Regression analysis and forecast study of particulate matter emission trends in foreign countries / Muhammad Syarifuddin Ahmad Zahari

Particulate matter is defined as suspended particles in the atmospheric air and existed in 2 major types which are identified by the sizing group, namely PM10 and PM2.5 particles. These particles bring with them adverse effects that do not just affect humans, but also the surrounding environment if they are exposed for more than the safe limit concentration of the elements present inside the particulate matter. Types of particulate matter ranges from coarse solids such as fumes and smog particles from coal combustion to sulphur and nitrogen derivatives such as oxides of sulphur and nitrogen. The adverse effects that may occur can lead to inflammation of the thoracic cavity, bronchitis, alteration of genomes in human neurons for affected fetus and can also cause death. In order to estimate on the particulate matter emission, regression analysis is used to identify the emission trends and predict the outcome of the concentration of emissions in the future. Regression is extensively used in order to identify lethal dosage of particulate matter and the trends of effects of particulate matter in a certain area. However, the use of regression mostly focuses on a certain very specific and intrinsic cases and are rarely used to estimate trends in a country-scale. In this research, regression analysis is conducted to in order to analyze the trends of particulate matter emissions in the United States of America, and to forecast the data until 2020 to estimate the future concentrations of particulate matter in the country and to provide mitigation options to control the emissions of particulate matter. From the research, the trends shows and exponential decrease for both PM10 and Pm2.5 particles for both present and forecasted data. Mitigations options provided include the use of clean energy sources such as natural gases and the implementation of hybrid technologies to reduce usage of fossil fuel combustion.