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. 2022 Aug 18;20(2):775–783. doi: 10.1007/s40201-022-00818-x

The links between microclimatic and particulate matter concentration in a multi-storey car parking: a case study iran

Nayereh Rezaei Rahimi 1, Reza Fouladi-Fard 2,✉, Mohammad Rezvani Ghalhari 3, Hasan Mojarrad 2, Ahmadreza Yari 2, Mohammad Mahdi Farajollahi 4, Amir Hamta 5, Maria Fiore 6
PMCID: PMC9672195  PMID: 36406607

Abstract

Multi-storey cars increasing with population growth have excellent security and temporary parking for cars in big cities, which isn’t suitable for parking in the streets. The goals of this study are (1) to determine PM concentrations in the ZGP and (2) to investigate the effect of temperature and humidity on PM concentration in ZGP. This study measured the levels of emitted PM1, PM2.5, and PM10 by GRIMM EDM 107 laser dust monitor in a busy multi-storey parking garage located in Qom. Moreover, the relationship between microclimatic parameters and the contaminants mentioned above was investigated. Samples were collected in two stages in different spatiotemporal conditions, namely, the summer and autumn of 2017. The results indicate that during the sampling period, the daily mean ± standard deviation of PM10, PM2.5, and PM1 were 120.9 ± 90.6, 28.5 ± 10.4, and 10.8 ± 3.8 µg/m3, respectively. A decrease in pollution level was observed during the measurement period. During rush hours, the levels of particulate matter increased. Also, a significant positive relationship between indoor humidity and particle level was observed, while there was a meaningful, inverse relationship between temperature and particle level. The high PM concentration in the parking garage indicates the necessity of proper management and planning.

Graphical Abstract

graphic file with name 40201_2022_818_Figa_HTML.jpg

Keywords: Indoor air pollution, Vertical parking, PM10, PM2.5, PM1

Introduction

Multi-store cars increasing with population growth have excellent security and temporary parking for cars in big cities, which isn’t suitable for parking in the streets. [1]. Increased vehicle traffic in metropolitans has led to tight parking spaces, increased vehicle cruising, etc. [2]. Multi-store parking garages are being built; however, due to the increase of cars, air pollutants have grown significantly to treat parking guards’ health. [3]. Heavy traffic in these microclimates causes a 10% increase in the concentration of particles compared to an outdoor environment [4, 5]. Air pollution, especially in the encapsulated areas with insufficient ventilation systems, has adverse effects on human health [6], including developing sick building syndrome (SBS). Also, Indoor parking represents microenvironments that potentially affect its staff and clients harmfully [7, 8].

Air pollution and traffic flow have a positive relationship in encapsulated spaces, especially in peak times and with limited fresh air ventilation. The vehicle exhaust is comprised of incombustible fuels, lubricating oils, and combustion products [9]. The main components of the vehicle’s exhaust gas are carbon monoxide (CO), carbon dioxide (CO2), nitrogen oxides (NOx), sulfur oxides (SOx), volatile organic compounds (VOCs), and particulate matter (PM) [10, 11]. PM emissions in the atmosphere result from anthropogenic and natural sources [12]. PM is considered an essential indicator of outdoor air quality, and many health problems are associated with the high levels of these particles. PM1, PM2.5, and PM10 are particulate matter with aerodynamic diameters of less than 1 μm (PM1) and 2.5 μm (PM2.5), and 10 μm (PM10) [13]. PM exposure has diverse negative health impacts on humans. To exemplify, lung cancer [14], respiratory diseases [3] and cardiovascular diseases (CVD) [15], endothelial dysfunction [16], reduction of sperm quality [17], preterm delivery [18], and increased hospital receptions [19] are directly/indirectly linked to PM exposure. Moreover, studies indicate a significant correlation between PM and different diseases such as schizophrenia [20], autism [21], psychiatric disorders [22], cardiovascular [23], Alzheimer’s diseases [24], and suicide[25].

Air quality in underground parking garages depends on many factors such as car engine types and ages, operating conditions, emission control systems, fuel consumption, parking capacity, volumetric parking space, and ventilation system [26, 27]. Traffic congestion duration, idling of engine [2], and traffic flow [27] are the major anthropogenic drivers of air pollution [28]. Most of the engine’s emissions occur during warm-up, in which a cold car consumes 27% more fuel than a hot car and produces 86%, 40%, and 12% of CO, CH, and NOx more than a hot car, respectively [29]. Underground garages can have natural and mechanical ventilation. Recent studies indicate a significant difference between these systems. For example, apart from structural design, natural ventilation in closed spaces is ineffective [27, 30]. Meanwhile, the association between air pollution and its meteorological drivers in the outdoor environment is well established; however, we are yet to know the underlying drivers of air pollution in an outdoor environment [31, 32].

