International Journal of Environmental Health Research ISSN: 0960-3123 (Print) 1369-1619 (Online) Journal homepage: https://www.tandfonline.com/loi/cije20 Exposure to organic and inorganic trafficrelated air pollutants alters haematological and biochemical indices in albino mice Mus musculus Azis Kemal Fauzie & G. V. Venkataramana To cite this article: Azis Kemal Fauzie & G. V. Venkataramana (2020) Exposure to organic and inorganic traffic-related air pollutants alters haematological and biochemical indices in albino mice Mus�musculus, International Journal of Environmental Health Research, 30:2, 117-133, DOI: 10.1080/09603123.2019.1577367 To link to this article: https://doi.org/10.1080/09603123.2019.1577367 View supplementary material Published online: 13 Feb 2019. Submit your article to this journal Article views: 132 View related articles View Crossmark data Citing articles: 1 View citing articles Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=cije20 INTERNATIONAL JOURNAL OF ENVIRONMENTAL HEALTH RESEARCH 2020, VOL. 30, NO. 2, 117–133 https://doi.org/10.1080/09603123.2019.1577367 ARTICLE Exposure to organic and inorganic traffic-related air pollutants alters haematological and biochemical indices in albino mice Mus musculus Azis Kemal Fauzie and G. V. Venkataramana Department of Studies in Environmental Science, University of Mysore, Mysore, India ABSTRACT ARTICLE HISTORY The relationship between air pollution exposure and haematology remains controversial. Evidences in the effect of trace organic air pollutants and in the impact of such exposure on lipid and protein levels are scarce. This work investigated the health effects of medium-term exposure to traffic-related air pollution on both haematological and biochemical indices in animal models. Two groups of albino mice (Mus musculus) were exposed to ambient air polluted by vehicle exhaust for three and six months, and one group was kept as control. Results found significant depletions (p < 0.05) in red blood cells, packed cell volume, neutrophils, eosinophils, monocytes, and total cholesterol after air pollution exposure. On the contrary, significant elevations (p < 0.05) were observed in platelet, lymphocytes, and serum albumin compared to control condition. Correlation data suggested that significant changes in blood parameters may be altered by the synergistic effect of several organic and inorganic air pollutants. Received 31 December 2018 Accepted 28 January 2019 KEYWORDS Air pollution; animal exposure; biomarker; haematology; vehicular emission Introduction Rapid economic development and industrialisation in India have sharply increased the trajectory and traffic growth in its major cities (CPCB 2010). Number of vehicle population in India has gradually increased from about 310 thousand in 1951 to 210 million in 2015 with an average growth of 10% per annum (MRTH 2017). Elevated number of automobiles demands higher consumption of non-eco-friendly fossil fuels that, later on, has resulted in a higher magnitude of pollution load in the urban environment. It is well-accepted that motor vehicles have emerged as a dominant source of urban air pollution in the developing world (Shrivastava et al. 2013). The four major air pollutants need to be concerned are nitrogen dioxide, sulphur dioxide, ozone, and particulate matter (WHO 2006). The other pollutants may also have adverse effects though the concentrations are very small. WHO (2000) has evaluated 16 organic and 12 inorganic pollutants, including the toxic heavy metals, that may have risks to human health. Ultrafine particles which are considered as inert substances in ambient air might turn into toxic substances after interact with human’s biological fluids and cells (Albuquerque et al. 2012), because they can act as condensation nuclei for the toxic organic and inorganic airborne species. Plenty of studies have been conducted to identify the level of air pollution attributable to road transport sector in India (Table S1), but most of them evaluated only the major criteria pollutants and very few of them took into account the magnitudes of trace organic and inorganic elements or CONTACT Azis Kemal Fauzie aziskemal@envsci.uni-mysore.ac.in Environmental and Cleaning Service Agency, Government of Karawang Regency, Jl. Lingkar Tanjungpura No.1, Karawang, West Java 41311, Indonesia Supplemental data for this article can be accessed here. © 2019 Informa UK Limited, trading as Taylor & Francis Group 118 A. K. FAUZIE AND G. V. VENKATARAMANA compounds. Most of the efforts worked on analysing the ambient air quality monitoring and registered vehicle data collected by the environmental and transportation authorities, and less number of study attempted to collect air pollution data by actual road traffic census. Air pollution is widely known as the important factors that threatening human health in all over the world for many centuries. Numerous epidemiological studies have reported the consistent effects of short-term and long-term exposure to air pollution on chronic morbidity and mortality (Emmerechts et al. 2011) due to respiratory and cardiovascular diseases (Maji et al. 2018), either in adults (Zuurbier et al. 2011) or children (Schwartz 2004) and either in urban or rural environment (Garcia et al. 2016). Furthermore, recent papers have reported the chronic health effects of pre- and perinatal exposure to air pollution (Deng et al. 2016a, 2016b; Lu et al. 2017). The top ten causes of worldwide death in 2015 (Figure 1) are mostly the air pollution-related diseases such as ischaemic heart disease, cerebrovascular disease (stroke), chronic obstructive pulmonary disease, lower respiratory infections, and tuberculosis (WHO 2017), and this figure is almost similar to the case of India in 2016 (Dandona et al. 2017). Several types of biomarkers can be employed to evaluate the effect of pollution on health (Peakall 1992). Higher preference is given to the use of haematology. Changes in blood cell counts may provide early indicator of the toxic effects of pollutants (Weeks et al. 1992), although the responses may depend on the age, sex, and genetic background of the individual (Biser et al. 2004). But in general, any changes in white blood cell count can be a resistance index of an individual to some diseases, whereas red blood cell count, haematocrit and haemoglobin concentrations can provide information in the capacity index of the blood to transfer oxygen (Tête et al. 2015). The reports brought by a number of researchers regarding the association between air pollution exposure and blood parameters still cannot give uniform results (Poursafa et al. 2011). Most of the studies reported the effect on haematological aspects such as haemoglobin, haematocrit, leucocytes, erythrocytes, and thrombocytes, but limited evidence was found in the effect on biochemical aspects including the protein and lipid profiles (Table 1). Thus, a need still exists for field validation on