Wei-Jung Tseng1, Jian-He Lu2, How-Ran Chao This email address is being protected from spambots. You need JavaScript enabled to view it.1,2,3,4, Ming-Hsien Tsai2,5, Yu-Ting Chang1, Liang-Jen Wang This email address is being protected from spambots. You need JavaScript enabled to view it.6, Chih-Cheng Chen7, Wan-Nurdiyana-Wan Mansor8, Juliana Jalaludin9, Chih-Lung Wang10,11, Ying-I Tsai  12 

1 Department of Environmental Science and Engineering, College of Engineering, National Pingtung University of Science and Technology, Pingtung 91201, Taiwan
2 Emerging Compounds Research Center, General Research Service Center, National Pingtung University of Science and Technology, Pingtung 91201, Taiwan
3 Institute of Food Safety Management, College of Agriculture, National Pingtung University of Science and Technology, Pingtung 91201, Taiwan
4 School of Dentistry, College of Dental Medicine, Kaohsiung Medical University, Kaohsiung 80708, Taiwan
5 Department of Child Care, College of Humanities and Social Sciences, National Pingtung University of Science and Technology, Pingtung 91201, Taiwan
6 Department of Child and Adolescent Psychiatry, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung 83301, Taiwan
7 Department of Pediatrics, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung 83301, Taiwan
8 Faculty of Ocean Engineering Technology & Informatics, Universiti Malaysia Terengganu, 21300, Malaysia
9 Faculty of Medicine and Health Science, Universiti Putra Malaysia, 43400,  Malaysia
10 Department of Civil Engineering and Geomatics, Cheng Shiu University, Kaohsiung 83347, Taiwan
11 Center for Environmental Toxin and Emerging-contaminant Research, Cheng Shiu University, Kaohsiung 83347, Taiwan
12 Department of Environmental Engineering and Science, Chia Nan University of Pharmacy & Science, Tainan 71710, Taiwan


Received: July 31, 2022
Revised: October 19, 2022
Accepted: October 30, 2022

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.


Download Citation: ||https://doi.org/10.4209/aaqr.220288  


Cite this article:

Tseng, W.J., Lu, J.H., Chao, H.R., Tsai, M.., Chang, Y.T., Wang, L.J., Chen, C.C., Mansor, W.N.W., Jalaludin, J., Wang, C.L., Tsai, Y.I. (2022). Associations between Children's Exposure to PM2.5 and their Serum Inflammatory Responses in Taiwan. Aerosol Air Qual. Res. 22, 220288. https://doi.org/10.4209/aaqr.220288


HIGHLIGHTS

  • PM2.5 had a positive association with children’s inflammatory responses in serum.
  • PM2.5 affected activation of tumor necrosis factor (TNF-α) in school children’s serum.
  • School children living in Traffic-related-air-pollution PM2.5 had the high serum TNF-α level.
 

ABSTRACT


PM2.5-induced inflammation have been demonstrated in the cellular and animal models, but few studies reported the associations of schoolchildren exposure to PM2.5 with their serum inflammatory biomarkers. Our goal was to examine whether serum inflammation was activated after children with long-term exposure to PM2.5 in an industrialized city of Taiwan. Schoolchildren (n = 183) between the ages of 6 and 12 years living in southern Taiwan were recruited to measure their serum inflammation including interferon-γ (IFN-γ), IFN-α2, interleukin 1b (IL-1b), IL-2, IL-6, IL-10, IL-13, IL-17, monocyte chemotactic protein (MCP)-1, and tumor necrosis factor (TNF-α). The subjects were sorted into three groups based on residential addresses in low-PM2.5, high-PM2.5, and traffic-related-air-pollution (TRAP)-PM2.5 areas on the basis of long-term PM2.5 pollution data from air monitoring sites collected over the past decade. Children living in the TRAP-PM2.5 areas had significantly higher TNF-α serum levels than those living in the low-PM2.5 areas. Although serum levels of MCP-1 in children exposed to low PM2.5 concentrations were lower than those in children exposure to high and TRAP PM2.5 levels, the differences were nonsignificant. Principal component (PC) analyses revealed a close association between serum MCP-1 and outdoor PM2.5 (Rotated PC2 (RPC2) and percentage of variance = 10.5%), whereas serum IFN-γ, IFN-α2, IL-1b, IL-2, IL-6, IL-10, IL-13, IL-17, and TNF-α were highly correlated (RPC1 and percentage of variance = 45.5%). Children's serum TNF-α was significantly linked to PM2.5 exposure scenarios (p = 0.031) by the test of the multivariate analysis (adjusted R2 = 0.033, p = 0.039), but the other variables of age (p = 0.147) and gender (p = 0.291) were not statistically significant. In conclusion, serum TNF-α might be positively and significantly correlated with the longitudinal exposure of schoolchildren to PM2.5, especially among children living in TRAP-PM2.5 region.


