Using a hybrid approach to apportion potential source locations contributing to excess cancer risk of PM_2.5-bound PAHs during heating and non-heating periods in a megacity in the Middle East

Abstract

Polycyclic aromatic hydrocarbons (PAHs) represent one of the major toxic pollutants associated with PM_2.5 with significant human health and climate effects. Because of local and long-range transport of atmospheric PAHs to receptor sites, higher global attentions have been focused to improve PAHs pollution emission management. In this study, PM_2.5 samples were collected at three urban sites located in the capital of Iran, Tehran, during the heating and non-heating periods (H-period and NH-period). The US EPA 16 priority PAHs were analyzed and the data were processed to the following detailed aims: (i) investigate the H-period and NH-period variations of PM_2.5 and PM_2.5-bound PAHs concentrations; (ii) identify the PAHs sources and the source locations during the two periods; (iii) carry out a source-specific excess cancer risk (ECR) assessment highlighting the potential source locations contributing to the ECR using a hybrid approach.

Total PAHs (TPAHs) showed significantly higher concentrations (1.56–1.89 times) during the H-period. Among the identified PAHs compounds, statistically significant periodical differences (p-value < 0.05) were observed only between eight PAHs species (Nap, BaA, Chr, BbF, BkF, BaP, IcdP, and DahA) at all three sampling sites which can be due to the significant differences of PAHs emission sources during H and NH-periods. High molecular weight (HMW) PAHs accounted for 52.7% and 46.8% on average of TPAHs during the H-period and NH-period, respectively. Positive matrix factorization (PMF) led to identifying four main PAHs sources including industrial emissions, petrogenic emissions, biomass burning and natural gas emissions, and vehicle exhaust emissions. Industrial and petrogenic emissions exhibited the highest contribution (19.8%, 27.2%, respectively) during the NH-period, while vehicle exhaust and biomass burning-natural gas emissions showed the largest contribution (40.7%, 29.6%, respectively) during the H-period. Concentration weighted trajectory (CWT) on factor contributions was used for tracking the potential locations of the identified sources. In addition to local sources, long-range transport contributed to a significant fraction of TPHAs in Tehran both during the H- and NH-periods. Source-specific carcinogenic risks assessment apportioned vehicle exhaust (44.2%, 2.52 × 10⁻⁴) and biomass burning-natural gas emissions (33.9%, 8.31 × 10⁻⁵) as the main cancer risk contributors during the H-period and NH-period, respectively. CWT maps pointed out the different distribution patterns associated with the cancer risk from the identified sources. This will allow better risk management through the identification of priority PAHs sources.

Introduction

Rapid urbanization is one of the most urgent challenges in the last decades (Lafortezza and Sanesi, 2019). Under this view, scientists around the world from a wide range of disciplines have designed research and investigated the effects of unsustainable urbanization development on various socio-economic and environmental factors, such as social inequality, infrastructure development and air and water pollution, and presented how these factors can undermine efforts to promote more sustainable cities and society (Akinwumiju et al., 2021; Bloom et al., 2008; Grimm et al., 2008; Kalnay and Cai, 2003). The fast-growing literature has highlighted the adverse effects of unsustainable development on a range of ecosystem services (ESS) and thus, on human health (Eigenbrod et al., 2011). Moreover, United Nations have also provided the sustainable development goals (SDGs) (UN Sustainable Development Goals (SDGs), 2015), the ways and means for balancing these effects toward sustainability. Climate action is one of the SDGs to take urgent action to combat climate change and its impacts. In this context, protection of the atmosphere and prevention of atmospheric pollution has been attracted a broad and multidimensional endeavor involving various sectors of economic activity.

Among the atmospheric pollutants, PAHs are a suit of ubiquitous and persistent organic pollutants in the urban atmosphere characterized by two or more fused aromatic rings (Li et al., 2016; Matos et al., 2021; Wu et al., 2020). Some of them are well recognized as environmental potential carcinogens and mutagens (Armstrong et al., 2004; Gao et al., 2015; Sei et al., 2021; Shen et al., 2019; Wang et al., 2018). They are predominant toxic pollutants associated with fine particles (PM_2.5, particles with an aerodynamic diameter <2.5 μm) which can cause significant short-term and long-term exposure health risks, especially in developing countries (Akhbarizadeh et al., 2021; Hazarika et al., 2019; Kim et al., 2013; Mehmood et al., 2020; Straif et al., 2006). Exposure to PM_2.5-bound PAHs in ambient air may result in cancers (Armstrong et al., 2004; Han et al., 2020; Kim et al., 2013; Sarigiannis et al., 2015; Shen et al., 2013). Higher lung cancer risk levels due to exposure to PAHs have been reported particularly in developing countries (Straif et al., 2006). Exposure to PAHs may also cause cardiovascular disease and eye irritation and inflammation (Kim et al., 2013). Because of the long-range transport of atmospheric PAHs to receptor sites, higher global attentions have been focused to improve PAHs pollution emission management (Chao et al., 2019; Kim et al., 2013; Sofowote et al., 2011; Wu et al., 2020; Yu et al., 2021; Zhen et al., 2021). PAHs can originate from natural (Harrison et al., 1996; Stanišić et al., 2021; Wu et al., 2020) and anthropogenic sources (Harrison et al., 1996; Huang et al., 2018; Y. Liu et al., 2017b; Polachova et al., 2020; Ravindra et al., 2008a; Zakaria et al., 2002).

