Abstract
As air pollution characterized by fine particulate matter has become one of the most serious environmental issues in China, a critical understanding of the behavior of major pollutant is increasingly becoming very important for air pollution prevention and control. The main concern of this study is, within the framework of functional data analysis, to compare the fluctuation patterns of PM2.5 concentration between provinces from 1998 to 2016 in China, both spatially and temporally. By converting these discrete PM2.5 concentration values into a smoothing curve with a roughness penalty, the continuous process of PM2.5 concentration for each province was presented. The variance decomposition via functional principal component analysis indicates that the highest mean and largest variability of PM2.5 concentration occurred during the period from 2003 to 2012, during which national environmental protection policies were intensively issued. However, the beginning and end stages indicate equal variability, which was far less than that of the middle stage. Since the PM2.5 concentration curves showed different fluctuation patterns in each province, the adaptive clustering analysis combined with functional analysis of variance were adopted to explore the categories of PM2.5 concentration curves. The classification result shows that: (1) there existed eight patterns of PM2.5 concentration among 34 provinces, and the difference among different patterns was significant whether from a static perspective or multiple dynamic perspectives; (2) air pollution in China presents a characteristic of high-emission “club” agglomeration. Comparative analysis of PM2.5 profiles showed that the heavy pollution areas could rapidly adjust their emission levels according to the environmental protection policies, whereas low pollution areas characterized by the tourism industry would rationally support the opportunity of developing the economy at the expense of environment and resources. This study not only introduces an advanced technique to extract additional information implied in the functions of PM2.5 concentration, but also provides empirical suggestions for government policies directed to reduce or eliminate the haze pollution fundamentally.
Highlights
With the rapid development of industrialization and urbanization in China, haze pollution characterized by particulate matter smaller than 2.5 μm occurs more frequently and widely, which has seriously endangered the physical and mental health of residents, and threatened the sustainable development of China’s economy
Since China’s State Council released the “Air Pollution Prevention and Control Action Plan” in September 2013, which was a milestone for reducing PM2.5 concentrations, local governments have promulgated their own air pollution control action plans
PM10 and PM2.5 concentration data collected from five air-quality monitoring sites in Lanzhou from October 2014 to October 2015, Guan et al investigated the primary transport path using Hybrid Single Particle Lagrangian Integrated Trajectory Model (HYSPLIT) and the PM2.5-to-PM10 ratio model [21]
Summary
With the rapid development of industrialization and urbanization in China, haze pollution characterized by particulate matter smaller than 2.5 μm occurs more frequently and widely, which has seriously endangered the physical and mental health of residents, and threatened the sustainable development of China’s economy. Since PM2.5 concentrations always change with time and fluctuate diversely across regions, intensive studies have been carried out on interpreting the spatial and temporal variability of PM2.5 concentrations in China, both from city-level and national-scale perspectives. PM10 and PM2.5 concentration data collected from five air-quality monitoring sites in Lanzhou from October 2014 to October 2015, Guan et al investigated the primary transport path using Hybrid Single Particle Lagrangian Integrated Trajectory Model (HYSPLIT) and the PM2.5-to-PM10 ratio model [21] In these studies, all model constructions and empirical results were based on discrete and equal-sampled observations without any error disturbance. The empirical results is helpful for enhancing the recognition of the spatial distributions and dynamic changes of PM2.5 concentrations in China, and can provide quantitative support for governments to formulate and implement air pollution prevention and control measures
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