the effects of traffic-related air pollution on both haematological and biochemical parameters as well as on body weights after exposure in medium-term period of several months. The present study is a relatively complete effort made to identify the pollution level of airborne vehicular air pollutants, either the major conventional or the minor organic compounds, by direct measurement of pollutant concentrations and by mathematical estimation of vehicular emission load based on actual road traffic survey and to observe their toxicological health impact on blood injury indices in animal models. Figure 1. The top 10 causes of death in India and worldwide. Sources: aWHO (2017); bDandona et al. (2017). Objects of study Mice Mice Mice Diabetic mice Wistar rats Rats Rats Albino rats Albino rats albino rats Albino rats Shrews Rabbits Parrots Asthmatic children SCA children School-going boys School students University students Healthy adults Healthy adults Healthy adults Healthy adults Healthy adults Healthy adults Healthy + MS adults Elderly CHD patients Diabetic patients Healthy cyclists Healthy commuters Police officers Troopers Factory workers Exposed to (E) or administered with (A) A ultrafine PM A ambient PM E diesel exhaust A diesel exhaust A silver nanoparticles A silver nanoparticles E fine CAP E SO2 E SO2 E urban air E welding fume E metal pollution A heavy metals E glass industry E indoor air E ambient air E urban air E ambient air E polluted air E ambient air E gas flares E road tunnel E diesel exhaust E diesel exhaust E diesel exhaust E diesel exhaust E ambient air (PM) E ambient air (PM) E traffic PM E traffic E traffic PM E ultrafine PM E patrol cars E cement dust ↑ ̶ ↓ ↓ ↑ ↑ ↑ ↑ ̶ ↑ ↓ ↓ ↑ ↑ ↑ ↓ ↓ ↓ ↓ ↓ ↓ ↑ ↓ ↓ ↑ ↑ ↓ ↓ ↑ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↑ ↑ ↓ ↓ ↓ ↓ ↓ ↓ ↑ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↑ ↓ ↓ PCV Hb ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↑ ↑ ̶ ↑ ↓ ↓ ↑ ↑ ↑ ↓ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↓ ↓ ↑ ↑ ↑ ↓ ↓ ↓ ↑ ↑ ↓ ↓ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↓ ↑ ↑ ↑ ↑ ↑ ̶ ↑ ↑ ↑ ↑ ↑ ↓ ↓ ↑ ↓ ↑ ↑ ↑ ↑ ↑ ↑ ↑ Eos ↓ ↓ ↓ Neu ↓ ↓ ↑ ↑ ↑ ↓ Bas ↓ ↑ ↓ ↓ ↑ ↑ ↓ ↓ ↑ ↑ ↓ ↓ Mon ↓ ↓ ↓ ↑ ↑ ↑ ↑ ↓ ↑ ↑ ↓ ↓ ↓ ↓ ↓ ↑ ↑ ↑ ↓ ↓ ↓ ↓ ↑ ↑ Lym ↓ ↑ ↑ ↓ ↑ ↑ ↓ ↑ ↑ ↑ ↓ ↓ ↓ ↓ ↓ ↑ ↑ ↑ ↓ ↓ ↓ ↓ ↓ ↑ ↑ ↓ ↑ ↑ ↓ ↓ ↓ PLT ↓ ↓ ↓ ↓ ↑ Alb Effect on haematological and biochemical parameters WBC ↓ ↓ ↓ ↓ ̶ ↑ ↑ ↑ ↑ ↓ ↓ ↓ ↓ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↓ ↓ RBC ↓ ↓ ↓ ↓ ↑ Prot ↑ Trig ↓ Chol Two arrows represent a significant change or association (p < 0.05), whereas one arrow indicates a non-significant change (p > 0.05) in blood cell counts or percentages. Up arrows reflect an increase or positive association and down arrows sign a decrease or negative association. A minus sign indicates that no effect was observed. PM = particulate matter, CAP = concentrated ambient particles, SO2 = sulphur dioxide, SCA = sickle cell anaemia, MS = metabolic syndrome, CHD = coronary heart disease. See Materials and methods for abbreviations in blood parameters. References Cozzi et al. 2006 Cozzi et al. 2007 Cassee et al. 2012 Nemmar et al. 2013 Naghsh et al. 2012 Cheraghi et al. 2013 Kooter et al. 2006 Etlik and Tomur 2006 Fatma et al. 2014 Olajire and Azeez 2012 Qasim and Ahmed 2013 Chardi et al. 2008 Bersenyi et al. 2003 Pathak and Rana 2012 Erdei et al. 2003 Mittal et al. 2009 Das and Chatterjee 2015 Poursafa et al. 2011 Kargarfard et al. 2011 Steenhof et al. 2014 Egwurugwu et al. 2013 Larsson et al. 2007 Lucking et al. 2008 Salvi et al. 1999 Törnqvist et al. 2007 Krishnan et al. 2013 Seaton et al. 1999 Rückerl et al. 2007 Jacobs et al. 2010a Jacobs et al. 2010b Zuurbier et al. 2011 Jordakieva et al. 2018 Riediker et al. 2004 Mojiminiyi et al. 2008 Table 1. Studies that investigated the association between air pollution and haematology. INTERNATIONAL JOURNAL OF ENVIRONMENTAL HEALTH RESEARCH 119 120 A. K. FAUZIE AND G. V. VENKATARAMANA Materials and methods Study area The study was conducted in Mysore city, the third largest city in the state of Karnataka, India. The city comes under semi-arid climate and has a warm, cool and salubrious climate throughout the year. The main seasons are summer from March to May, monsoon from June to August, post-monsoon from September to November, and winter from December to February. Similar to many other Indian cities, Mysore city has high growth in vehicle population and emission problem. It has over half million vehicles registered in 2015 and the number is about to expand 120% in 2020 (Harish 2013). Five locations have been sampled for air pollution studies. Two sites are in the commercial area (Site 1 and Site 2) and one spot is in the industrial area (Site 3). Two sampling points have been selected to represent residential area; one is in university campus (Site 4) and another one is in the newly developed village (Site 5). All sites have different traffic volume as well as pedestrian and vegetation density. Experiment aimed to explore the effect of air pollution on animal health was conducted in Site 1, which is visually considered as the highest air-polluted site, and in Site 4 as control site for data comparison. Air quality and weather monitoring Air quality was measured in terms of nitrogen dioxide (NO2) and suspended particulate matter (SPM) concentrations. NO2 was measured digitally using Aeroqual active sampler, whereas SPM was measured and collected manually using vacuum air pump sampler. The vacuum pump contains two types of exchangeable filters, ie bag filter and screen filter. The collection of SPM was carried out on the roadside at daytime with a flow rate of 25 L/min and total sampling duration at each site was noted. SPM entrapped in the vacuum pump filters were then taken out thoroughly and its concentration was measured using gravimetric analysis as described by Araújo et al. (2014). Outdoor temperature, humidity, heat index, barometric pressure and wind speed were monitored at all sites using a weather centre, AcuRite® model 00615. The instrument consists of a sensor and a digital display unit; both are connected using wireless transmission signal (Fauzie and Venkataramana 2017). The instrument readings were noted down and the average values of each weather variable for each season were calculated. Traffic census and emission load measurement Number of vehicles travelled in each site was estimated by traffic survey that was sampled for few days in each season. In the present study, vehicles are differentiated into six categories namely two-wheelers (2Ws), three-wheelers (3Ws), cars, buses, light commercial vehicles (LCVs), and heavy commercial vehicles (HCVs). Two-stroke (2S) and four-stroke (4S) mopeds, scooters, and motorcycles are classified as two-wheelers. Three-wheelers comprise 2S and 4S auto rickshaws with different fuels including diesel, CNG, and LPG. Cars are mainly small passenger cars, jeeps, vans, and multi-utility vehicles. LCVs include small