Keywords: PM2.5, Tumor necrosis factor (TNF-α), Inflammatory responses, Traffic-related air pollution (TRAP), Health risk


1 INTRODUCTION


Airborne particulate, particularly respirable or fine particle matter (PM) whose aerodynamic diameter is less than 2.5 µm (PM2.5), has been recognized as the main contribution to the worst air pollution in decades. The global concern towards PM2.5 pollution due to the increase in adverse health effects including heart attack, cardiovascular disease, pulmonary disease including chronic obstructive pulmonary disease (COPD), lung cancer, chronic kidney disease (CKD), diabetes mellitus (DM), and neurological disorders is increasing (Shah et al., 2013; Meier et al., 2014; Wang et al., 2015; Feng et al., 2016; Kao et al., 2019; Shou et al., 2019; Grande et al., 2021; Li et al., 2021). PM2.5 is emitted from vehicular engines, industrial processing, open biomass burning, chemical industries, cooking fume, resuspended road dust, incomplete combustion from fossil fuels, and agricultural and construction processing (Fann et al., 2012; Aunan and Wang, 2014; Loftus et al., 2015; Chao et al., 2018; Mukherjee and Agrawal, 2018; Chen et al., 2019; Yu et al., 2021). PM2.5 from the long-range atmospheric transport (LRAT) or transboundary transportation through Asian dust storms or monsoon mainly contributed to PM2.5 pollution in the winter and spring seasons of Taiwan (Liu et al., 2017; Shen et al., 2020; Yu et al., 2021). According to the report “Particulate Matters (PM2.5) Precursor Demonstration Guidance” by the United States Environmental Protection Agency (U.S. EPA) (Mathias and Wayland, 2019), nitrogen oxides, sulfur dioxide, volatile organic compounds, aldehydes or ketones, and ammonia, which are the precursors of PM2.5 associated with the traffic-related air pollutants (TRAPs), which was easily developed or converted into PM2.5. TRAP-PM2.5-bound chemicals might be linked with several inorganic or organic pollutants, such as carbon black, polycyclic aromatic hydrocarbons (PAHs), heavy metals, carbonaceous compounds, and chlorinated dioxins/furans (Chao et al., 2016). The PM2.5 is characterized by large surface area and small particle diameter, which enables it to induce a variety of toxic responses (Xing et al., 2017; Chung et al., 2019, 2020). Several environmental factors such as temperature, relative humidity, transboundary pollution, and season variation might be related to the PM2.5 concentrations in the local region of Taiwan (Chen et al., 2019). Compared with PM10 (PM diameter less than 10 µm), the small size and large surface area of PM2.5 render potentially harmful chemicals to adhere to these particles (Hassanvand et al., 2015; Pun et al., 2017; Xing et al., 2017; Chen et al., 2019; Hao et al., 2022).

PM2.5 inhaled directly through the airway will deposit and penetrate the lung alveoli into the blood circulation (Han and Zhuang, 2021; Xie et al., 2021). TRAP PM2.5 poses potential threats to human health by further activating the inflammatory responses, leading to DNA damage or adducts, and causing epigenetics modifications, which is detrimental to lung function (Li et al., 2014; Chao et al., 2018; Hisamuddin et al., 2020; Suhaimi et al., 2021; Putri Anis Syahira et al., 2020). In our previous study (Chao et al., 2018), compared with policemen working office jobs, policemen employed as traffic conductors exhibited significantly higher levels of serum proinflammatory biomarkers, such as tumor necrosis factor (TNF-α), which was attributed to heavy exposure to TRAP PM2.5. The value of TNF-α equivalence (TNFα-EQ) in the serum from the exposed group (the traffic conductors) was 0.0302 ± 0.0048 pg TNFα-EQ mL1 was significantly higher than that whose level was 0.00440 ± 0.000800 pg TNFα-EQ mL1 from the reference control (the office policemen) (Chao et al., 2018). Taxi drivers exhibited systematic inflammation and high concentrations of serum cytokines (e.g., TNF-α) after they were exposed to TRAP (Brucker et al., 2013). PM2.5-bound hazardous air pollutants (HAPs) (e.g., PAHs induced the activation of systematic inflammatory responses, including TNF-α, C-reactive protein (CRP), interleukin-1b (IL-1b), intercellular adhesion molecule-1 (ICAM-1), and vascular adhesion molecule-1 (VCAM-1), in the blood (Huang et al., 2013; Liu et al., 2017; Chao et al., 2018; Wang et al., 2018; Rahmatinia et al., 2021). PM2.5 also induced cytokine release and oxidative stress to be involved lung cancer and chronic airway inflammatory diseases like asthma and COPD (Li et al., 2018).

