USING INDICATIVE METHOD FOR OZONE CONCENTRATION PREDICTION MODEL (CASE STUDY: DKI JAKARTA)

• Main Author: Bella Haqi, Verdina
• Format: Theses
• Language: Indonesia
• Subjects: Teknik saniter dan perkotaan; teknik perlindungan lingkungan
• Online Access: https://digilib.itb.ac.id/gdl/view/47025
• Institution: Institut Teknologi Bandung

Air pollution is currently one of the issues of concern in developing countries and affecting the health of world population. One of the pollutants that can be attributed to human respiration diseases and death is ozone. Ozone in the the troposphere is a reactive and irritating gas that can cause negative impacts on human health, climate, and the environment. The main source of ozone forming in the troposphere is the photochemical gases formed by human activities such as VOC and NOx as well as ozone derived from the stratosphere layer. Recognizing the negative impacts caused by ozone pollutants, it is necessary to measure ozone in the ambient air. The ozone pollution monitoring method aims to reduce cases and effects due to exposure to pollutants to protect human health, ecosystems, and climate in urban areas. The measurement using the reference method is a method with accuracy that has been recognized such as that of automatic method of the Air Quality Monitoring System (AQMS). This method require expensive electrical pumps, calibration, operational systems, and maintenance. On the other hand, there are indicative methods such as passive sampler to measure the pollutants concentration with tools that are more economical, efficient, and do not require any electricity or human resources trained to operate it. Reference method that requires complex and expensive equipment can be substituted by a method which simple and easy to operate like indicative method. The main objective of the study is to make a statistical model to predict the value of the annual ozone concentration using passive sampler. Passive sampler monitoring was conducted in the province of DKI Jakarta at three locations that have AQMS including DKI-1 (Bundaran HI), DKI-2 (Kelapa Gading), and DKI-3 (Jagakarsa) for one year. The results of the expected research is that there is to estimate the ozone concentration measured by the AQMS, using the passive method measurement as a proxy to determine the amount of ozone concentration for validation and predictive models. The results of descriptive analysis based on the concentration graph showed that, passive sampler tends to overestimate that of automatic method at Bundaran HI as roadside area of 82%, Kelapa Gading as urban area of 63%, and Jagakarsa as the suburban area of 57%. The precision ranged between 49.58-84.53% with varying accuracy at each point. The value of the accuracy was seen from the value of Root Mean Squared Error (RMSE) of the measured data using reference method and indicative for one year. The results showed that Bundaran HI has the accuracy 267.15 µg/m3, Kelapa Gading 224.1 µg/m3 and Jagakarsa 182.89 µg/m3. The data were tested for normality, transformed, and correlation between passive ozone concentrations to that of active and their relationship to meteorological conditions and precursors were analyzed. Bundaran HI has a prediction model for passive sampler concentration at a range of concentration of 0-400 ?g/m3 with equation 1/O3act=(0.04067±0.005291(p=9.19x10-9))+(0.0001662.O3pas±0.00006887(p=0.02174)) +(-5.47x10-7.O3pas2±0.0000001843(p=0.00558)) and with a value of Adjusted R2 by 20%. The ozone concentration in Kelapa Gading is predicted to use model passive sampler with equation O3act=(0.01038±0.1012)(p=5.54x10-11)+(-0.3445O3pas±0.1413)(p = 0.0214)+(0.0009125.O3pasif2±0.0003822)(p=0.024) with an Adjusted R2 of 6.7%. Jagakarsa has a predictive model that can be used for the concentration of passive sampler in the range of concentration 1-300 ?g/m3 with equation O3act=(84.3825±6.053(p=2.19x10-14))+(0.219.O3pas±0.1198(p=0.0771))+(-0.0009599.O3pas2±0.0004464(p=0.04)) with R2 of 10.31%. A very small adjusted value of R2 were found due to interference during the monitoring process such as meteorological conditions and the concentration of a precursor of disruptor such as NO.