The Relationships Between Dust Particulates and Meteorological Parameters in Kuala Lumpur and Petaling Jaya, Malaysia

THE RELATIONSHIPS BETWEEN DUST PARTICULATES AND METEOROLOGICAL PARAMETERS IN KUALA LUMPUR AND PETALING JAYA, MALAYSIA

1Mohamed E. Yassen Universiti Kebangsaan Malaysia, Bangi, Malaysia

Abstract

This study aims to examine the relationship between dust particulate and selective meteorological variables such as rainfall, relative humidity, temperature and wind speed in Kuala Lumpur and Petaling Jaya, Malaysia, during 1983-1997. Correlation, simple regression and multiple regression techniques have been widely used to model dust concentrations as a function of meteorological conditions. The results reveal that a correlation between dust and rainfall yields reasonable negative relationship, the r- value was ranged from -0.050 to –0.687 and not statistically significant at 0.05 level. The variation of rainfall, relative humidity, wind speed and temperature on the average explains 46% of dust concentrations. This indicates that about 46% of the dust particulates concentrations are attributed to meteorological parameters.

Key words, dust, meteorological, regression

1. INTRODUCTION

The actual analysis of dust particulate as a function of one or more meteorological parameters can be carried out on quite different levels; yearly, and monthly. The analysis of dust particulate and climate data can be utilized to illustrate certain functional and other statistical relationships. The correlation and regression analysis techniques are useful in investigating these relationships. The relationship between meteorological parameters and pollutant concentrations has been well studied in different parts of the world for different time periods. A lot of research has been devoted to the study of the local climatic conditions in relation to the air quality. Dickson (1961), for example, studied the relationship between precipitation and particulate matter in Nashville, whereas Sham (1979), used a linear correlation to model respirable dust particulate and average wind speed at Kuala Lumpur and Petaling Jaya, his study yielded r- value of -0.2358.

2. DATA AND METHOD

Monthly dust particulates were obtained from the Department of Environment (DOE) and Alam Sekitar Malaysia Sdn Bhd (ASMA) for 1983-1997. In addition to monthly dust data, monthly data of several meteorological variables (temperature, solar radiation, sunshine, wind speed and relative humidity) for the same period were obtained from Malaysian Meteorological Service (MMS). From the above data set, time series of dust concentrations and meteorological variables were developed and analyzed here. The regression techniques has been widely used to model dust indicators as a function of meteorological parameters, such as temperature, solar radiation, sunshine, relative humidity and wind speed. The relation between dust particulate and meteorological variables can be understood better by using multiple regression model. The general approach is to regress dust against independent variables, which include meteorological data. Possible relationships between these variables were examined statistically using the multiple linear regression method which is appropriate to relate dust particulate to meteorological variables, has the formulation given as follows:

Si= b0 + b1Rfi + b2Rhi + b3Ti + b4Wsi + ei (1)

Where: i: 1,2,3…, n

SI: dependent variable (dust particulate) b0: the intercept b: 1…4, the model parameters Rf: Rainfall (mm) Rh: Relative humidity (%)

* Corresponding author address: Mohamed E. Yassen, Program of Geography, School of Social, Development and Environmental Studies, Faculty of Social Sciences & Humanities, Universiti Kebangsaan Malaysia, Bangi, 43600 UKM, Bangi, Selangor, Malaysia; e-mail: [email protected] T: Temperature (0C) Ws: Wind speed (m/sec)

4. RESULTS AND DISCUSSION Analyses of the meteorological parameters affecting dust concentrations should include rainfall, relative humidity, temperature and wind speed.

4.1 Linear regression

Regression analyses were performed to determine the meteorological variables that explained the most variance in the overall data. The results of application of correlation coefficients and regression analysis are listed in Table

4.1.1 Rainfall

A linear correlation between dust particulates and rainfall yields reasonable negative relationship, the r-values ranged from -0.050 to -0.687. A correlation between dust concentrations and rainfall amount shows poor to moderate results. The resulting t-value was low (ranged between -0.481 and -1.016) and not statistically significant at 0.05 level. The negative relation might be dust particulate reduced through washout process. Sham

(1979) pointed out that a poor relation between dust and precipitation amount may probably be due to the generally more stable atmospheric condition and hence less pollution dispersion when rain occurs. Kerker and

Hampel (1974) stated that washout may be significant factor in cleansing the atmosphere of 0.1 mm aerosol. The lighter rainfall is much more efficient in cleansing the atmosphere of 0.1 mm aerosol because of the greater collection efficiency of the smaller raindrops. Precipitation is important through the absorption processes within the cloud, known as rainout, and that termed washout, which is a scavenging of air pollutants by falling raindrops

(cf. Elsom and Chandler 1978).

4.1.2 Relative humidity

From Table 1, relative humidity also correlates negatively with dust particulate. The correlation coefficients between mean dust particulates and relative humidity ranged from 0.013 to -0.712. No significant relationship was found between dust and relative humidity, although there appears to be a tendency towards an inverse relationship between the two variables. The relative humidity in the study area more than 70% on the average.

Therefore, the higher the relative humidity, the greater the rate of settling of the dust from the atmosphere.

4.1.3 Temperature

A linear correlation between dust particulates and temperature yields a moderate positive relationship, r-value ranged from 0.02 to 0.612. The resulting t-value is not statistically significant at the 0.05 level. It is nevertheless interesting to note that the r-value is slightly better than that between TSP and temperature. Sham (1979), stated that the influence of weather factors upon respirable dust particulates in Kuala Lumpur-Petaling Jaya area is largely inconclusive. 4.1.4 Wind speed

A linear correlation between dust particulate and wind speed also yields a poor relationship. The correlation coefficient however is low, r-value ranged between 0.063 and -0.510 and statistically is not significant at 0.05 level. Dust particulate comes more from the lower level of domestic sources. The effects of wind speed operate to be far more effectively on high level emissions than on lower emissions. Chow and Lim (1984) stated that winds below an altitude of 1 km over the Klang Valley, especially the urban centers, are generally light and variable. Hence, with regard to the horizontal transport pollutants it is suspected that the dispersion of pollutants by wind in the Klang Valley region is relatively limited. Sham (1979) found a poor relationship between respirable dust particulate and wind speed, the correlation coefficient in the order of -0.1273 in Kuala Lumpur-Petaling Jaya.