PM2.5 may trigger neurodevelopmental disruption or delay and may affect neurological behavior in humans. Recent studies also reported a negative association of TRAP PM2.5 with neurological development or function, such as function deterioration of the central nervous system, delay of neurobehavior, and autism spectrum disorder (ASD) in the early age of life (Becerra et al., 2013; Raz et al., 2015; Sram et al., 2017). Aside from TRAP PM2.5, ambient PM2.5 is also a neurotoxicant that triggers harmful effects on the brain, especially during the utero, infancy, and early childhood (Sassá et al., 2011). According to the current epidemiology, prenatal or postnatal children exposed to severe PM2.5 has a high risk of developing ASD and disrupting the motor and cognitive development of infants (Lertxundi et al., 2015; Talbott et al., 2015; Xi and Wu, 2021). A review article exploring the relationship of ASD and attention deficit hyperactivity disorder (ADHD) with PM2.5 exposure concluded that although the evidence was insufficient to establish a causal relationship between ADHD and PM2.5, ADHD development was linked to several factors, such as genome (e.g., transgenerational effects), in-utero exposure, and home environment that were easily influenced by PM2.5, whereas high levels of PM2.5 were associated with acceleration of ASD development (Xi and Wu, 2021). In an animal model, decreased responses of brain-derived neurotrophic factor (BDNF) expression and cAMP response element binding protein (CREB) phosphorylation were observed in the immature rats with high-dose exposure to PM2.5 (Liu et al., 2021). PM2.5 might also disrupt emotional and cognitive development (Liu et al., 2021). The evidence suggests that children's health is susceptible to PM2.5 exposure and PM2.5 is a potentially toxic precursor that causes neurological and neurobehavioral impairment.

 
2 METHODS

 
2.1 The Studying Participants

This cross-sectional study was followed up an established cohort from the Kaohsiung Chang Gung Memorial Hospital (KCGMH) ADHD case-control (KCGMH-ADHD cohort) study. The KCGMH-ADHD cohort was recruited from the Department of Child Psychiatry of KCGMH in southern Taiwan. Specially, patients with ADHD were recruited from the outpatient Department of Child Psychiatry of KCGMH, and children without ADHD were recruited from the communities surrounding KCGMH to serve as controls (Wang et al., 2022a). Of the 234 participants aged 6–12 years from the KCGMH-ADHD cohort, 183 children, comprising 115 ADHD patients (case) and 68 children without ADHD (control), were selected to participate in the present study based on completed questionnaires, inflammatory biomarker data, and submission of a complete and correct residential address. The study protocol was approved by the Institutional Review Boards (IRB) of the Human Ethical Committee of the KCGMH. All ethical standards outlined in the Declarations of Helsinki were followed. After receiving a detailed explanation of the study and its potential consequences, all participants’ parents or guardians signed written informed consent forms before the enrolment. The subjects were selected according to the criteria described in our previous study (Wang et al., 2022a). The inclusion criteria for the patients with ADHD were as follows: (1) verified clinical diagnosis of ADHD according to Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) at the age of 6–12 years; (2) drug-naïve; and (3) no recorded history of comorbid neurodevelopmental or psychiatric disorder, such as ASD, intellectual disability, psychotic disorder, bipolar disorder, epilepsy, or brain injury. The children without ADHD were selected based on the following requirements: (1) age-matched to the study group; (2) without diagnosis of ADHD; and (3) no recorded history of major neurological, neurobehavioral developmental, or psychiatric disorder including ASD, intellectual disability, psychotic disorder, major depressive disorder, substance dependence, or severe head trauma.

 
2.2 Neurological Measurements

The neurological and neurobehavioral status of the participants from the KCGMH-ADHD cohort was determined by an experienced child psychologist by the Wechsler Intelligence Scale for Children-Fourth Edition (WISC-IV) (Baron, 2005; Wang et al., 2022a). The behavioral symptoms were examined using the standard questionnaires of the Swanson, Nolan, and Pelham Version IV Scale (SNAP-IV), specifically the SNAP-IV parent form (answered by the child’s parents) and the SNAP-IV teacher form (answered by the child’s homeroom teacher).

The parent and teacher SNAP-IV forms each comprised a 26-item questionnaire evaluating ADHD symptoms, severity, and needs (Zhang et al., 2005; Bussing et al., 2008). These 26 items included symptoms of inattention (n = 9), hyperactivity/impulsivity (n = 9), and oppositional defiant disorder (n = 8). Each item was scored on a Likert scale ranging from 0 to 3. The present study only selected the core ADHD symptoms (inattention and hyperactivity/impulsivity subscales) for further evaluation.

