Modelling of Tropospheric Ozone Concentration in Urban Environment

Nadiyatur Rahmatikal Wasi’ah, Driejana Driejana

Abstract


Tropospheric ozone is harmful to human health and plants. It is resulted from photochemical processes involving NOx and VOCs from reactions of motor vehicle emissions and solar radiation in polluted urban environment. Historical data in Jakarta indicated that ozone concentrations often exceeded ambient standard threshold.  To minimize its impact to human health it is important to predict its concentration. This paper reports the use of multivariate statistical method to predict ozone concentration, using precursor concentration and meteorological parameters.  CH4, CO, NMHC, NO, NO2, THCdata concentration, wind direction and speed, temperature, solar radiation and relative humidity during 2011 - 2012 were used to build the model. Multiple linear regressions were applied to predict ozone concentration at Thamrin Station, Jakarta. These data were used as predictors at time (t) to estimate the ozone concentration at time (t +1). Meteorological conditions were found to strongly affect the concentration of ozone. The strongest relationship was found between ozone and temperature (0.513, p = 0.000). Weaker but significant positive correlations were found for  solar radiation and NO2 (r = 0.242, p= 0.000),. NMHC and NO correlation (r= 0.353, p= 0.000).  Both NO and NMHC are freshly emitted from exhaust gas.  Correlations between humidity, wind speed and direction were negative. Methana, NMHC, were negatively correlated with ozone due to their roles for producing NO2 as the main precursor, while NO was for its scavenging reaction with O3. Based on Adjusted R2 value, all predictors could explain variation in ozone concentration of approximately 46.32%. These findings will be useful as input in urban transportation planning and management in cities with tropical climate like Indonesia, as all precursors are emitted from vehicle combustion.

Keywords


urban air pollution; sustainable city; tropical climate; urban planning; air quality modeling

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DOI: http://dx.doi.org/10.12962/j23546026.y2017i6.3279

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