Temporal characteristics and forecasting of PM2.5 concentration based on historical data in Houston, USA

Abstract Urban PM2.5 has been recognized as a critical type of air pollutions that could damage the human respiratory system. Numerous studies have aimed to perform spatiotemporal assessment on diverse urban areas and proposed various PM2.5 forecasting models. Geographical variations and the inadequacy of hourly PM2.5 concentration forecasting are the main limitations of previous researches. The objectives of this paper are to elaborate the temporal characteristics of PM2.5 concentration of each geographic region in Houston, Texas, USA, from inland rural to coastal areas, and to build a forecasting model that could perform daily and hourly PM2.5 predictions. This research analyzed historical PM2.5 concentration data from 2008 to 2017 collected from five regions in Houston by the Texas Commission on Environmental Quality (TCEQ). Temporal characteristics calculation equations were provided. Analysis results revealed several important temporal characteristics of PM2.5 concentrations in Houston. The annual average daily PM2.5 concentration showed decreasing trends from 2008 to 2017. Higher PM2.5 concentration levels were observed in summer months than winter months. In downtown area the PM2.5 concentration levels were higher on weekdays than weekends. In rural, suburban, residential, and coastal areas the PM2.5 concentration levels were lower on weekdays than weekends. Moreover, PM2.5 concentration levels were significantly higher during traffic rush hours than other time intervals. The accuracy and precision of forecasting have been improved based on a combination of factors compared to the forecasting based solely on day-of-week factors (DWF), hourly concentration factors (HCF), and monthly concentration factors (MCF). Advantages of such combined forecasting include lowering calculation complexity and providing hourly concentration forecasting. Look-up tables for each temporal characteristic factor were provided that could be easily applied for further forecasting.

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