 
2.3 Biochemical Tests of Inflammatory Markers

Blood samples from the participants were collected between 8:00 and 9:00 AM after an overnight fast. Approximately 2–4 c.c. of serum was collected from each participant. Each blood sample was centrifuged at 3000 rpm for 5 min and then stored at −80°C. Pre-inflammatory biomarkers including TNF-α, monocyte chemotactic protein (MCP)-1, interleukin 1b (IL-1b), IL-2, IL-6, IL-10, IL-13, IL-17, interferon-γ (IFN-γ), and IFN-α2 in the serum were determined using a custom multiplex Laminex immunoassay (Luminex, Austin, TX). Recombinant cytokines pre-diluted in pooled blank plasma from HCs were used as standards for measurement. To detect antigen‐reactive IgG, 50 µL of diluted Microparticle Cocktail was mixed with 50 µL of diluted serum and standard, and then incubated with detection antibodies for 2 h at room temperature (22 ± 3°C) with shaking at 800 rpm. After washing, 50 µL well1 of diluted Biotin-Antibody Cocktail was added and incubated for 1.5 h at room temperature with shaking. The plates were washed again and 50 µL well1 of phycoerythrin-conjugated streptavidin was added and incubated for 30 min at room temperature with shaking. Finally, the concentration of cytokines in the bead array was measured based on the fluorescence intensity calculated using the Flowmetrix software. All the biomarker measurements exceeded the limits of detection (LODs). The relative standard deviations of intra-assay precision and inter-assay precision were within 10% for all measured markers.

 
2.4 PM2.5 in the Ambient Air

The long-term data of PM2.5 in the ambient air from 12 TEPA air monitoring sites (Ciaotou, Nanzi, Mino, Linyuan, Fuxing, Fengshan, Qianzhen, Qianjin, Zuoying, Renwu, Xiaogang, and Daliao) during 2010−2019 and one air monitoring station (Ai-Gou) during 2016-2019 were obtained from the Environmental Protection Bureau Kaohsiung Government (KEPB) (Fig. 1 and Table S1). The historical PM2.5 levels were recorded from the websites of TEPA and KEPB. Finally, three types of PM2.5 exposure scenarios were determined.

Fig. 1. PM2.5 concentrations in 12 air monitoring sites from TEPA or KEPB.Fig. 1PM2.5 concentrations in 12 air monitoring sites from TEPA or KEPB.

 
2.5 Statistical Analysis

Further statistical analysis was conducted on biomarkers with measurements exceeding the LODs. Descriptive analysis was used to determine the means and standard deviations (SD) of demographics, ADHD symptoms, SNAP-IV forms, IFN-α, IFN-γ, IL-16, IL-2, IL-6, IL-10, IL-13, IL-17, MCP-1, and TNF-α stratified by three PM2.5 exposure scenarios. Evaluation of normality was performed using the Shapiro-Wilks or Kolmogorov-Smirnov method. An unpaired independent two-tailed student’s t-test assessed the differences in measured parameters among the three PM2.5 exposure scenario groups. A logistic regression was used to examine levels of inflammatory biomarkers (e.g., TNF-α) among the three PM2.5 exposure scenarios. The multivariate analysis was used to predict inflammatory biomarker levels by the PM2.5 exposure scenarios after the age, gender, or ADHD were adjusted (Model 1 was adjusted for age and gender, Model 2 was adjusted for age, gender, and ADHD.). Model 3 was used to determine the association between ADHD and inflammatory responses with adjustment of age, gender, and PM2.5 exposure scenarios. All statistical analyses were performed using Statistical Product and Service Solutions, version 12.0 (Chicago, Ill., USA). A p-value of less than 0.05 represented statistical significance.

 
3 RESULTS AND DISCUSSION


 
3.1 The Historical Data of PM2.5 in the Studying Region

Upon examining the historical records and long-term exposure data for PM2.5 in the study area (Kaohsiung City), we observed a substantial reduction (almost 50%) in PM2.5 from 2010 to 2019 (Table S2). PM2.5 emissions improved in the study area over the past decade. The study area is the economic center of southern Taiwan and is location of several key industries including manufacturing, steel-making, oil refining, freight transport, and shipbuilding. According to the big data analysis of PM2.5 detected by PM2.5 sensors in Kaohsiung from our previous study (Chen et al., 2019), four main patterns of PM2.5 were concentrated in the industrial zones or the harbors in the northern and southern Kaohsiung. The TRAP PM2.5 pattern was independent of the four main types of PM2.5 patterns (Chen et al., 2019; Yu et al., 2021). In the present study, the Sanmin and Fengshan districts located in the central Kaohsiung were selected as the typical TRAP PM2.5 areas in the present study. According to the historical and annual records of PM2.5 by TEPA and KEPB and the PM2.5 patterns in the studying areas from our previously published paper (Chen et al., 2019), PM2.5 levels in the study areas could be categorized into three types, specifically low (Ciaotou, Nanzi, Mino, and Linyuan), high (Fuxing, Qianzhen, Qianjin, Zuoying, Renwu, Xiaogang, and Daliao), and TRAP (Sanmin and Fengshan) PM2.5. Further statistical analyses were stratified by these three types of PM2.5 exposure scenarios in the present study.

