Long-term trends in PM_2.5 mass and particle number concentrations in urban air: The impacts of mitigation measures and extreme events due to changing climates

Highlights

• Both PM_2.5 and PNC had a monotonic downward trend in all cities except Brisbane.

• Extreme events due to changing climates caused positive step-changes to PM_2.5.

• Negative step-changes in PNC were observed upon regulation of sulphur in fuels.

• Gradual reduction of PM_2.5 and PNC was achieved by traffic and fleet management.

Abstract

Urbanisation and industrialisation led to the increase of ambient particulate matter (PM) concentration. While subsequent regulations may have resulted in the decrease of some PM matrices, the simultaneous changes in climate affecting local meteorological conditions could also have played a role. To gain an insight into this complex matter, this study investigated the long-term trends of two important matrices, the particle mass (PM_2.5) and particle number concentrations (PNC), and the factors that influenced the trends. Mann-Kendall test, Sen’s slope estimator, the generalised additive model, seasonal decomposition of time series by LOESS (locally estimated scatterplot smoothing) and the Buishand range test were applied. Both PM_2.5 and PNC showed significant negative monotonic trends (0.03–0.6 μg m⁻³. yr⁻¹ and 0.40–3.8 × 10³ particles. cm⁻³. yr⁻¹, respectively) except Brisbane (+0.1 μg m⁻³. yr⁻¹ and +53 particles. cm⁻³. yr⁻¹, respectively). For the period covered in this study, temperature increased (0.03–0.07 °C.yr⁻¹) in all cities except London; precipitation decreased (0.02–1.4 mm. yr⁻¹) except in Helsinki; and wind speed was reduced in Brisbane and Rochester but increased in Helsinki, London and Augsburg. At the change-points, temperature increase in cold cities influenced PNC while shifts in precipitation and wind speed affected PM_2.5. Based on the LOESS trend, extreme events such as dust storms and wildfires resulting from changing climates caused a positive step-change in concentrations, particularly for PM_2.5. In contrast, among the mitigation measures, controlling sulphur in fuels caused a negative step-change, especially for PNC. Policies regarding traffic and fleet management (e.g. low emission zones) that were implemented only in certain areas or in a progressive uptake (e.g. Euro emission standards), resulted to gradual reductions in concentrations. Therefore, as this study has clearly shown that PM_2.5 and PNC were influenced differently by the impacts of the changing climate and by the mitigation measures, both metrics must be considered in urban air quality management.

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 PM_2.5 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 PM_2.5 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 PM_2.5 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 PM_2.5 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 PM_2.5 and PNC differ, as well as their impacts relating to human health.

Long-term studies of PM_2.5 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 PM_2.5 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 PM_2.5 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 PM_2.5 and PNC; and (3) investigate whether the observed changes in PM_2.5 and PNC can be attributed to modifications in the operation of anthropogenic sources. Analysis of long-term trends in concentration changes using both PM_2.5 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.