Table 1 Results of Regression Analysis between Dust particulates and Selected Meteorological Parameters at Petaling Jaya, 1983 Parameters Constant B Std. E. B Beta t-statistic p-value Rainfall 1602.496 -1.177 1.781 -0.205 -0.661 0.523 Relative humidity 4131.431 -34.936 67.786 -0.161 -0.515 0.617 Temperature 9051.843 -278.402 191.307 -0.418 -1.455 0.176 Wind speed 939.205 449.996 901.940 0.156 0.499 0.629

4.2 Multiple Linear Regression

Having established with meteorological parameters control resulting ground level concentrations of dust particulates in Petaling Jaya, these were then combined to produce multiple regression equations. The important meteorological controls derived in previous section were rainfall (Rf), relative humidity (Rh), temperature (T) and wind speed (Ws). These four meteorological parameters were combined to produce multiple regression equations. The multiple regression equations are outlined in Table 2 in terms of the parameter coefficient, the multiple correlation coefficient (R) and the amount of variance explained (R2, expressed in per cent). The four- variable accounted for 46% (R2 = 0.462) of the variability in the dust concentrations. The overall dust concentration in Petaling Jaya during the study period were most affected by relative humidity, indicated by the strong negative beta coefficient associated with this variable. Rainfall and wind speed were also important parameters in determining the variability of dust concentrations. It accounted for approximately 46% of the variability in the concentrations. Although the results are statistically insignificant at the 95% level. Multiple regression analysis is used to interpret the relationship between dust concentration and meteorological conditions. The specific application of multiple regression to dust concentration yields reasonable results. It is note that in the multiple regression with four variable having reasonable effects upon dust concentrations the amount of variance in dust that is explained rises to 46% (R square = 0.462). Table 2 Summary of the Multiple Regression to Calculate Average Dust Particulates Concentration and Meteorological Parameters at Kuala Lumpur- Petaling Jaya, 1993-1997 ______

Regression coefficient of parameter Year Constant Rf Rh T Ws Multiple Variance corr. explained % Coeff. 1983 27124.834 -0.080 -0.653 -0.802 0.150 0.693 0.480 1984 -11058.00 -0.722 0.253 0.335 -0.164 0.753 0.567

1985 22636.682 0.327 -0.319 -0.460 -0.033 0.791 0.625 1986 9397.823 0.189 -0.744 -0.549 0.503 0.472 0.223 1987 27068.967 -0.213 -0.773 -0.877 0.314 0.794 0.630 1988 14344.803 -0.459 -0.447 -0.524 -0.183 0.784 0.615 1989 1390.274 0.744 -0.321 0.475 -0.118 0.495 0.245 1990 11289.428 -0.570 -0.623 -0.223 0.141 0.801 0.642 1991 2459.470 0.303 -0.541 0.367 -0.335 0.523 0.273 1992 1773.826 0.834 -0.647 0.510 -0.969 0.600 0.640 1993 15986.521 -0.171 -0.232 -0.621 0.048 0.800 0.300 1994 -11324.02 -0.491 0.953 0.446 -0.591 0.540 0.360 1995 20267.006 0.193 -0.736 -0.539 0.109 0.470 0.230 1996 11211.106 -0.913 -0.061 -0.400 -0.182 0.709 0.503 1997 16935.015 -0.024 -0.047 -0.141 -0.393 0.771 0.595 Avrg. 10448.212 -0.069 -0.49 -0.191 -0.087 0.666 0.462

5. CONCLUSION

This study has developed a series of multiple regression equations incorporating a limited number of readily available meteorological parameters to concentrations for 1983-1997. The multiple regression analyses were performed to determine the meteorological variables that explained the most variance in the overall data. The results show that most meteorological parameters correlate negatively with dust particulates, thereby indicate that lower concentrations of dust particulates are associated with higher meteorological parameters. It should be observed that most meteorological parameters correlate negatively with dust particulates, thereby indicate that lower concentrations of dust particulates are associated with higher meteorological parameters. The relationships are reasonable good as is evidenced by even moderate correlation coefficients.

References

Chow K. K. and Lim J. T., 1984, Monitoring of suspended particulate In Petaling Jaya, In Yip Y. H. and Low K. S. (eds.): Urbanization and Ecodevelopment, University of Malaya, pp178-185. Dickson R. R., 1960, Meteorological factors affecting particulate air pollution of a city. Bulletin of American Meteor. Soc. 42: 556-560. Elsom D. M., 1978, Meteorological controls upon ground level concentrations of smoke and sulphur dioxide in two urban areas of the United Kingdom. Atmos. Environ. 12:1543-1554. Kerker M. and Hanel V., 1974, Scavenging of aerosol particles by falling water drop and calculation of washout coefficient. Journal of the Atmos. Scien. 31:1368- 1376. Sham S., 1979, Aspect of air pollution climatology in a tropical city. A case of Kuala Lumpur- Petaling Jaya, Area Malaysia. Bangi, UKM press, Malaysia. Yassen M. E., 2000, Analysis of climatic conditions and air quality observations in Kuala Lumpur and Petaling Jaya, Malaysia, during 1983-1997. MPhil Thesis