 
3.2 PM2.5 and Serum Cytokines in School-children

The schoolchildren in the present study were categorized into three groups, namely low-PM2.5, high-PM2.5, and TRAP-PM2.5 exposure groups, on the basis of their residential addresses (Table 1). Descriptive analyses of neurological or neurobehavioral development and inflammatory biomarkers were stratified by the three types of PM2.5 exposure scenarios (low-PM2.5, high-PM2.5, and TRAP-PM2.5 areas). Most of the SNAP-IV scores from the parent and teacher questionnaires displayed the significant differences (p < 0.05) among the three PM2.5 exposure groups. ADHD symptoms were highly correlated with SNAP-IV records in the PM2.5 exposure groups. Because the participants in this study were from the KCGMH-ADHD cohort (Wang et al., 2022a), the ratio of children with ADHD symptoms (62.8%) in this study was higher than that in the general Taiwanese population (3%–7%). The sex ratios (male/female) of the cohort in the present study, the prevalence of Taiwanese ADHD cases, and the Taiwanese general population were 2.59, 3.0, and 1.08, respectively. Xi and Wu (2021) found that air pollutants, including PM2.5, was significantly associated with the development of ASD, but the occurrence of ADHD appeared to be connected to genetics, daily diet and nutrition, perinatal conditions, and home environment. Dunn et al. (2019) indicated that ADHD etiology was related to increased serum cytokine levels and the genetic polymorphism of relevant inflammatory pathways, which exacerbated ADHD through inflammatory mechanisms after children were exposed to environmental toxicants. In our previous study, we observed that, compared with children in the control group, children with ADHD had significantly lower plasma TNF-α levels (Wang et al., 2022b). Although PM2.5 exposure could not be definitively linked to ADHD based on the current evidence, several ADHD patients might be comorbid with inflammatory and autoimmune disorders; therefore, the possible contribution of PM2.5 exposure to ADHD was also adjusted for in the present study. PM2.5 penetrates the alveolar cells of the lung directly through the airway and is further transported to the circulatory system by alveolar cells (Han and Zhuang, 2021; Xie et al., 2021). In addition to causing neurological disorders, PM2.5 can lead to premature death caused by inflammatory responses or local or systematic disorders such as lung cancer, cardiovascular diseases, COPD, respiratory disease, and pneumonia (Pun et al., 2017; Mukherjee and Agrawal, 2018; Chowdhury et al., 2019).

Table 1. Neurological or neurological behavioral development and inflammatory responses in children at the age between 5 and 16 with exposure to three scenario types of PM2.5 (n = 183).

In the present study, the IL-6 levels in the low-PM2.5 participants were margin-significantly higher than those in the TRAP-PM2.5 participants (p = 0.088). Compared with the high-PM2.5 participants, the low-PM2.5 participants had a borderline-significantly lower IL-10 level (p = 0.098). MCP-1 concentrations in the higher exposure groups including the high-PM2.5 (p = 0.089) and TRAP-PM2.5 (p = 0.081) participants were higher than those in the low-PM2.5 exposure group, with borderline significance. Finally, the participants in the TRAP-PM2.5 exposure group had the significantly higher TNF-α levels than those in the low-PM2.5 exposure group (p = 0.016), whereas the participants in the high-PM2.5 group exhibited higher TNF-α concentrations than the participants in the low-PM2.5 area did, with the margin significance (p = 0.053).