Due to high traffic flow and high demand for visiting, the pilgrimage sites are one of the busiest places in Iran, which requires a high demand for parking spots. It has been concluded that these sites on weekends are more prone to high pollution concentrations than on weekdays [33]. Meteorological conditions can impact the arrangement and transport of PM [34]. In the German Hernandez (2017) study about Temperature and Humidity Effects on PM concentrations, the results show that there is a strong relationship between the concentrations of PM and meteorological parameters like RH and Temperature. RH affects the common deposition preparation of PM, whereby moisture particles follow PM [35]. Qom, as a holy city, is the host of many Muslim pilgrims during the year. The present research is the first study exploring the relationship between meteorological variables and PM1, PM2.5, and PM10 in the Zaer Parking Garage (ZPG). This study was the first investigation performed in the ZGP, one of the parking locations in Iran with high client rates. The goals of this study are (1) to determine PM concentrations in the ZGP and (2) to investigate the effect of temperature and humidity on PM concentration in ZGP.

Materials and methods

Site characterization

Qom city, the capital of Qom province, is located 130 km southwest of Tehran (44°34’37” N, 55°33’27” E) in the central plateau of Iran. The area of the city is roughly 730 km2, and the city population is near 1.2 million people. This city consists of 8 urban districts, and the temperature in the residential quarters ranges from − 2.3° C in January to 40.1° C in June [36, 37]. The ZPG is one of the enormous parking garages in Iran, which is located downtown the city. Its capacity is about 1830 vehicles, and its ventilation combines mechanical and natural ventilation [28]. The main entrance of the ZPG is from the fifth floor from underground to the surface. The ZPG has a horizontal area of 21,659 m2 and a vertical depth of 3.5 m for each floor [28].

Figure 1 illustrates the space of the ZPG underground parking in which the entrance is on the left side of the parking lot, and the small squares around the area are ZPG’s elevators. Also, a hatched area in the middle of the figure shows a closed space related to building facilities.

Fig. 1.

Fig. 1

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Zaer Parking Garage schematic

Sampling

The PM1, PM2.5, and PM10 levels in the air were measured by GRIMM EDM 107 dust analyzer (GRIMM Aerosol Technik GmbH & Co. KG, Ainring, Germany) laser dust monitor in real-time. The GRIMM environmental particle analyzers are unique in providing real-time information on the source apportionment of particulates. The GRIMM EDM 107 dust analyzer (GRIMM Aerosol Technik GmbH & Co. KG, Ainring, Germany) measurement range is from 0.25 to 32 microns. This device is calibrated annually by the company’s representative. The measured values are based on detecting the scattered angle of the laser on the sample air. Sampling was performed at the height of 1.5 m from the ground. Every minute, the measured data are automatically and continually saved on a removable memory card. Similarly, GRIMM EDM 107 dust analyzer (GRIMM Aerosol Technik GmbH & Co. KG, Ainring, Germany) recorded meteorological variables such as temperature, pressure, and humidity.

Considering that three of the five existing floors are used for car parking, and one floor is used as a passage, on each floor, selected three sampling points (entrance, exit, and middle of the parking lot) and four sample steps. We took samples and to ensure and control the sampling process, at least two repetitions in 10% of the samples were considered. Since there was no general instruction for the number of samples, based on similar studies, these samples were taken in the summer and autumn of 2017 and the exit point and the middle area of each parking garage floor [38]. In general, the number of samples was done as follows: Three samples in each floor (twice) in two different temperature conditions and two maximum and normal capacities. Finally, from any point (entrance, exit, and area) with 10% repetition and six samples on the upper floor of the parking lot (passage).

Data Analysis

Statistical analysis was performed using SPSS (Statistical Package for the Social Science, version 21.0; SPSS Inc., Chicago, IL, USA). Continuous variables were expressed as median (interquartile range, 25th -75th ). Kruskal-Wallis test was used for the assessment of PM levels on every floor. Overall, the collected data were analyzed by the skewness-kurtosis test, Kruskal-Wallis test, Kolmogorov–Smirnov test, and Pearson Correlation test.