goods carrier vehicles and traveller or tourist carrier vehicles. Buses are a group of CNG- and diesel-fuelled buses and minibuses. HCVs mainly consist of diesel trucks having six tyres or more. The estimation of emission load of a compound generated by vehicles is calculated using formula given elsewhere (Gurjar et al. 2004; Ramachandra and Shwetmala 2009; Fauzie and Venkataramana 2016; Venkataramana et al. 2018) and it highly depends on the number of vehicles travelled in the traffic area and their emission factors. The emission factors required for the calculation were determined according to the types of vehicles (Table S2). They were assumed constant for each vehicle category and taken as average values of emission factors for Indian vehicles as measured by ARAI (2008). ARAI classified the emission factors of different types of vehicles by considering their fuel types, engine cylinder capacity, vintages, and fuel technology INTERNATIONAL JOURNAL OF ENVIRONMENTAL HEALTH RESEARCH 121 used. Further study by Janhäll et al. (2012) suggested that emission factors may not always be constant, but vary depend on the ambient temperature and relative humidity measured at a particular site. Apart from emission load of five major compounds namely carbon monoxide (CO), carbon dioxide (CO2), oxides of nitrogen (NOX), total hydrocarbons (HC), and particulate matter (PM) that are expressed in g/h, the emission load of other trace organic compounds like benzene (Bzn), 1,3-butadiene (Btd), formaldehyde (Fmd), acetaldehyde (Acd), total aldehydes (Ald), and total polycyclic aromatic hydrocarbons (PAH), were also calculated in this present study and expressed in mg/h. Toxicological study Fifteen three-weeks-old albino mice (Mus musculus) were obtained from the animal house, Department of Zoology, University of Mysore. Animals were put into cages and divided into three groups; two groups were exposed to urban air for three and six months (six hours per day and five days a week) and one group was kept in university’s laboratory (Site 4). Food and water were provided ad libitum. All laboratory animals received humane care in compliance with the guide published by the National Academy of Sciences (NAS 2011). This study was approved by the Institutional Animal Ethics Committee of University of Mysore No. UOM/ IAEC/25/2018. The experimental set up consisted of a cubical plastic chamber connected to a vacuum air pump as main machine for supplying polluted air to expose to the animals. Inhalation chamber was placed in front of a high storey building in the side of Irwin Road (Site 1), a very busy and congested road in the city. The filters in the vacuum pump which prevented the admission of vehicular dust particles (designated as pollutants) were removed, thus the atmospheric air can enter the inhalation chamber without filtering system. Animals were anaesthetised using chloroform and sacrificed directly after the termination of exposure period. Blood samples were drawn from the heart and collected in heparinised anticlotting tubes containing EDTA (tripotassium salt). Haematological parameters of study animals such as haemoglobin (Hb), haematrocrit or packed cell volume (PCV), leukocyte or white blood cell (WBC) total count, neutrophil (Neu), eosinophil (Eos), basophil (Bas), monocyte (Mon) and lymphocyte (Lym) differential counts, erythrocyte or red blood cell (RBC) count, and thrombocyte or platelet (PLT) count were assessed in the laboratory by using an automated haematological analyser, Sysmex XP-100. The analyser also measured other biochemical parameters in the blood plasma including the levels of triglyceride (Trig), total cholesterol (Chol), serum albumin (Alb), and total protein (Prot). Statistical analysis Data were expressed as mean ± SD. They were subjected to one-way between-groups analysis of variance (ANOVA) followed by post-hoc comparisons using Fisher’s least significant difference (LSD) test. Significant differences of the values between the groups were tested at the p < 0.05 level, unless specified. SPSS version 16.0 and Data Analysis ToolPak from Ms. Excel were employed for the statistical analysis. The effect size is calculated using eta squared (Pallant 2007, p. 247) and follows the Cohen’s (1988, p. 284–287) guidelines that classify 0.01 as a small effect, 0.06 as a medium effect, and 0.14 as a large effect. Linear regression analyses and two-tailed bivariate Pearson’s correlation have been prepared to identify the association between air quality parameters, number of vehicles, and emission load. Pearson’s correlation was also used to evaluate the association between air pollutants and changes in blood indices of the exposed animals. 122 A. K. FAUZIE AND G. V. VENKATARAMANA Results and discussion Air pollution and vehicular emissions data Air pollution, weather monitoring, and road traffic survey data in five study sites are given in Table S3. The numbers reported in the data are the average values of repeated observations in four seasons at each site. Result found that Site 1 has the highest NO2 and SPM concentrations. Road traffic census also brought the same figure that Site 1 holds the highest traffic volume, thus the highest level in pollution load compared to other sites. Two-wheelers dominate the traffic census at all sites. However, they may not always be the highest emission contributor. Result in vehiclewise emission load suggested that cars and three-wheelers contribute high to the air pollution in Site 1, whereas buses pollute more in Site 2. Both LCVs and HCVs are the major source of road pollution in Site 3. Ramachandra and Shwetmala (2009) reported that trucks are the highest pollution contributor in all over India, followed by LCVs, buses, and cars. Meteorological condition was found to be insignificant to alter the air quality and pollution load (Table S4), but NO2 and SPM concentrations show highly significant correlation with the number of all types of vehicles, except HCVs. This implies that almost all fossil-fuelled vehicles have contributed equally to the gaseous and particulate air pollution in the city. A significantly positive correlation has been found between NO2 and SPM (r = 0.821, p < 0.01; 19 d.f). It means that sites with high NO2 level will have high SPM concentration as well (Figure 2(e)). This finding is quite similar to that of Hung et al. (2014). They found that PM, particularly black carbon, and NOX were positively correlated during wet and dry seasons, because both are emitted primarily from diesel vehicles. The relationship may be useful for evaluating the advanced emission control technologies in the road traffic management, because slope of the PM-NOX relationship may decrease significantly when diesel particulate filters are installed on the tail-end-pipes of vehicles (Park et al. 2002). Both NO2 and SPM concentrations positively correlated with the total traffic volume (r = 0.859 and 0.938, respectively, p < 0.01; 19 d.f) which