Several articles have addressed the associations of PM2.5 with the levels of inflammatory cytokines such as TNF-α in schoolchildren. A Mexican study investigated the systematic inflammation in the schoolchildren living in urban (Mexico City: high PM2.5 area) and rural (Polotitlán: low PM2.5 area) areas. They reported that children living in Polotitlán exhibited significantly lower serum levels of TNF-α (26.3 pg mL−1, p = 0.005), IL-1b (4.43 pg mL−1, p = 0.02), and IL-10 (3.54 pg mL−1, p < 0.001) than those living in Mexico City (TNF-α, IL-1b, and IL-10: 66.5, 19.2, and 5.97 pg mL−1, respectively) (Calderón-Garcidueñas et al., 2008). The present study focused on the relationship between PM2.5 exposure and serum cytokine levels in children; our results are consistent with those reported in literatures, particularly regarding the associations between the adults’ exposure to PM2.5 and their serum cytokine concentrations (e.g., TNF-α). In our previous study (Chao et al., 2016), the police officers who worked as traffic conductors were found to have significantly higher levels of serum TNF-α-EQ than the officers who worked in the office by testing an NF-κB reporter gene assay when they were exposed to PM2.5 (the police officers carried PM2.5 personal pumps for estimation of personal PM2.5 exposure). Brucker et al. (2013) found that the serum TNF-α levels in taxi drivers (n = 39) were significantly higher than those in individuals with nonoccupational PM2.5 exposure (n = 21) possibly due to the taxi drivers with occupational exposure to TRAP PM2.5. For diabetes patients, the increased serum levels of IL-6 and TNF-α were positively related to higher levels of PM2.5 (Schneider et al., 2010). Shakya et al. (2019) investigated air pollutants, including PM2.5, in Kathmandu Valley, Nepal, during the spring and summer of 2014 and found PM2.5 emissions of 124 and 45.2 µg m−3 in spring and summer, respectively. They observed that the serum levels of inflammatory cytokines, such as TNF-α and IL-1β, 2, 4, 6, 8, 10, 12, and 13, in traffic officers and train conductors were significantly lower in spring (high PM2.5 period) than in summer (low PM2.5 period), as revealed by an independent t-test and Wilcoxon test (p < 0.05). These findings of Shakya’s report conflict with those of other studies and the present report (Brucker et al., 2013; Calderón-Garcidueñas et al., 2008; Chao et al., 2016; Schneider et al., 2010; Shakya et al., 2019).

In Fig. 2, the PCA test with varimax rotation was used to examine the associations between systematic inflammation, schoolchildren's age and gender, and three types of PM2.5 exposure scenarios. Four principal components (PCs) could account for 73.9% of the total variances if the eigenvalue was set to > 1.00. Rotated PC1 (RPC1) accounted for 45.5% of variance in IL-1b, IL-2, IL-10, IFN-α2, IL-6, IFN-γ, TNF-α, IL-13 levels. RPC2, comprising MCP-1 and the type of PM2.5 exposure, accounted for 10.5% of variance. RPC3 and RPC4 were only associated with the variables of age (9.10% of variance) and sex (8.86% of variance), respectively. PCA tests revealed that the PM2.5 exposure type and serum MCP-1 were closely correlated. Few studies have reported whether serum MCP-1 in children is affected by PM2.5 exposure. Several cellular studies using RAW264.7 cells have revealed that PM2.5 triggers the phosphorylation of NF-κB to activate the gene and protein expression of inflammatory responses (e.g., MCP-1 or TNF-α) (Fu et al., 2020; He et al., 2017). A Chinese research team revealed that BALB/c mice with continuous exposure to PM2.5 exhibited increased MCP-1 expression from the bronchoalveolar lavage fluid (BALF), indicating that the expression of inflammatory cytokines in the lung was substantially induced by PM2.5 (Yang et al., 2020).

Fig. 2. The associations between inflammatory biomarkers, age, and PM2.5 scenarios groups were examined by the test of the principal component analysisFig. 2. The associations between inflammatory biomarkers, age, and PM2.5 scenarios groups were examined by the test of the principal component analysis.

Table 2 presents the odds ratios (ORs) of inflammatory biochemical markers for high-PM2.5 and TRAP-PM2.5 exposure groups in comparison with the reference group of children with low-PM2.5 exposure group. The TRAP-PM2.5 exposure group exhibited significantly higher ORs of IL-2 (OR = 3.61, 95% confidence interval (CI): 1.19–11.0, p = 0.023) and lower ORs of IL-13 (OR = 0.991, 95% CI: 0.983–0.999, p = 0.022) than the reference group. The OR of IL-1b (OR = 0.483 with 95%CI between 0.205 and 1.14, p = 0.095) in TRAP-PM2.5 exposure group had the margin significantly higher than that of the reference control.

Table 2. Odds ratios of inflammatory responses among three PM2.5 exposure scenario groups.

A few epidemiological studies have examined the associations between children's exposure to PM2.5 and cytokine levels in their serum or blood. Calderón-Garcidueñas et al. (2008) showed that childhood exposure to high levels of PM2.5 had significantly higher levels of IL-1β and IL-10 than those exposed to low levels of PM2.5. For Nepalese traffic officers, serum levels of IFN-γ, IL-1β, IL-2, IL-6, IL-8, IL-10, IL-12, IL-13, and TNF-α were significantly higher during summer (TRAP-low-PM2.5 season) than during spring (TRAP-high-PM2.5 season) (Shakya et al., 2019). In the present study, our findings indicated that the children living in the TRAP-PM2.5 areas had lower ORs of serum IL-13 than the controls living in the low-PM2.5 areas, which is consistent with the result of the aforementioned Nepalese study (Shakya et al., 2019).

