DATA SONIFICATION OF AIR QUALITY IN JAKARTA TRAIN STATIONS INTO CHORD PROGRESSION AND ITS POTENCY AS A DETECTION TOOL FOR AIR QUALITY

• Main Author: Fauzan, Muhammad
• Format: Theses
• Language: Indonesia
• Online Access: https://digilib.itb.ac.id/gdl/view/57503
• Institution: Institut Teknologi Bandung

The Air Pollutant Standard Index (ISPU) is currently used to report air quality to the public to show pollutant level and how it affects health after inhaling the air. ISPU is determined based on the parameters, such as Particulate Matter (PM) 10, Sulphur Dioxide (SO2), Nitrogen Dioxide (NO2), Ozone (O3) and Carbon Monoxide (CO). According to the Ministry of Environment and Health (KLHK), there are five categories of air quality based on PM 10, SO2, NO2, and CO parameters: good (range 0-50), moderate (range 51-100), unhealthy (range 101 -199), very unhealthy (range 200-299), and dangerous (range 300 and above). Each category is coloured green, blue, yellow, red, and black, respectively. However, ISPU uses visual data that understandable only for people who do not have visual disabilities. The existing ISPU data does not include data on aerial microbes such as bacteria, fungi, and viruses. There are two ways of elucidating the Pollutant Standard Index: visual or audial form. Data sonification converts visual data into sound that is reasonable for musician's ears, while still maintaining the validity of the data. The conversion of visual data into audial data is called data sonification. This study aims to convert data on the number of microbes in the air into chord progressions that easy to understand for both the general public and people with disabilities. The conversion of air microbial count data into audial data is carried out based on physical and biological parameters. Air samples were taken at eight train stations located in the Jakarta area (Cakung Station, Manggarai Station, Sudirman Station, Jakarta Kota Station, Tanjung Priok Station, Tanah Abang Station, Taman Kota Station) and Bekasi (Bekasi Station). Air samples were taken using an air sampler (active method). The air sample was then preserved in PBS solution at 4 oC. Bacterial enumeration was carried out using the Total Plate Count method on five mediums: Nutrient Agar, R2A Agar, Staphylococcal Agar, Eosin- Methylene Blue Agar, and Salmonella-Shigella Agar. Culture incubation was carried out at 27 oC. The classification of air quality is performed using the following data: temperature, humidity, Total Volatile Organic Compound (TVOC), Particulate Matter (PM) 2.5, PM 10, light intensity, carbon dioxide emissions (ecH2O), total bacteria overall, total pathogenic bacteria, and presence of viruses. Air quality classification is carried out by reducing variables using Principal Component Analysis (PCA), resulting in four classes of air quality based on the number of aerial microbes. Audial data were determined from the number of bacteria and the number of viruses in air samples from stations in Jakarta and Bekasi. Audial data shows the chord formed at each station and collected according to the classification of the number of microbes in the air to form a chord progression. Progression chords are then analysed to see the chord patterns formed for each air quality. Based on ISPU data of the city of Jakarta, on the sampling date (26-29 October 2020), the air quality in Jakarta is categorized as unhealthy (yellow coloured, 26 and 28 October 2020), and moderate (blue coloured, 27 and 29 October 2020). Based on variable reduction analysis, the total bacteria and the copy number of viruses were two independent variables (P = 0). The sample with the highest number of bacteria was Jakarta Malam (JAKM, 24 oC, humidity 57%) 2.55 × 107 CFU/mL, while the least was Bekasi Malam (BKSM, 26 oC and 75% humidity) 1.85 × 105 CFU/mL). The sample with the highest viral copy number was Taman Kota Malam (TKOM, 24 oC and 61%) 1.5 × 104 copy number and the least were Manggarai Malam (MGRM, 25 oC, humidity 73.16%), Sudirman Malam (SUDM, 27 oC, 70% humidity), Manggarai Pagi (MGRP, 26 oC, humidity 73%), Tanjung Priok Malam (TPKM, 29 oC, humidity 63%), and Tanah Abang Pagi (TNBP, 32 oC, humidity 58%) with negative viral copy number. The air quality parameters formed were divided into four quadrants: clean-low risk infection (Q1; the total range of bacteria 1.59 × 106 CFU/mL – 3.48 × 106 CFU/mL; range of pathogenic bacteria (Salmonella and Shigella) negative – 4,15 × 105 CFU/mL; viral copy number range negative – 7.5 × 103 copy number), dirty-low risk infection (Q2; total bacterial range 1.95 × 107 CFU/mL – 2.69 × 107 CFU/mL; range of pathogenic bacteria (Salmonella and Shigella) negative - 2.4 × 107 CFU/mL; negative viral copy number range - 7.5 × 103 copy number), clean-high risk infection (Q3; total bacterial range 1.59 × 106 CFU/mL – 3.48 × 106 CFU/mL; pathogenic bacteria range (Salmonella and Shigella ) negative – 2.76 × 106 CFU/mL; viral copy number range 7.5 × 103 pg – 1.5 × 104 copy number), and dirty-high risk of infection (Q4; total bacterial range 1.95 × 107 CFU/ mL – 2.7 × 107 CFU/mL; range of pathogenic bacteria (Salmonella and Shigella) negative – 2.7 × 107 CFU/mL; viral copy number range 7.5 × 103 pg – 1.5 × 104 copy number). The progression chord of each air quality quadrant is as follows: suspended-suspended-minor-major (Q1), suspended (Q2), minor-suspended-major-major (Q3), and minor-suspended- minor-suspended (Q4). In addition, chord composition analysis was carried out in each quadrant and obtained dominant chords: suspended (Q1), major (Q3), and balanced (Q4). Q3 chord progressions generally have two distinct chords, so they sound discordant. Q1 and Q4 have chords that sound more harmonious. As a result, Q3 has a darker and sinister impression than Q1 and Q4. In addition, Q3 is a quadrant that has the characteristics of clean-high risk of infection. The air quality in Jakarta at the date of sampling was categorized as moderate and unhealthy (ISPU score ranged around 82-117). It can be concluded from this study that the station that has the best microbial quality in the air is the Manggarai station (Q1, clean-low risk infection), and the station that has the worst microbial quality in the air is the Jakarta Kota station (Q4, dirty-high risk infection). This research can be improved by incorporating other air sample data sets into the air quality quadrant model so that the model and chord progression can be more representative and universal.