explains that increased number of vehicle will increase the air quality level (Figure 2(a,b)). In addition, the traffic volume has positive effect on the pollution load (Figure 2(f)), as they correlated significantly (r = 0.916, p < 0.01; 19 d.f). Based on those evidences, it may be suggested that the experimentally measured air quality indicator levels have similar trend with the mathematically calculated air pollution load (Figure 2(c,d)), as both NO2 and SPM concentrations have significant positive correlation with the total vehicular emission load (r = 0.866 and 0.801, respectively, p < 0.01; 19 d.f). Effects on body weight The body weight of mice, either male or female, may be affected by the direct exposure to ambient vehicular pollution. The measurement compared between initial weights and weights after exposure to urban traffic air since the exposure started and continuously every one week interval using 0.1-μg-accuracy analytical balance. Result found a statistically significant difference in male mice body weight: F(6, 35) = 54.378, p < 0.01 (Figure 3). Post-hoc comparisons using the LSD test indicated that the mean value of initial weights was significantly different from weights after 1month exposure (p < 0.01). The difference was also significant at the p < 0.05 level between male mice body weights after 3 months and 4 months. Mean value of initial female mice body weight was significantly different from weight after 1month exposure (p < 0.01). Body weights of female mice between after 1-month and 2-month exposure were considerably different at the p < 0.1 level, while weight after 3-month exposure was found to be insignificantly different from weight after 2-month exposure. When compared to control condition, increases in body weights of male and female mice after 2-month and 3-month exposure were significantly different at the p < 0.01 level (Figure 4). It is widely acceptable that stress has the effects on food intake and body weight. Different types of stressor can be given including the traffic-related air pollution exposure. Torres and Nowson INTERNATIONAL JOURNAL OF ENVIRONMENTAL HEALTH RESEARCH 123 Figure 2. Linear regression analysis between air quality (NO2 and SPM), traffic volume, and vehicular emissions. All correlations are significant at the p < 0.01 level (two-tailed), n = 20 (sampled in four seasons at five sites). ▲ = Site 1, ● = Site 2, ◊ = Site 3, Δ = Site 4, ○ = Site 5. (2007) suggested that as cortisol (stress hormone) levels increase, food intake will consecutively decrease. But Wolk and Kozlowski (1989) believed that body weight of animals depends on season, age, and sex of the individuals. Therefore, studies in the effect on body weight usually get the least concern for the scientists. Effects on haematological parameters A one-way between-groups ANOVA was conducted to explore the impact of exposure to trafficpolluted air on the Hb and PCV concentrations as well as RBC, WBC, and PLT counts. Subjects were divided into three groups namely control group, 3-month treatment (3M) group, and 6month treatment (6M) group (Table 2). The result showed insignificant difference at the p < 0.05 124 A. K. FAUZIE AND G. V. VENKATARAMANA Figure 3. Body weight of albino mice after 1-month to 6-month vehicular air pollution exposure. *p < 0.1, **p < 0.05, ***p < 0.01, NS = non-significant. Figure 4. Increase in body weight after 2-month and 3-month vehicular air pollution exposure compared to control. *p < 0.01, NS = non-significant. level in Hb for the three groups (p = 0.120, Figure 5(a)). The actual difference in the three mean Hb concentrations was very small (14.07, 11.70, and 13.56). Result in mean PCV of the three groups showed a large difference (53.63, 46.13, and 45.74). The effect size calculated using eta squared was also large (0.46). Post-hoc test indicated that the mean PCV for control group was significantly higher than the 6M group (p = 0.038, Figure 5(b)). Our result was similar to the findings of Mojiminiyi et al. (2008), Cheraghi et al. (2013), Qasim and Ahmed (2013), and Das and Chatterjee (2015) mentioning a decrease in PCV after exposure to dust particles. PCV levels are influenced by both internal and environmental conditions. Thus, it may be considered an early and sensitive biomarker for air pollution effects and may constitute a useful nonlethal parameter in evaluating the toxicity of pollutants (Tête et al. 2015). LSD test has demonstrated the significant difference in mean RBC count between the control and 3M group (p = 0.043, Figure 5(c)). The decrease in RBC count noted in this study agrees with the findings suggested by Cheraghi et al. (2013), Egwurugwu et al. (2013), and Das and Chatterjee (2015). Machiedo et al. (1989) proposed that particles may bring free radicals that may induce the destruction of RBC. Post-hoc test noted a significantly lower value of mean WBC count for 3M group than 6M group (p = 0.033, Figure 5(d)). The increase in WBC count of the exposed animals follows the findings of Mojiminiyi et al. (2008), Naghsh et al. (2012), Egwurugwu et al. (2013), Qasim and Ahmed (2013), and Das and Chatterjee (2015). The leukocyte increase due to pollution may indicate a response to an INTERNATIONAL JOURNAL OF ENVIRONMENTAL HEALTH RESEARCH 125 Table 2. Changes in haematological and biochemical parameters in mice after exposure to air pollution. Parameters Blood cell profile: Haemoglobin, g/dL Packed cell volume, % Red blood cell count, ×106/mm3 White blood cell count, ×103/mm3 Platelet count, ×105/mm3 Control 3-month exposure 6-month exposure 14.07 53.63 10.10 9.27 8.04 ± ± ± ± ± 0.84d 3.67c 0.57b 0.47d 2.58c 11.70 46.13 8.72 6.03 4.81 ± ± ± ± ± 1.92d 3.61d 0.70a 2.29c 3.85c 13.56 45.74 9.15 10.26 13.70 ± ± ± ± ± 1.13d 4.99a 0.76d 2.71b 2.23a,b WBC differential count: Neutrophils, % Eosinophils, % Lymphocytes, % Monocytes, % 13.67 2.00 80.67 3.67 ± ± ± ± 1.53b,c 0.00c 1.15c 2.08b,c 8.33 2.67 84.33 4.67 ± ± ± ± 2.89a 0.58c 4.16d 1.15a,c 10.00 1.00 87.60 1.20 ± ± ± ± 1.22a 0.71a,b 1.52a 0.45a,b Lipid and protein profile: Triglycerides, mg/dL Total cholesterol, mg/dL Serum albumin, g/dL Total protein, g/dL 50.67 64.67 1.50 2.67 ± ± ± ± 6.64d 4.06c 0.10c 0.26d 46.67 48.00 1.80 2.47 ± ± ± ± 5.24d 6.08d 0.17d 0.20c 46.80 56.80 2.06 2.90 ± ± ± ± 0.86d 0.97a 0.07a 0.04b Data were expressed as mean ± SD. a Significantly different at p < 0.05 from control group, b Significantly different at p < 0.05 from 3-month group, c Significantly different at p < 0.05 from 6-month group, d Not significantly different from any other groups across the row at p < 0.05. inflammatory process, irritating diseases, and infection by pathogens (Tête et al. 2015), or may be a response to the tissue-damaging effects of pollutants (Das and Chatterjee 2015) and the increase of antibody activity in phagocytosis the particles (Naghsh et al. 2012). A significant positive effect was observed in PLT count: F(2, 8) = 10.199, p = 0.006. LSD posthoc