The inflammatory cytokines in children's serum could be predicted by the independent variables using the general linear models (GLM) in the present study (Tables 3 and 4). In the univariate analysis, serum TNF-α in children could be significantly and weakly explained by the variable of PM2.5 exposure scenarios without adjusting for confounders factors such as age, gender, and ADHD (adjusted R2 = 0.027, p = 0.032). As presented in Table 3, only two inflammatory biomarkers (i.e., MCP-1 and TNF-α) were statistically or marginally significant predictors in Model 1 (the variable of PM2.5 exposure scenarios and the confounders of age and gender) and Model 2 (the variables of PM2.5 exposure scenarios and the confounders of age, gender, and ADHD). In Model 1, TNF-α could be partially explained (adjusted R2 = 0.033, p = 0.039) by the independent variables of age (p = 0.147), gender (p = 0.291), and PM2.5 exposure (p = 0.031) in the multivariate analysis. The children’s serum TNF-α levels were significantly linked to their PM2.5 exposure scenarios in Model 1. In Model 2, the children’s serum TNF-α levels could also be significantly predicted (adjusted R2 = 0.055, p = 0.010) by age (p = 0.290), gender (p = 0.130), PM2.5 exposure scenarios (p = 0.070), and ADHD (p = 0.025). ADHD was significantly correlated with serum TNF-α levels, and the correlation between TNF-α levels and PM2.5 exposure scenarios was marginally or borderline significant in Model 2 in multivariate analysis. Gender was slightly and significantly correlated with ADHD occurrence (R2 = 0.052, p = 0.002), and boys had a significantly higher ADHD prevalence (OR: 2.80, 95% CI: 1.44–5.43, p = 0.002) than girls. On the basis of the results of the multivariate analysis, ADHD was a major variable that significantly affected the GLM of Model 2. In Table 4, the results of Model 3 presented that ADHD is a significant predictor of serum TNF-α in children (p = 0.035), with adjustment for the confounders of age (p = 0.253), gender (p = 0.138), and PM2.5 exposure scenarios (p = 0.044; adjusted R2 = 0.054, p = 0.008). In Model 3, ADHD and PM2.5 exposure scenarios were significantly associated with serum TNF-α. The environmental epidemiological studies have yet to provide adequate evidence on the association of ADHD occurrence in children with the severity of exposure to PM2.5 (Suades-González et al., 2015; Xi and Wu, 2021); rather, PM2.5 exposure scenarios and ADHD occurrence were found to be independent. In the present study, ADHD prevalence and SNAP-IV scores were inversely and significantly correlated with heavily PM2.5 exposure scenarios in Table 1, and ADHD prevalence was significantly correlated with gender among the children. On the basis of our results, we determined that serum TNF-α in our cohort might be slightly or weakly correlated with living in the TRAP-PM2.5 areas, and the influence of ADHD on inflammatory responses must also be adjusted for.

Table 3. The multivariate analyses of inflammatory responses were predicted by the PM2.5 exposure scenario groups with the adjustment of children's age, gender, or ADHD

Table 4. The multivariate analyses of inflammatory responses were predicted by ADHD with the adjustment of children's age, gender, and PM2.5 exposure scenario groups.

The findings of the present study were limited because this is an environmental epidemiological study and the in-vitro and in-vivo tests are not included. Firstly, the statistics were only used to examine the cause-effect with the inflammation in children’s serum after they were longitudinally exposed to PM2.5. This cause-effect relationship was still needed to be tested by the cellular and animal experiments. Secondly, the three PM2.5 exposure scenarios were determined by the long-term PM2.5 data from TEPA air monitor sites. The subjects were assigned into the different PM2.5 exposure scenarios based on the children’s household address. Our subjects might have complicated PM2.5 exposure scenarios. Finally, compared with the general population in Taiwan, the KCGMH-ADHD cohort might have some sampling bias to influence the study’s outcomes.

According to most in-vitro, in-vivo, and epidemiological studies (Brucker et al., 2013; Calderón-Garcidueñas et al., 2008; Chao et al., 2016; Chen et al., 2018; Fu et al., 2020; He et al., 2017; Schneider et al., 2010; Yang et al., 2020), the elevated levels of inflammatory cytokines are linked to the severe PM2.5 exposure. On the basis of our results, certain cytokine biomarkers in the serum, such as IL-2, IL-13, and TNF-α, in the schoolchildren were associated with exposure to PM2.5, particularly for those children in the TRAP areas. The in-vitro and in-vivo studies usually used the high doses of PM2.5 to test their models. Compared with the doses used in the in-vitro and in-vivo models, our epidemiological report used the relatively low doses to examine the cause-effect relationship in the present study. Several environmental epidemiological studies have demonstrated that PM2.5 induces asthma morbidity in both children and adults, especially among those living in TRAP PM2.5 areas (Loftus et al., 2015; Klümper et al., 2015; Orellano et al., 2017; Rosenquist et al., 2020; Jalaludin et al., 2014). Several review articles have reported that PM2.5 might be involved in triggering and aggravating asthma and COPD through PM2.5-induced cytokine release and oxidative stress production (Orellano et al., 2017; Li et al., 2018; Liu et al., 2022). Because few epidemiological studies have focused on the relationship between serum cytokines and PM2.5 exposure in children, the hypothesis of PM2.5-induced activation of systematic inflammation that causes asthma symptoms in schoolchildren is difficult to examine or evaluate. Further studies are warranted to examine the relationships between inflammatory responses and PM2.5 exposure in children.

