A prediction model of polycyclic aromatic hydrocarbon quarterly emissions based on GDP from a government policy perspective

Abstract

Polycyclic aromatic hydrocarbons (PAHs) are a kind of carcinogenic, teratogenic and mutagenic pollutants that exist widely. In this study, a quarterly emission inventory of 16 PAHs listed as the US EPA priority pollutants was established by using the emission factor (EF) method. The results showed that the distribution of PAHs in different industries varied greatly with different year and quarter. The main characteristics of PAHs seasonal emission are that Q1 emission is the most and Q4 emission is the least. Among them, naphthalene and phenanthrene are the most important compounds. There are significant differences in PAHs emissions from different sources. Traffic and civil use emissions are the main sources. For example, in 2019, traffic emissions accounted for 20.9% and civil use emissions accounted for 78.3% of the total emissions. Then, a linear multiple regression model was established to predict the quarterly emission of PAHs. A vector autoregressive (VAR) approach was applied to analyse the correlation between the gross domestic product (GDP) and other macroeconomic parameters. This study could be used as a guide to provide recommendations for government policy and macro-control efforts.