test found that the mean PLT count for 6M group was significantly higher than both control group and 3M group (Figure 5(e)). There is large difference in the mean values of the groups and the effect size was also large (0.72). The present finding on the increase in PLT count due to traffic pollution exposure is in agreement with the results found by Mojiminiyi et al. (2008) and Cheraghi et al. (2013). The mechanisms of these changes are not precisely known, but Schwartz (2001) believed that change in PLT count was not only associated with PM but also with NO2. Seaton et al. (1999) proposed that ultrafine particles may alter alveolar inflammation, pass into blood stream, and interact with platelets and fibrinogen that may increase coagulation activity and other vulnerable risk factors. Effects on WBC differential counts As part of haematological study, attempt was also taken to evaluate the effects of vehicular pollution exposure on WBC subtypes including neutrophils, eosinophils, monocytes, and lymphocytes. The result obtained a significantly negative effect in neutrophils due to traffic pollution: F(2, 8) = 6.643, p = 0.020. LSD post-hoc analysis found that the mean neutrophil count for control group was significantly higher than both 3M and 6M group (p = 0.008 and p = 0.026, respectively, Figure 6(a)). The eta-squared effect size was large (0.62) following the actual difference in mean values of the groups which was also large (13.67, 8.33, and 10.00). The analysis also observed statistically significant differences at the p < 0.05 level in eosinophils (p = 0.011) and monocytes (p = 0.011). LSD post-hoc comparisons indicated that the mean differential counts of both cells for 6M groups were significantly different (p < 0.05) from both the control and 3M groups. Despite reaching statistical significance, both eosinophil (Figure 6(b)) and monocyte (Figure 6(c)) differential counts of the control groups did not differ significantly at the p < 0.05 level from that of the 3M groups. Steenhof et al. (2014) observed the similar negative 126 A. K. FAUZIE AND G. V. VENKATARAMANA Figure 5. Haematological changes in (a) haemoglobin, (b) PCV, (c) RBC count, (d) WBC count, and (e) platelet count after 3month and 6-month exposure to air pollution compared to control. *Significantly different at p < 0.05. association between number of eosinophils and particle concentration, but they found positive association between particle concentration and number of monocytes. Statistically significant difference has been demonstrated in number of lymphocytes: F(2, 8) = 7.831, p = 0.013. Post-hoc analysis indicated that the mean lymphocyte count for control group was significantly lower than 6M group (p = 0.004, Figure 6(d)). The actual difference in mean values was large (80.67, 84.33, and 87.60) and the effect size was also large (0.66). The increase in lymphocytes revealed in this report supports the earlier findings of Mojiminiyi et al. (2008) and Pathak and Rana (2012). Brüske et al. (2010) found that lymphocytes increased in association with CO and NOX air pollution, but they INTERNATIONAL JOURNAL OF ENVIRONMENTAL HEALTH RESEARCH 127 Figure 6. Changes in WBC differential count of (a) neutrophils, (b) eosinophils, (c) monocytes, and (d) lymphocytes after 3month and 6-month exposure to air pollution compared to control. *Significantly different at p < 0.05. apparently showed no effect in regard to PM. The reason may be due to PM has characteristics in covarying with other gaseous pollutants, such as NO2, and NO2 itself has the capacity to induce airway toxicity (WHO 2006). Effects on biochemical parameters The study also investigated the effects of air pollution exposure on lipid and protein levels such as triglycerides, total cholesterol, total protein, and serum albumin. Study in the biochemical parameters of the blood plasma is important because albumin assesses the nutritional status, total protein informs the dehydration or cancer of blood cells, and triglycerides play important role in lipid maintenance (Cassee et al. 2012). Proteins and lipids are essential not only for haemopoiesis but also for other body functions such as body building, cell repairs, blood clotting, hormone, and enzyme production (Egwurugwu et al. 2013). The result in the study showed insignificant difference in triglycerides (p = 0.748, Figure 7(a)). Similarly, the total protein observed insignificant difference (p = 0.186, Figure 7(b)). But there was a statistically significant difference in total cholesterol: F(2, 8) = 4.917, p = 0.040. One-way ANOVA followed by LSD post-hoc test indicated that mean cholesterol for control group was significantly higher than 3M group (p = 0.014, Figure 7(c)). The effect size was large (0.55), because the actual difference in mean scores of the groups was quite large (64.67, 48.00, and 56.80 mg/dL). Menzel (1976) suggested that NO2 can indirectly affect cell function and viability by damaging lipids, proteins, and other biomolecules. In our experiment, the PM-polluted air exposed to the animals may contain considerable amount of NO2. 128 A. K. FAUZIE AND G. V. VENKATARAMANA Figure 7. Changes in lipid and protein profiles including (a) triglycerides, (b) total cholesterol, (c) serum albumin, and (d) total protein after 3-month and 6-month exposure to air pollution compared to control. *Significantly different at p < 0.05. The positively significant effect was found in serum albumin after traffic air pollution exposure: F(2, 8) = 7.150, p = 0.017. Post-hoc comparisons using LSD test indicated that the mean serum albumin for control group was significantly lower than the 6M group (p = 0.006, Figure 7(d)). The eta-squared effect size was large (0.64) and very large difference was also found in the mean scores of the three groups (1.50, 1.80, and 2.06 g/dL). The observed changes in biochemical parameters such as albumin and fat in this study may also explain the changes in the measured haematological indices of the exposed animal models. Correlation between haematological parameters and pollutants The correlation coefficients between one and other haematological indices are calculated in order to identify the connectivity effect of the blood parameter changes (Table S5). Positive correlations were identified between PCV and RBC (r = 0.930, p < 0.01; 10 d.f), PLT and WBC (r = 0.626, p < 0.05; 10 d.f), eosinophils and monocytes (r = 0.710, p < 0.05; 10 d.f), and serum albumin and total protein (r = 0.610, p < 0.05; 10 d.f). Other study by Steenhof et al. (2014) observed the positively significant association between neutrophils and monocytes after air pollution exposure. In addition, Olajire and Azeez (2012) found the significantly positive correlation between PCV and PLT when they exposed mice to traffic air pollution. These positive correlations imply that if one parameter decreases the other parameter may also decrease. On the other hand, the significantly negative correlations