It is essential to identify the relative contribution of various sources and potential sources locations to perform efficient health risk management and appropriate controls of PAHs pollution. Several qualitative statistical approaches, including the use of diagnostic ratios (DRs), and quantitative receptor modeling tools, such as positive matrix factorization (PMF), have been applied for identifying PAHs emission sources (Chao et al., 2019; Khan et al., 2015; Larsen and Baker, 2003; Tobiszewski and Namieśnik, 2012; Yunker et al., 2002). Compared with DRs, PMF has multiple advantages (e.g. non-negative constraints, uncertainty profiles and missing values treatment) which has led to the wider use of this technique in source apportionment studies in the world (Hopke, 2016; Paatero and Tapper, 1994). Conversely, dispersion modeling tools such as concentration weighted trajectory (CWT) has been rarely applied for identifying the geographic origins of PAHs pollutants (Liu et al., 2018; Wang et al., 2016; Zhang et al., 2019). Since it is not easy to distinguish PAHs pollutants derived from different sources, the combination of receptor and dispersion models (PMF-CWT) were deemed appropriate for differentiating the contribution of each factor from certain directions of pollutants (Wang et al., 2016; Zhang et al., 2019). To the best of the author's knowledge, a few studies have been conducted for identifying the regional transport pathway of PAHs bounded to PM_2.5 using PMF-CWT approach in the study area until now (Ali-Taleshi et al., 2021).

For the first time, in 1976, 16 priority PAHs was classified by the United States Environmental Protection Agency (US EPA) based on their occurrence and toxicity (Han et al., 2020; U.S. EPA, 2002). Among them, Benzo [a] pyrene (BaP) has been introduced as Group 1 of carcinogenic agents by the International Agency for Research on Cancer (IARC) (IARC, 2016). The focus of the major previous studies was the singular calculation of health risks (Zhang et al., 2019). Therefore, the development of hybrid methods (e.g.: PMF-ECR, positive matrix factorization excess cancer risk) to allocate the contributions of apportioned sources to cancer risk is vital for appropriate health risk management in the study area. Moreover, identification of potential sources locations that contributed to ECR using the PMF-ECR-CWT approach can help local and regional policymakers in the adoption of feasible strategies and effective control measures to improve air quality and the reduction of PAHs pollution at a regional scale. To the best of the authors’ knowledge, the current study is the first that investigates the regional source that contributed to cancer risk in the study area.

In recent years, many studies have shown the periodical variation effects on PAHs pollution in urban environments. Most of these studies focused on the concentrations, sources and meteorological factors influencing PAHs variations (Alghamdi et al., 2015; Callén et al., 2014; Masiol et al., 2013; Teixeira et al., 2015). For example, Zhang and Tao (2009) and Nagy and Szabó (2019) demonstrated that during heating periods (H-periods), besides additional PAHs emissions due to fuel consumption for household heating, stringent meteorological conditions do not allow the dispersion of air pollutants increasing PAHs concentrations compared to the other periods. Also, PAHs tend to be bound to atmospheric particles under cold condition, which supported the high PAHs concentrations on aged particles during heating periods (Stanišić et al., 2021; Venkataraman et al., 1999). These evidences suggested that PAHs concentrations present a more reliable index for air-quality assessment (Amodio et al., 2009). Despite the contributions of the local sources, investigations indicated that the transport of dust particles from remote dust sources are noticeably active during the non-heating periods (NH-periods), especially in the Middle East (Alizadeh-Choobari et al., 2016; Arhami et al., 2017). Moreover, back-wind trajectories studies also showed that the long-range transport of transboundary air pollutants typically observed in the H-periods may partly contribute to elevated particles and components found in the study area and the world (Chen et al., 2016; Javid et al., 2015; Kuo et al., 2013). Thus, because the transport of particles could pose a serious challenge to pollution control and mitigation strategies at a local scale, regional-scale monitoring can be a useful tool to assess source apportionment of ambient PM_2.5. Many studies also pointed out that the health risks in H-period are higher than NH-period (Chen et al., 2017; Sosa et al., 2017; Xia et al., 2013). However, the source-specific cancer risk in different periods has been rarely investigated.

The capital of Iran, Tehran, is home to more than 8.5 million people, with more than 17 million vehicle trips per day by a significant number of vehicles with outdated technology (World Bank, 2018). Tehran suffers from severe atmospheric pollution and has been ranked as the 24th most polluted capital city in the world (PM_2.5 annual average concentration of 25.9 μg m⁻³, IQAir, 2019), which has led to health burdens in recent years, especially during H-period. Besides the high population density, the particular topography, and a unique urban climate, the air quality of this region is further worsened by a complex emission scenario including emissions caused by unsustainable developemnet from urbanized, industrial and agricultural activities (Ali-Taleshi et al., 2021; Esmaeilirad et al., 2020). Until now, few studies reported the chemical composition and sources of PM_2.5 in Tehran with a focus on organic and inorganic elements. Most of them were designed to investigate pollution episodes (Givehchi et al., 2013), specific months or specific locations (Arhami et al, 2017, 2018; Esmaeilirad et al., 2020; Taghvaee et al., 2018a, 2018b) and seldom covering a large area of the city during H and NH-periods.

The current study aims to investigate the periodical variations of PM_2.5 and PM_2.5-bound PAHs concentrations at three urban sites in Tehran. The main goal is to identify the various PAHs sources using DRs and their contributions with PMF models, to extract potential sources origins of PAHs using CWT method, to evaluate source-specific excess cancer risk of PM_2.5-bound PAHs via PMF-ECR method, and to explore potential sources locations contributed to ECR using PMF-ECR-CWT approach.