Exposure to particulate matter and gaseous pollutants during cab commuting in Nur-Sultan city of Kazakhstan

Highlights

• PM₁ was found to be the major pollutant among all sizes of PM.

• CO concentrations met the European Commission and EPA standards.

• Combustion sources and power plants were the potential major sources.

• Astana is a great example for city developments regarding commuter exposure.

Abstract

Exposure to particulate matter, carbon dioxide, and carbon monoxide inside a car during commuting were determined during the period October–November 2017 in Nur-Sultan, Kazakhstan. We choose to follow five bus routes (#10, 18, 19, 37 and 53) that cover the majority of the city's area. CO (ppm), CO₂ (ppm) and PM₁, PM_2.5, PM₄, and PM₁₀ mass concentration (μg/m³) were measured in this study. PM₁ was found to be the largest fraction of all sizes of PM. The mean PM₁ concentrations along the forward (backward) paths for each of the five bus routes were measured as 11 ± 14 (11 ± 7), 14 ± 8 (16 ± 6), 25 ± 11 (21 ± 14), 23 ± 8 (15 ± 6) and 76 ± 26 (99 ± 55) μg/m³, respectively. Average CO concentrations among five bus routes (#10, 18, 19, 37 and 53) along the forward (backward) paths were 0.67 ± 0.16 (0.78 ± 0.17), 0.7 ± 0.16 (0.53 ± 0.32), 1.04 ± 0.01 (2.3 ± 0.95), 2.67 ± 1.3 (2.03 ± 0.41), 3.54 ± 3.57 (2.17 ± 0.37) ppm. The mean PM₁/PM_2.5 and PM_2.5/PM₁₀ ratios were 0.96 and 0.91, respectively. Nur-Sultan could be an example for those cities that are under major developments and candidates to be green cities by showing the exposures to atmospheric pollutants across the city. Those cities that are developing themselves as tourist attractions should create maps of PM exposures along major urban routes, and route traffic to exclude tourist areas from being hotspots.

Introduction

Globally, the exposure to ambient particulate matter (PM) is associated with 4 million deaths (accounting for 7.5% of all deaths) in 2016 (Guo et al., 2018). Only in Europe, the PM_2.5 exposure was the reason for 432,000 premature deaths in 2012 (EEA, 2015). Air pollution related to traffic is a complex mixture of particles and gaseous compounds that can directly come from the tailpipe exhaust, tire wear and brake, resuspension of road dust, and secondary aerosols (formed through chemical and physical processes) (Viana et al., 2008; Thorpe and Harrison, 2008; Amato et al., 2009; Bahreini et al., 2012). In a close proximity to traffic, commuters can face higher concentrations (Kumar and Goel, 2016; Kumar et al., 2018; Rivas et al., 2017a; Rivas et al., 2017b; Kumar et al., 2014) particularly during the morning rush hour (Moreno et al., 2009; Gómez-Perales et al., 2007). Exposure to highly variable concentrations of atmospheric pollutants including short-time extreme concentrations during commute may contribute 12–35% of total daily air pollution exposure for commuters (Williams and Knibbs, 2016; Rivas et al., 2016). Furthermore, the transport mode can affect the exposure during the commute. Moreno et al. (2015a, 2015b) reported particle number concentrations (PNC) were ordered as: urban background < underground < tram < walking in a suburban main road < walking and cycling in the city center < bus. However, this order might be different for other pollutants since the highest PM mass concentrations were reported in the subways (Martins et al., 2016a; Adams et al., 2001). In Barcelona, PM_2.5 concentrations were higher in cars compared to PM_2.5 concentrations in buses (De Nazelle et al., 2012) similar to what was found in Arnhem (Zuurbier et al., 2010). In London and Dublin, the reverse situation was reported (Adams et al., 2001; McNabola et al., 2008). The commuter exposure inside each transport mode (car, train, or bus) is influenced by the ventilation system and fuel type (Karanasiou et al., 2014). Given different conditions (like fuel type and ventilation rates) in each transport mode, it is challenging to compare the pollutant concentrations among different studies (Karanasiou et al., 2014; Goel and Kumar and Gupta, 2016a; Kaur et al., 2007) because of the substantial differences in the measurement technologies employed. Knibbs et al. (2011) reported that ventilation rates is a key factor for the transport of the ambient ultra fine particles (UFPs) into the cabin (Knibbs et al., 2011) such that higher ventilation rates increases the penetration of outdoor particles into the bus and cars (Karanasiou et al., 2014; Zuurbier et al., 2010). On the other hand, filtration system of the vehicles can reduce the penetration of outdoor particles into the cabin (Briggs et al., 2008).