Results and discussions

PM concentration

The results showed that during the sampling period, the PM10 level was from 43.3 to 684.8 µg/ m3. The daily mean level of PM10 was 120.86 ± 90.55 µg/ m3. It was reported that PM2.5 experienced an interval between 14.7 and 84.8 µg/m3, which means that the daily average concentration of this particulate matter was 28.46 ± 10.35 µg/m3. The least and maximum levels of PM1, 6.8 ± 3.8, and 72.9 ± 3.8 were obtained, respectively, and the daily average was 10.8 ± 3.779 µg/m3. Table 1 was presented descriptive statistics of PMs concentration and environmental condition in ZGP. According to Liu et al. (2019), PM2.5 levels in underground parking garages had a marginal difference compared to outdoors. However, this difference is higher in PM10 [27], similar to the ZPG measurements, which demonstrated a higher level of PM10 than PM2.5. PM1 can negatively impact human health more than PM2.5 owing to its smaller diameter causing greater penetration into the lungs, and it could affect alveoli profoundly [39, 40]. Human health can be affected by exposure to these particles causing vascular diseases. Cardiovascular diseases (CVDs) contain about one-third of the world’s deaths; thus, identifying the causes of these diseases and their control is crucial [41]. These particulate matter can also have many health effects on the respiratory system, vision, and climate change, all of which highlight the importance of monitoring and controlling them [42].

Table 1.

descriptive statistics of PMs concentration and environmental condition in ZGP

Parameters Min Max Range Median Mean SD P95 Var CV
PM10 (µg/ m3) 43.3 684.8 641.5 82.4 120.862 90.555 2.174 8200.247 0.749
PM2.5 (µg/ m3) 14.7 84.8 70.1 24.8 28.462 10.352 0.249 107.163 0.364
PM1.0 (µg/ m3) 6.8 72.9 66.1 9.9 10.809 3.779 0.091 14.278 0.350
Temperature (◦C) 26.5 37.6 11.1 34.3 33.909 1.675 0.040 2.804 0.049
Humidity (%RH) 12.5 27.1 14.6 14.4 14.831 1.941 0.047 3.767 0.131
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Distribution of PM levels in different floors

The Kolmogorov-Smirnov test showed that the level of suspended particles corresponding to all sampling units does not follow the Gaussian distribution. Moreover, the results of the Kruskal-Wallis test highlighted that particle levels had significant differences in different stories/floors (p-value < 0.001). As shown in Fig. 2, the highest suspended particle levels were observed on the third floor. The convective heat flux (due to vehicular activity) transfers the air to the higher floors (i.e., the warm air tends to move up to the upper floors). Thus, the produced PMs were being carried to the upper floors, which might be why the higher levels of the particles on the upper floors were higher.

Fig. 2.

Fig. 2

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Distribution of PM levels by floors. (a) PM10 (b) PM2.5 (c) PM1

Comparison of PM levels in parking and standards

Table 2 shows the allowable range of PM10, and PM2.5 reported by the World Health Organization, the United States Environmental Protection Organization (USEPA), and the European Union (EU).

Table 2.

Some of the PM (µg/m3) standard ranges

Particle size fraction WHO USEPA and Iran EU

PM10

Annual mean

24-hour mean

20

50

50

150

20

50

PM2.5

Annual mean

24 h mean

10

25

15

35

Not set

Not set
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Figure 3 compares the PM level at high and low traffic flows. As the results show, it was significantly higher in the busy days (mainly due to many pilgrims and visitors) with an estimated average of 8500 vehicles. The results also indicate a more significant PM concentration than days with 4000 vehicles (p-value < 0.001). Sentian et al. (2004) showed that the amount of PMs in the aboveground and underground parking garages significantly correlated with vehicles. They also demonstrated that PM concentration increases during the weekend due to high traffic congestion [43]. Also, the result of this study showed a strong correlation between the PM concentration and the number of vehicles (traffic flow), as the study proved that the number of vehicles increases on holidays and religious occasions.

Fig. 3.

Fig. 3

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Distribution of PM levels by traffic congestion (a) PM10 (b) PM2.5 (c) PM1

Furthermore, due to high traffic flow in different stories and numerous idling of engines, PM concentration substantially increases [43]. Liu et al. (2019) studied an underground parking garage in Puding, China, which indicated that the daily concentration averages of PM10 and PM2.5 particles were more than to that of ZPG; this could be related to the high volume of traffic flow and the excessive number of vehicles in Puding. Also, the results showed a significant relationship between the concentration of PM10 with the volume of traffic flow (P-value < 0.001) [27].