were obtained between platelet and eosinophils (r = −0.756, p < 0.01; 10 d.f), PCV and lymphocytes (r = −0.620, p < 0.05; 10 d.f), lymphocytes and neutrophils (r = −0.709, p < 0.05; 10 d.f), lymphocytes and eosinophils (r = −0.672, p < 0.05; 10 d.f), and lymphocytes and monocytes (r = −0.644, p < 0.05; 10 d.f). Steenhof et al. (2014) INTERNATIONAL JOURNAL OF ENVIRONMENTAL HEALTH RESEARCH 129 also observed the negative association between lymphocytes and eosinophils after exposure to PM and NO2. These negative correlations explain that reduction in one parameter might lead to elevation in the other. The attempt was also made to calculate the correlation coefficients between the blood parameters and the pollutants in order to determine the specific pollutants responsible for the observed damages in cell and blood tissues (Table S6). Elevation in PLT of animals exposed to air pollution could have possibly been due to SPM because of significant correlation (r = 0.633, p < 0.05; 9 d.f) between PLT and SPM. Synergistic combination of CO and HC may be the major contributors to reduction in PCV, as both pollutants correlated significantly (r = −0.63, p < 0.05; 9 d.f) with the reduction in PCV of animals exposed to traffic air pollution. Almost all pollutants, except HC, acetaldehyde and total aldehyde, might be responsible for the reduction in eosinophils, as they correlated significantly (p < 0.05) with the decrease in eosinophils of the exposed animals. The elevated number in lymphocytes and the increased level in serum albumin were assumed due to the combination effect of all pollutants because there are significant correlations (p < 0.05) between these two blood indices and all types of air pollutants. Changes in lymphocytes and eosinophils are associated with the combination of PM and NO2 exposure (Steenhof et al. 2014). NO2 is a free radical that may promote oxidation and formation of nitric and nitrous acid. NO2 may react with substrates within the respiratory tract lining fluids to form nitrite and further enters the blood stream (Ewetz 1993). Therefore, nitrite and other secondary oxidation products of NO2 may bring the observed systemic effects in the blood of the exposed individuals. Conclusion The present study highlighted the association between traffic-related air pollution and its health impact using blood indices as appropriate biomarkers. The concentrations of SPM in the different site locations were measured and they were correlated significantly with the NO2 level. Moreover, the SPM and NO2 levels have significant positive association with the number of vehicles in the study area and their emission load. When exposing the animals in the urban traffic site, we found that treated mice have lesser increase in body weight compared to the untreated ones. High degree of stress experienced in the high polluted environment induced the appetite of mice, hence influenced their body weight increase. The major finding of the study is that vehicular emissions were able to induce vast change in blood parameters of the animal models, including their blood cell counts, protein and lipid profiles. Air pollution exposure significantly decreased the RBC count and PCV concentration, and increased the WBC and platelet counts. Analysis performed in WBC subtypes revealed that vehicular exhaust has the capacity to reduce number of neutrophils, eosinophils, and monocytes, but it may elevate the lymphocyte count. Urban traffic air may also alter the biochemistry of the blood by reducing the cholesterol level and increasing serum albumin. Vehicular emission load of each air pollution compound has been calculated based on traffic census. Furthermore, this data was used to deduce any possible pollutants that may have association with blood injury. Statistical analysis found that a synergistic combination of different types of pollutants is likely responsible for affecting the quantity of eosinophils, lymphocytes, and serum albumin. Changes in haematological and biochemical parameters of the study animals indicated that their blood and organs were injured due to the toxic effects of air pollutants. Acknowledgments Author AKF gratefully thanks the Indian Council for Cultural Relations (ICCR) for providing scholarship to carry out Ph.D. research programme in India. All authors are thankful to the Yale Tropical Research Institute, USA and 130 A. K. FAUZIE AND G. V. VENKATARAMANA Ravi Diagnostic Laboratory, Mysore for instrumentation and laboratory analyses; and to the Department of Zoology, University of Mysore for providing laboratory animals. We also acknowledge the helps, assistances, and facilitations of our colleagues Sreenivasa, Chandana M., and Amruta Nori-Sarma in the sampling and collection of data during field work. Disclosure statement The authors have no competing financial interests. Funding This work has no financial support. References Albuquerque PC, Gomes JF, Bordado JC. 2012. Assessment of exposure to airborne ultrafine particles in the urban environment of Lisbon, Portugal. J Air Waste Manag Assoc. 62(4):373–380. doi:10.1080/ 10962247.2012.658957. [ARAI] Automotive Research Association of India. 2008. Emission factor development for Indian vehicles. Pune: The Automotive Research Association of India. Project Rep No.: AFL/2006-07/IOCL/Emission Factor Project/Final Rep. Araújo IPS, Costa DB, de Moraes RJB. 2014. Identification and characterization of particulate matter concentrations at construction jobsites. Sustainability. 6:7666–7688. doi:10.3390/su6117666. Bersenyi A, Fekete SG, Szocs Z, Berta E. 2003. Effect of ingested heavy metals (Cd, Pb, and Hg) on hematology and serum biochemistry in rabbits. Acta Vet Hung. 51(3):297–304. doi:10.1556/AVet.51.2003.3.5. Biser J, Vogel L, Berger J, Hjelle B, Loew SS. 2004. Effects of heavy metals on immunocompetence of white-footed mice (Peromyscus leucopus). J Wildl Dis. 40(2):173–184. doi:10.7589/0090-3558-40.2.173. Brüske I, Hampel R, Socher MM, Rückerl R, Schneider A, Heinrich J, Oberdörster G, Wichmann HE, Peters A. 2010. Impact of ambient air pollution on the differential white blood cell count in patients with chronic pulmonary disease. Inhal Toxicol. 22(3):245–252. doi:10.3109/08958370903207274. Cassee FR, Campbell A, Boere AJF, McLean SG, Duffin R, Krystek P, Gosens I, Miller MR. 2012. The biological effects of subacute inhalation of diesel exhaust following addition of cerium oxide nanoparticles in atherosclerosis-prone mice. Environ Res. 115(1):1–10. doi:10.1016/j.envres.2012.03.004. Chardi AS, Marques CC, Gabriel SI, Silva FC, Cabrita AS, Fuster MJL, Nadal J, Mathias ML. 2008. Haematology, genotoxicity, enzymatic activity and histopathology as biomarkers of metal pollution in the shrew Crocidura russula. Environ Pollut. 156:1332–1339. doi:10.1016/j.envpol.2008.05.015. Cheraghi J, Hosseini E, Hoshmandfar R, Sahraei R. 2013. Hematologic parameters study of male and female rats administrated with different concentrations of silver nanoparticles. Int J Agri Crop Sci. 5(7):789–796. Cohen JW. 1988. Statistical power analysis for the behavioral sciences. 