In diesel cars and buses, PM₁₀ exposures and median particle concentration were higher compared to electrical buses (Zuurbier et al., 2010). In addition, between vehicles of different manufactures, it is challenging to separate the effect of fuel type from those caused by differences in ventilation system in standard settings (Karanasiou et al., 2014; Knibbs et al., 2009). The utility of Euro emission standards including Euro 6 in petro- and diesel-fueled vehicles has shown to reduce the level of the exposure to PM in cabins, compared to Euro 0–2 or Euro 0–4, respectively (Campagnolo et al., 2019). Also, installation of diesel particulate filters (DPFs) resulted in reductions in in-cabin PM exposure (Campagnolo et al., 2019).

Other variables such as wind speed, vehicle speed, vehicle air conditioning (filtration), and air exchange rate of the vehicle can affect the passenger exposure to gases and PM (Onat and Stakeeva, 2013; Harik et al., 2017; Ham et al., 2017). In London, Adams et al. (2001) showed that the route choice and wind velocity influenced the personal PM_2.5 concentrations during commuting by bus, car, and bicycle, while transport mode did not have a significant impact. Kaur and Nieuwenhuijsen (2009) reported 62% variations in PNC, due to the influence of temperature and wind speed, transport mode, and traffic counts, as the significant predictor variables. Also, only 9% variation in PM_2.5 concentration was observed to be related to the transport mode with little traffic intensity effect. Wind speed and temperature were significant determinants of PNC during walking and trips by bus and automobile in Montreal (Weichenthal et al., 2008). Rivas et al. (2017a) investigated commuter exposure to particulate matter (PM₁, PM_2.5, and PM₁₀), black carbon (BC), and ultrafine particle concentrations for different travel modes along four commuting routes using four different transport modes (car, bus, walk, and subway) in London. Wind speed and ambient concentrations mainly explained the changes in concentration. The commuting route and period of the day had minor influences. The wind speed had the largest effect on PM fractions during the trips by bus (Rivas et al., 2017a).

Recent studies have shown that in-cabin exposure to PM, carbon monoxide, and carbon dioxide has increased because of prolonged traveling times in cars (Harik et al., 2017). They reported that self-pollution coming from the car's engine accounts for approximately 15% and 30% of the commuter's exposure to CO and PM_2.5, respectively.

Commuters can be exposed to carbon monoxide resulting from the incomplete combustion in the vehicle engines including private cars, buses, trucks, and motorcycles (Barbulescu and Barbes, 2017; Harik et al., 2017). Thus, traffic congestion or high-speeds can lead to higher CO emissions. Congestion-related exposures occur during the morning and evening rush hour periods (Kim et al., 2015). A systematic mobile monitoring campaign was conducted to assess personal exposures to particulate matter (PM) on a pre-defined route in four transport microenvironments (car, cycle, bus, and walk) (Kumar et al., 2018). Due to the limited physical activity, in car exposures were the lowest and had commensurately low respiratory deposition doses (RDD) for coarse particles. Despite the high concentrations of fine particles, car exposures produced the lowest RDD (Kumar et al., 2018).

Yan et al. (2015) suggested that it is possible to reduce in-cabin exposure to CO and PM_2.5 by decreasing vehicle's speed and setting the air conditioner to its recirculation mode (Yan et al., 2015). Yan et al. (2015) found that air-conditioned buses had higher in-cabin PM concentrations compared to non-AC transports in wintertime due to the presence of the engine exhaust in the cabin to provide heat. Qiu et al. (2017) studied exposure to PM in buses between May and June 2016 in Xi'an, China. Two types of buses: AC (closed window) and non-AC (open window) were studied. In the summertime, Qiu et al. (2017) observed that AC type of buses had higher PM concentrations inside the bus compared to non-AC buses due to the transport of the outdoor PM into the cabin.

Additionally, when the AC was on, coarse particles were removed by the filter inside the AC. Higher PM concentrations were observed in areas where transportation and constructions activities were heavily involved (Basagaña et al., 2017). However, the relative importance of these two primary urban sources with respect to commuting exposure needs to further investigation.

Kazakhstan is the ninth largest country by land area in the world and is the focal point of Central Asian countries with low populations (ASRK, 2005). In 2014, Kazakhstan was ranked as the second largest residential coal consumer per capita in the world (Kerimray et al., 2017). Nur-Sultan is the capital city of Kazakhstan located in the north of the country with the population of about 1 million. Nur-Sultan typically experiences a severely cold climate in winter time such that in January 2018, the ambient temperature reached approximately −40 °C. Nevertheless, in summertime, the temperature may reach 30 °C leading to increased traffic emissions and the highest NO₂ concentrations (Darynova et al., 2018). The number of registered motor vehicles in Nur-Sultan increased to 285,000, a 5.3% increase over previous years (Administrative Police Department, 2017). Nur-Sultan is divided into two zones including the east side (old city) and the west side (new city-under the development). Unlike the available studies, the present study assessed the impact of the city development as one of the socio-economic factors on PM and CO exposures during commuting.

The objectives of this study were to compare the exposure to PM while commuting in the old part of the city with the new part of the city since the old part is dominated by transportation and residential coal burning pollution while the new part of the city experiences construction pollution and to some extent traffic pollution and also to identify the factors influencing the temporal variability of particles in different parts of city.