Effect of meteorological variables on PM levels

At the sampling time, the humidity and temperature measurements indicated that the average moisture and temperature were 14.8 (1.9) % and 33.9 (1.7) ˚C, respectively. Figure 4 shows a correlation between the PM, humidity, and temperature in ZPG. According to the results, the concentration of each PM has a significant positive correlation with the concentration of other PM (p-value < 0.0001). The correlation coefficients of PM10 with PM2.5 and PM1 were 0.836 and 0.341, respectively. Moreover, the results demonstrated a positive relationship between increasing moisture and increasing the PM level; the correlation coefficient of moisture for PM10, PM2.5, and PM1, were 0.043, 0.354, and 0.530, respectively. Besides, it was observed that when the internal temperature of ZPG decreased, the PM levels increased (p-value < 0.0001). The negative correlation might be related to various factors. When the temperature decreases, an inversion is created, which acts as a barrier to dispersion and diffusion of PM. The potential reasons for this inversion are the compression of volatile organic compounds (VOCs) and the increase in fuel consumption [35]. In conformity with the present research results, Zeki et al. (2018) study showed that there had been a significant positive correlation in an indoor environment in turkey between PM levels and relative humidity.

Fig. 4.

Fig. 4

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Correlations between PM, Temperature, and Humidity

On the other hand, there was a significant negative correlation between temperature and PM levels [42]. The study of Wan Kuen et al. (2006) in South Korea also showed proportionate to upper stories, PM10 concentration was 2.5 times for winter and 1.2 times for summer higher on lower floors. Moreover, an inverse relationship is observed between temperature and PM10 concentration [44]. Similarly, Chen et al. (2018) showed that the PM1 concentration is higher in the winter and lower in the summer [40].

Conclusions

This study investigated the PM levels in terms of particle size PM10, PM2.5, and PM1 of ZPG and their relationship with microclimatic parameters. It was found that PM10 had the highest level on different floors of ZPG and various conditions. The results indicated that the lower floors had less PM concentration than the upper floors, which is majorly caused by the convective airflow; moreover, during daylight, when the traffic flow increases, the level of PM increases in the indoor air of ZPG due to high traffic flow and congestion. The study found a positive and significant relationship between PM concentration and humidity. Also, it was observed that the temperature had a negative relationship with PM levels. Also, the daily average concentration of PM10 was more than twice the standard the WHO provided. But often, PM2.5 was within the standard limit of USEPA and the WHO. The higher level of PM10 in the ZPG indicated the necessity of proper management and planning to provide a better ventilation system to reduce the harmful effects PM exposure.

Limitation

There were two limitations to this study:

1) Sampling was stopped for four hours (i.e., 00:00 to 4:00) during the ZPG closure.

2) It was not possible to take samples continuously for all religious holidays due to the manufacturer’s limited allowable time of sampling.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1 (23.4KB, docx)

Acknowledgements

The authors would like to appreciate the Research Center for Environmental Pollutants of the Qom University of Medical Science (grant number: 96899) for providing financial support for this research. The authors would like to extend their appreciation to Mostafa Rezaali for reviewing the manuscript.

Author contribution

Nayereh Rezaei Rahimi: Conceptualization, Methodology, Validation, ,Writing - original draft, Writing - review & editing. Reza Fouladi-Fard: Conceptualization, Methodology, review & editing, Writing - original draft, Supervision, Project administration. Mohammad Rezvani Ghalhari: Conceptualization, Writing - original draft, Writing - review & editing. Hasan Mojarrad: Sampling, Methodology. Ahmadreza Yari: Methodology, Conceptualization, Writing - original draft. Mohammad Mahdi Farajollahi: Writing - original draft, review & editing. Amir Hamta: Methodology, Conceptualization. Maria Fiore: Methodology, Conceptualization, Writing - original draft.

Funding

This work was supported by the Research Center for Environmental Pollutants of the Qom University of Medical Science.

Data Availability

All data generated or analyzed during this study are included in this published article.

Declarations

Ethical approval

Not applicable.

Consent to participate

Not applicable.

Consent to Publish

Not applicable.

Competing interests

The authors of this article declare that they have no conflict of interests.

Footnotes

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40201-022-00818-x.

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Supplementary Materials

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Data Availability Statement

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