2nd ed. Hillsdale (NJ): Lawrence Erlbaum Associates. Cozzi E, Hazarika S, Stallings HW III, Cascio WE, Devlin RB, Lust RM, Wingard CJ, van Scott MR. 2006. Ultrafine particulate matter exposure augments ischemia-reperfusion injury in mice. Am J Physiol Heart Circ Physiol. 291:H894–H903. doi:10.1152/ajpheart.01362.2005. Cozzi E, Wingard CJ, Cascio WE, Devlin RB, Miles JJ, Bofferding AR, Lust RM, van Scott MR, Henriksen RA. 2007. Effect of ambient particulate matter exposure on hemostasis. Transl Res. 149(6):324–332. doi:10.1016/j. trsl.2006.12.009. [CPCB] Central Pollution Control Board (India). 2010. Status of the vehicular pollution control programme in India. Delhi: Central Pollution Control Board. Programme Objective Series: PROBES/136/2010. Dandona L, Dandona R, Kumar GA, Shukla DK, Paul VK, Balakrishnan K, Prabhakaran D, Tandon N, Salvi S, Dash AP, et al. 2017. Nations within a nation: variations in epidemiological transition across the states of India, 1990–2016 in the global burden of disease study. Lancet. 390:2437–2460. doi:10.1016/S0140-6736(17)31928-1. Das P, Chatterjee P. 2015. Aerobic capacity and haematological response to exercise: a study on school-going regularly exercising boys in two different air pollution zones. J Exerc Sci Fit. 13:99–103. doi:10.1016/j. jesf.2015.08.001. Deng Q, Lu C, Li Y, Sundell J, Norbäck D. 2016b. Exposure to outdoor air pollution during trimesters of pregnancy and childhood asthma, allergic rhinitis, and eczema. Environ Res. 150:119–127. doi:10.1016/j.envres.2016.05.050. Deng Q, Lu C, Yu Y, Li Y, Sundell J, Norbäck D. 2016a. Early life exposure to traffic-related air pollution and allergic rhinitis in preschool children. Respir Med. 121:67–73. doi:10.1016/j.rmed.2016.10.016. INTERNATIONAL JOURNAL OF ENVIRONMENTAL HEALTH RESEARCH 131 Egwurugwu JN, Nwafor A, Ezekwe S. 2013. Impacts of prolonged exposure to gas flares on some blood indices in humans in the Niger Delta Region, Nigeria. Arch Appl Sci Res. 5(1):98–104. Emmerechts J, Jacobs L, Hoylaerts MF. 2011. Air pollution and cardiovascular disease. In: Khallaf M, editor. The impact of air pollution on health, economy, environment and agricultural sources. Rijeka: InTech Europe; p. 69–92. Erdei E, Bobvos J, Brózik M, Paldy A, Farkas I, Vaskövi E, Rudnai P. 2003. Indoor air pollutants and immune biomarkers among Hungarian asthmatic children. Arch Environ Health. 58(6):337–347. Etlik Ö, Tomur A. 2006. The oxidant effects of hyperbaric oxygenation and air pollution in erythrocyte membranes (hyperbaric oxygenation in air pollution). Eur J Gen Med. 3(1):21–28. doi:10.29333/ejgm/82356. Ewetz L. 1993. Absorption and metabolic fate of nitrogen oxides. Scand J Work Environ Health. 19:21–27. Fatma F, Begum N, Sharma B, Pandey RS. 2014. Toxicological evaluation of sulphur dioxide in albino rat, Rattus norvegicus. J Biochem Res. 2(2):18–26. Fauzie AK, Venkataramana GV. 2016. Vehicular particulate emissions in Mysore city. Asian J Environ Sci. 11 (1):78–86. doi:10.15740/HAS/AJES/11.1/78-86. Fauzie AK, Venkataramana GV. 2017. Characterization of particulate matter collected at Mysore city roadways in association with urban traffic condition. Arch Curr Res Int. 10(1):1–17. doi:10.9734/ACRI/2017/36803. Garcia CA, Yap PS, Park HY, Weller BL. 2016. Association of long-term PM2.5 exposure with mortality using different air pollution exposure models: impacts in rural and urban California. Int J Environ Health Res. 26 (2):145–157. doi:10.1080/09603123.2015.1061113. Gurjar BR, van Aardenne JA, Lelievelda J, Mohan M. 2004. Emission estimates and trends (1990–2000) for megacity Delhi and implications. Atmos Environ. 38:5663–5681. doi:10.1016/j.atmosenv.2004.05.057. Harish M. 2013. Urban transport and traffic management – for sustainable transport development in Mysore city. Int J IT Eng Appl Sci Res. 2(3):86–92. Hung NTQ, Lee SB, Hang NT, Kongpran J, Oanh NTK, Shim SG, Bae GN. 2014. Characterization of black carbon at roadside sites and along vehicle roadways in the Bangkok Metropolitan Region. Atmos Environ. 92:213–239. doi:10.1016/j.atmosenv.2014.04.011. Jacobs L, Emmerechts J, Mathieu C, Hoylaerts MF, Fierens F, Hoet PH, Nemery B, Nawrot TS. 2010a. Air pollution-related prothrombotic changes in persons with diabetes. Environ Health Perspect. 118(2):191–196. doi:10.1289/ehp.0900942. Jacobs L, Nawrot TS, de Geus B, Meeusen R, Degraeuwe B, Bernard A, Sughis M, Nemery B, Panis LI. 2010b. Subclinical responses in healthy cyclists briefly exposed to traffic-related air pollution: an intervention study. Environ Health. 9:64. doi:10.1186/1476-069X-9-64. Janhäll S, Molnarb P, Hallquist M. 2012. Traffic emission factors of ultrafine particles: effects from ambient air. J Environ Monit. 14:2488–2496. doi:10.1039/c2em30235g. Jordakieva G, Grabovac I, Valic E, Schmidt KE, Graff A, Schuster A, Sommergruber KH, Oberhuber C, Scheiner O, Goll A, et al. 2018. Occupational exposure to ultrafine particles in police officers: no evidence for adverse respiratory effects. J Occup Med Toxicol. 13(5):1–9. doi:10.1186/s12995-018-0187-8. Kargarfard M, Poursafa P, Rezanejad S, Mousavinasab F. 2011. Effects of exercise in polluted air on the aerobic power, serum lactate level and cell blood count of active individuals. Int J Prev Med. 2(3):145–150. Kooter IM, Boere AJF, Fokkens PHB, Leseman DLAC, Dormans JAMA, Cassee FR. 2006. Response of spontaneously hypertensive rats to inhalation of fine and ultrafine particles from traffic: experimental controlled study. Part Fibre Toxicol. 3:7. doi:10.1186/1743-8977-3-7. Krishnan RM, Sullivan JH, Carlsten C, Wilkerson HW, Beyer RP, Bammler T, Farin F, Peretz A, Kaufman JD. 2013. A randomized cross-over study of inhalation of diesel exhaust, haematological indices, and endothelial markers in humans. Part Fibre Toxicol. 10:7. doi:10.1186/1743-8977-10-56. Larsson BM, Sehlstedt M, Grunewald J, Sköld CM, Lundin A, Blomberg A, Sandström T, Eklund A, Svartengren M. 2007. Road tunnel air pollution induces bronchoalveolar inflammation in healthy subjects. Eur Respir J. 29:699– 705. doi:10.1183/09031936.00035706. Lu C, Deng L, Ou C, Yuan H, Chen X, Deng Q. 2017. Preconceptional and perinatal exposure to traffic-related air pollution and eczema in preschool children. J Dermatol Sci. 85:85–95. doi:10.1016/j.jdermsci.2016.11.004. Lucking AJ, Lundback M, Mills NL, Faratian D, Barath SL, Pourazar J, Cassee FR, Donaldson K, Boon NA, Badimon JJ, et al. 2008. Diesel exhaust inhalation increases thrombus formation in man. Eur Heart J. 29:3043– 3051. doi:10.1093/eurheartj/ehn464. Machiedo GW, Powell RJ, Rush BF Jr. 1989. The incidence of decreased red blood cell deformability in sepsis and the association with oxygen free radical damage and multiple system organ failure. Arch Surg. 124:1386–1389. doi:10.1001/archsurg.1989.01410120032007. Maji S, Ghosh S, Ahmed S. 2018. Association of air quality with respiratory and cardiovascular morbidity rate in Delhi, India. Int J Environ Health Res. 28(5):471–490. doi:10.1080/09603123.2018.1487045. Menzel DB. 1976. The role of free radicals in the toxicity of air pollutants (nitrogen oxides and ozone). In: Pryor WA, editor. Free radicals in biology. New York: Academic Press; p. 181–202. 