TEMPORAL VARIATION ANALYSIS FOR PREDICTING PM2.5 ANNUAL MEAN CONCENTRATION

• Main Author: Zahara, Annisa
• Format: Theses
• Language: Indonesia
• Subjects: Teknik saniter dan perkotaan; teknik perlindungan lingkungan
• Online Access: https://digilib.itb.ac.id/gdl/view/80344
• Institution: Institut Teknologi Bandung

Monitoring is a fundamental component of air quality management. Fine particulate matter (PM2.5) has gained significant attention among air pollutants due to its destructive effects on both the environment and human health. The determination of annual average concentrations is necessary to understand long-term exposure in relation to the chronic impacts of PM2.5 and to assess air quality in each regency/city through the Air Quality Index (AQI). However, due to various technical and resource constraints, monitoring is often not fully conducted 24-hour for daily average and 365 days for annual average. This condition potentially results in data that do not represent the actual daily and annual value. The objectives of this paper are to elaborate the temporal characteristics of PM2.5 concentration and to build a statistical model for estimating daily average concentrations from less than 24 hours measurement and annual average concentrations from incomplete monitoring days in a year. Predictions were made by simulating the limitations in PM2.5 concentration data, developing correction factors based on concentration ratios, and measuring the mean error of estimated values compared to actual values. Validation results indicate that the minimum duration for representative daily average measurements is 16 hours with concentration ratio 0.952 ± 0.0036 (MAE 3.39 µg/m3 ; MA 89.63%), and the minimum number of monitoring days for predicting the annual average is 24 days, 2 daily data per month with concentration ratio 1.023 ± 0.070 (MAE 3.97 µg/m3 ; MA 88.59%). Predictions clearly demonstrate an increase in error values as the number of monitoring days decreases in the calculation of annual averages.