, dubbed the Singapore Tongue (SGT), that is capable of discriminating different brands of bottled water and waters of different geographical attribute is described. Twelve kinds of bottled waters were tested by the SGT, and changes of absorbance spectra were analyzed by unsupervised classification methods to validate the SGT system for water analysis. All 12 bottled waters were discriminated at 1 × concentration, and SGT could distinguish the identity of samples of the waters diluted up to 100 times, except distilled waters. Following the study of 63 tap waters in different mass rapid transit stations in Singapore, two distinct clusters were observed from a principal component analysis plot, which correspond to the origin of the tap water. The successful discrimination and identification of in this study demonstrates the practical application of the SGT as a simple tool for water analysis.