132 A. K. FAUZIE AND G. V. VENKATARAMANA Mittal H, Roberts L, Fuller GW, O’Driscoll S, Dick MC, Height SE, Thein SL, Rees DC. 2009. The effects of air quality on haematological and clinical parameters in children with sickle cell anaemia. Ann Hematol. 88:529– 533. doi:10.1007/s00277-008-0598-1. Mojiminiyi FBO, Merenu IA, Ibrahim MTO, Njoku CH. 2008. The effect of cement dust exposure on haematological and liver function parameters of cement factory workers in Sokoto, Nigeria. Nigerian J Physiol Sci. 23(1–2):111–114. [MRTH] Ministry of Road Transport and Highways (India). 2017. Road transport year book 2013–14 and 2014–15. New Delhi: Ministry of Road Transport and Highways, Government of India; [accessed 2017 Dec 21]. https:// data.gov.in/catalog/road-transport-year-book-2013-14-and-2014-15. Naghsh N, Noori A, Aqababa H, Dehkordi AS. 2012. Effect of nanosilver particles on alanin amino transferase (ALT) activity and white blood cells (WBC) level in male wistar rats, in vivo condition. Zahedan J Res Med Sci. 14(7):34–37. [NAS] National Academy of Sciences. 2011. Guide for the care and use of laboratory animals. 8th ed. Washington (DC): The National Academies Press. Nemmar A, Subramaniyan D, Yasin J, Ali BH. 2013. Impact of experimental type 1 diabetes mellitus on systemic and coagulation vulnerability in mice acutely exposed to diesel exhaust particles. Part Fibre Toxicol. 10:14. doi:10.1186/1743-8977-10-14. Olajire AA, Azeez L. 2012. Effects of Solanum macrocarpon (African eggplant) on haematological parameters of wistar rats exposed to urban air pollution. Adv Environ Res. 1(2):109–123. doi:10.12989/aer.2012.1.2.109. Pallant J. 2007. SPSS survival manual. 3rd ed. Berkshire: McGraw-Hill Open University Press. Park SS, Kim YJ, Fung K. 2002. PM2.5 carbon measurements in two urban areas: Seoul and Kwangju, Korea. Atmos Environ. 36:1287–1297. doi:10.1016/S1352-2310(01)00552-0. Pathak P, Rana KS. 2012. Effects of air pollution on hematology of parrot–psittacula krameri manillensis at Firozabad City, U.P. Adv Bioresearch. 3(1):96–98. Peakall D. 1992. Animal biomarkers as pollution indicators. London: Chapman and Hall. Poursafa P, Kelishadi R, Amin A, Abasgholi A, Amin MM, Lahijanzadeh M, Modaresi M. 2011. Association of air pollution and hematologic parameters in children and adolescents. J Pediatr (Rio J). 87(4):350–356. doi:10.2223/ JPED.2115. Qasim FK, Ahmed AH. 2013. Effects of welding fume particles on haematological parameters in male albino rats. Zanco J Med Sci. 17(2):422–428. doi:10.15218/zjms.2013.0027. Ramachandra TV, Shwetmala. 2009. Emissions from India’s transport sector: statewise synthesis. Atmos Environ. 43:5510–5517. doi:10.1016/j.atmosenv.2009.07.015. Riediker M, Cascio WE, Griggs TR, Herbst MC, Bromberg PA, Neas L, Williams RW, Devlin RB. 2004. Particulate matter exposure in cars is associated with cardiovascular effects in healthy young men. Am J Respir Crit Care Med. 169:934–940. doi:10.1164/ajrccm.169.8.951. Rückerl R, Phipps RP, Schneider A, Frampton M, Cyrys J, Oberdörster G, Wichmann HE, Peters A. 2007. Ultrafine particles and platelet activation in patients with coronary heart disease – results from a prospective panel study. Part Fibre Toxicol. 4:1. doi:10.1186/1743-8977-4-1. Salvi S, Blomberg A, Rudell B, Kelly F, Sandström T, Holgate ST, Frew A. 1999. Acute inflammatory responses in the airways and peripheral blood after short-term exposure to diesel exhaust in healthy human volunteers. Am J Respir Crit Care Med. 159:702–709. doi:10.1164/ajrccm.159.3.9709083. Schwartz J. 2001. Air pollution and blood markers of cardiovascular risk. Environ Health Perspect. 109(Suppl 3):405–409. Schwartz J. 2004. Air pollution and children’s health. Pediatrics. 113(4):1037–1043. Seaton A, Soutar A, Crawford V, Elton R, McNerlan S, Cherrie J, Watt M, Agius R, Stout R. 1999. Particulate air pollution and the blood. Thorax. 54:1027–1032. Shrivastava R, Neeta S, Geeta G. 2013. Air pollution due to road transportation in India: a review on assessment and reduction strategies. J Environ Res Dev. 8(1):69–77. Steenhof M, Janssen NAH, Strak M, Hoek G, Gosens I, Mudway IS, Kelly FJ, Harrison RM, Pieters RHH, Cassee FR, et al. 2014. Air pollution exposure affects circulating white blood cell counts in healthy subjects: the role of particle composition, oxidative potential and gaseous pollutants – the RAPTES project. Inhal Toxicol. 26(3):141– 165. doi:10.3109/08958378.2013.861884. Tête N, Afonso E, Bouguerra G, Scheifler R. 2015. Blood parameters as biomarkers of cadmium and lead exposure and effects in wild wood mice (Apodemus sylvaticus) living along a pollution gradient. Chemosphere. 138:940– 946. doi:10.1016/j.chemosphere.2014.12.031. Törnqvist H, Mills NL, Gonzalez M, Miller MR, Robinson SD, Megson IL, MacNee W, Donaldson K, Söderberg S, Newby DE, et al. 2007. Persistent endothelial dysfunction in humans after diesel exhaust inhalation. Am J Respir Crit Care Med. 176:395–400. doi:10.1164/rccm.200606-872OC. Torres S, Nowson C. 2007. Relationship between stress, eating behaviour and obesity. Nutrition. 23(11–12):887– 894. doi:10.1016/j.nut.2007.08.008. Venkataramana GV, Fauzie AK, Naveen S. 2018. The status of air pollution attributable to automobile emissions in Mysore: implications for urban transport planning. Clim Chang. 4(16):715–722. INTERNATIONAL JOURNAL OF ENVIRONMENTAL HEALTH RESEARCH 133 Weeks BA, Anderson DP, Dufour AP, Fairbrother A, Goven AJ, Lahavis GP, Peters G. 1992. Immunological biomarkers to assess environmental stress. In: Huggett RJ, Kimerle RA, Mehrle PM Jr, Bergman HL, editors. Biomarkers: biochemical, physiological and histological markers of anthropogenic stress. Lewis (Boca Raton (FL)): SETAC Special Publication Series; p. 211–234. WHO. 2000. Air quality guidelines for Europe. 2nd ed. Copenhagen: World Health Organization. WHO Regional Publications European Series No. 91. WHO. 2006. Air quality guidelines: global update 2005. Copenhagen: World Health Organization Regional Office for Europe. WHO. 2017. The top 10 causes of death. World Health Organization; [accessed 2018 Jan 17]. http://www.who.int/ mediacentre/factsheets/fs310/en/. Wolk E, Kozlowski J. 1989. Changes of body weight and haematological parameters in a fluctuating population of Apodemus flavicollis. Acta Theriol (Warsz). 34(31):439–464. doi:10.4098/AT.arch.89-44. Zuurbier M, Hoek G, Oldenwening M, Meliefste K, Krop E, van Den Hazel P, Brunekreef B. 2011. In-traffic air pollution exposure and CC16, blood coagulation, and inflammation markers in healthy adults. Environ Health Perspect. 119(10):1384–1389. doi:10.1289/ehp.119-a484.
