Long-term trends in PM2.5 mass and particle number concentrations in urban air: The impacts of mitigation measures and extreme events due to changing climates
Graphical abstract
Introduction
Air quality has changed throughout history, but particularly over the past few decades. Elevated concentrations of air pollutants due to industrialisation and urbanisation, in particular, have become a global problem because of their impacts on human health and the environment. To address this problem, local and national authorities in an increasing number of countries have been introducing policies and strategies to mitigate anthropogenic emissions and improve air quality. As a result, improvements in air quality have been observed, for example, in the United States (USEPA, 2019), the European Union (EEA, 2018) and China (Fontes et al., 2017). Conversely, where policies and strategies are not implemented, air quality continues to worsen due to the emissions from an increasing number of local and regional sources, in particular the transportation sector and fossil fuels for energy generation (Al-Taani et al., 2019; Pant et al., 2019).
Airborne particulate matter (PM) is one of the most relevant pollutants to human health, with both short- and long-term exposure linked to increased morbidity and mortality (Atkinson et al., 2010; Tobías et al., 2018). To add to the complexity, the impacts of PM on health are related to the particle size: smaller particles, such as those emitted by combustion sources, have a lower deposition velocity and therefore stay suspended longer in the air (Rose et al., 2012; Schmale et al., 2011); and they also deposit deeper in the respiratory tract causing a range of local and systemic health effects (Fang et al., 2017; Fireman et al., 2017). With the growing understanding of the negative impacts of PM, standards for particle mass concentration have been introduced in many countries worldwide and compliance monitoring of PM2.5 and PM10 has been conducted (mass concentration of particles with an aerodynamic diameter < 2.5 μm and <10 μm, respectively). However, there are no standards, and therefore little monitoring is conducted, for ultrafine particles (UFPs, size <100 nm); although with traffic being a major pollution source in cities around the world, this size fraction of PM may be more significant in terms of health impacts than larger particles of higher mass in urban air (Kumar et al., 2014; Rönkkö et al., 2017). UFPs are measured in terms of particle number concentration (PNC), rather than mass.
An important factor that affects particles of different sizes somewhat differently is meteorology. A changing climate, which in turn affects local and global meteorological parameters, can also have an impact on particle characteristics, irrespective of the impact of changes in the sources. For example, stronger winds will, in general, result in higher resuspension of larger particles, but faster dispersal and thus dilution of smaller particles (Teinilä et al., 2019). On the other hand, colder ambient temperatures with high relative humidity (RH) can increase PNC by favouring nucleation, especially during winter (Jeong et al., 2006; Rönkkö et al., 2006), but higher temperatures with low RH (below 60%) enhance H2SO4 levels in the air, promoting new particle formation (An et al., 2015; Birmili & Wiedensohler, 2000; Hamed et al., 2011).
Smaller and larger particles in the air typically originate from different sources and, therefore, require different mitigation strategies. Conversely, mitigation strategies have different impacts on particles of different sizes; therefore, the concentration trends could differ between PM2.5 and PNC as evidenced by the experience in Eastern Germany after the German reunification in 1990 (Kreyling et al., 2003; Pitz et al., 2001). A comprehensive review of the measurement metrics, source apportionment, health effects and legislations on PM by Heal et al. (2012) revealed that controlling PM2.5 and PM10 resolves only a part of the problem, and does not necessarily address the problem of UFPs. Therefore, our study of the long-term trends of both PM2.5 and PNC further illustrates that monitoring and characterising air quality in terms of PM mass concentrations only, without conducting any monitoring of PNC, might be insufficient given that the sources and drivers for PM2.5 and PNC differ, as well as their impacts relating to human health.
Long-term studies of PM2.5 and PNC have shown that the impacts of emission control strategies and policies can be either a steady decrease or a step change. For example, the consistent decrease of PM2.5 in Seoul, South Korea, in the period from 2004 to 2013 can be partially explained by the implementation of several emission reduction strategies such as the use of natural gas as a bus fuel and the installation of emission control retrofits (Ahmed et al., 2015). However, an abrupt reduction in PNC was observed when London, England, introduced sulphur-free diesel fuel and a traffic pollution charge scheme for heavy goods vehicles in 2007 (Jones et al., 2012). Trend analysis for PM metrics is commonly done by using simple linear regression such as the Theil-Sen method to obtain the slope that quantifies gradual changes. However, this cannot capture significant patterns in time series data as effectively as curve fitting by applying smoothing functions. Moreover, doing a time series decomposition prior to analysis to separate trend, seasonality, and noise components are more precise when specific attribution is desired.
Considering the need to understand the effectiveness of mitigation measures on controlling particles in the air, but with the backdrop of other changes occurring, in particular climatic changes that will affect meteorological parameters, the aims of this work were to: (1) determine the long-term trends of PM2.5 and PNC in cities using time series analysis; (2) evaluate the impact of changes in climate (based on key meteorological factors, after removing seasonality) on the observed trends of PM2.5 and PNC; and (3) investigate whether the observed changes in PM2.5 and PNC can be attributed to modifications in the operation of anthropogenic sources. Analysis of long-term trends in concentration changes using both PM2.5 and PNC can provide an understanding of the magnitude of changes and of the factors that influenced their ambient concentrations; in particular, the efficiency of human interventions (e.g. changes in technology or fuels and the impact of new regulations). This information can provide a more complete picture for policy makers and state leaders to design a more effective and efficient regulatory approach.
Section snippets
Material and methods
The criteria for inclusion of data in this study were: (1) measurements of PM2.5, PNC and the selected meteorological parameters (mean air temperature, total precipitation and mean wind speed) performed for at least 10 years; (2) PM2.5, PNC and the selected meteorological parameters to be collected concurrently and from the same location or in proximity; and (3) measurements to be recorded at monthly resolution or higher. Data acquisition was done through convenience by connecting with
Results and discussion
The amount of missing data in each city for the period covered in this study varied from 0 to 7.5% for PM2.5, 7–11.0% for PNC except for the 30% in London-RS, 0–6.6% for temperature, 0–7.9% for precipitation except for the 45.5% in Augsburg, and 0–6.6% for wind speed. The PNC data for Brisbane, on the other hand, were only from 1998 to 2000 and 2011 to 2015. Despite this limitation in available data, we could still use them to derive important information about long-term variations in PM
Conclusions
The long-term trends in PM2.5 and PNC in five cities in Australia, Europe, and the United States were assessed with regard to the changing climates and regulatory policies. However, determining which of the factors affecting PM concentration took effect in a particular event is complicated because of the complex dynamics of pollution formation and transport. Both PM2.5 and PNC declined in all cities except Brisbane for the course of the study, with a greater magnitude of reduction for PNC. In
CRediT authorship contribution statement
Alma Lorelei de Jesus: Conceptualization, Methodology, Software, Formal analysis, Writing - original draft, Visualization. Helen Thompson: Methodology, Software, Writing - review & editing. Luke D. Knibbs: Conceptualization, Writing - review & editing. Michal Kowalski: Writing - review & editing. Josef Cyrys: Resources, Writing - review & editing. Jarkko V. Niemi: Resources, Writing - review & editing. Anu Kousa: Resources. Hilkka Timonen: Resources, Writing - review & editing. Krista Luoma:
Declaration of interests
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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- 1
School of Environmental Science and Management, University of the Philippines Los Baños, College, Laguna, Philippines.
- 2
Department of Environmental Sciences/Centre of Excellence in Environmental Studies, King Abdulaziz University, PO Box 80203, Jeddah, Saudi Arabia.
- 3
Centre for Air Resources Engineering and Science, Clarkson University, Potsdam, New York, United States of America.