Modeling and Estimation of Air Pollutants from Vehicles in Yangon, Myanmar with Google Traffic Map

摘要

Air pollution is one of the most serious issues around the world. According to World Health Organization, air pollution causes 4.2 million deaths per year. Particularly in recent years, PM 2.5, which is a micro substance whose diameter is less than 2.5 micro meter, has been drawing attention because it has serious health impact. PM 2.5 often includes harmful substances. When you draw PM2.5 into lungs, it is absorbed deep into the lungs and blood and causes respiratory diseases and lung cancer, myocardial infarction, stroke. According to the Health Effect Institute, it was estimated that 6 million people were dead because of longtime absorption of polluted air. In Yangon, which is the largest city in Myanmar, the amount of PM2.5 sharply increased in recent years. The traffic volume has also increased in Yangon, so it is considered that the growth in traffic volume is one of the cause of increment in air pollution. To solve the problem, it is important to evaluate the detailed distribution of PM2.5 from traffic and the relationship between PM2.5 and health hazard. We estimated the amount of PM2.5 discharged from road traffic. So far, various studies related to traffic flow and air pollution have been conducted. For example. "High Resolution Air Pollution Assessment System for Road Transport Policy Evaluation" conducted by Kuwahara. However, there is no method which observes the large scale real time PM2.5 distribution exhausted from traffic at short interval. We attempted to estimate the traffic flow by using traffic data of Google traffic map, which shows the traffic speed on each road section. Processing the information, we tried to estimate the volume of traffic. Moreover, combining it with the amount of PM2.5 of each car, the average amount of PM2.5 in every hour was estimated. In the end, in order to confirm the validity of the result, compared it with the result from field survey which we carried out in Yangon. The estimated value is lower than the surveyed value. The reason is considered that the estimated value did not include the background value.