 
4 CONCLUSIONS


Currently, the evidence from these epidemiological studies was inadequate to verify the correlations between serum inflammatory biomarkers (e.g., inflammatory cytokines) in schoolchildren and outdoor PM2.5. In this study, we examined the serum levels of cytokines in schoolchildren exposed to PM2.5. Several inflammatory biomarkers except for TNF-α in schoolchildren’s serum were not linked with children exposure to PM2.5. Furthermore, our results demonstrated that the serum levels of TNF-α in children were significantly correlated with living in high-PM2.5 exposure areas, particularly TRAP exposure areas. The in-vitro and in-vivo studies were shown that pre-inflammatory and inflammatory responses were activated by the cells or animals after they were exposed to severe PM2.5, but few epidemiological studies examined the cause-effect relationship between serum inflammation and children exposure to heavy PM2.5 pollution. This study might provide the observation to show that TRAP PM2.5 induced the activation of serum TNF-α after children were longitudinally exposed to TRAP.

 
ACKNOWLEDGMENTS


Our study was supported by grants from the Ministry of Science and Technology (MOST 109-2221-E-020-009-MY3 and MOST 111-2314-B-182-018-MY3), Chang Gung Memorial Hospital (CMRPG8K1281 and CMRPG8K1282), and Pingtung Christian Hospital (PS110005). This manuscript was edited by Wallace Academic Editing. The authors wish to thank the joined research teams in the Environmental Health and Risk Assessment Laboratory, National Pingtung University of Science and Technology and Department of Child and Adolescent Psychiatry, and Department of Pediatrics in Kaohsiung Chang Gung Memorial Hospital for their assistance with this study.

 
DISCLAIMER


The authors declare no competing interest.


REFERENCES


  1. Aunan, K., Wang, S. (2014). Internal migration and urbanization in China: Impacts on population exposure to household air pollution (2000–2010). Sci. Total Environ. 481, 186–195. https://doi.org/10.1016/j.scitotenv.2014.02.073

  2. Baron, I.S. (2005). Test review: Wechsler Intelligence Scale for Children-Fourth Edition (WISC-IV). Child Neuropsychol. 11, 471–475. https://doi.org/10.1080/09297040590951587

  3. Becerra, T.A., Wilhelm, M., Olsen, J., Cockburn, M., Ritz, B. (2013). Ambient air pollution and autism in Los Angeles County, California. Environ. Health Perspect. 121, 380–386. https://doi.org/​10.1289/ehp.1205827

  4. Brucker, N., Moro, A.M., Charão, M.F., Durgante, J., Freitas, F., Baierle, M., Nascimento, S., Gauer, B., Bulcão, R.P., Bubols, G.B., Ferrari, P.D., Thiesen, F.V., Gioda, A., Duarte, M.M., de Castro, I., Saldiva, P.H., Garcia, S.C. (2013). Biomarkers of occupational exposure to air pollution, inflammation and oxidative damage in taxi drivers. Sci. Total Environ. 463–464, 884–893. https://doi.org/10.1016/j.scitotenv.2013.06.098

  5. Bussing, R., Fernandez, M., Harwood, M., Wei, H., Garvan, C.W., Eyberg, S.M., Swanson, J.M. (2008). Parent and teacher SNAP-IV ratings of attention deficit hyperactivity disorder symptoms: Psychometric properties and normative ratings from a school district sample. Assessment 15, 317–328. https://doi.org/10.1177/1073191107313888

  6. Calderón-Garcidueñas, L., Villarreal-Calderon, R., Valencia-Salazar, G., Henríquez-Roldán, C., Gutiérrez-Castrellón, P., Torres-Jardón, R., Osnaya-Brizuela, N., Romero, L., Torres-Jardón, R., Solt, A., Reed, W. (2008). Systemic inflammation, endothelial dysfunction, and activation in clinically healthy children exposed to air pollutants. Inhalation Toxicol. 20, 499–506. https://doi.org/10.1080/08958370701864797

  7. Chao, H.R., Hsu, J.W., Ku, H.Y., Wang, S.L., Huang, H.B., Liou, S.H., Tsou, T.C. (2018). Inflammatory response and PM2.5 exposure of urban traffic conductors. Aerosol Air Qual. Res. 18, 2633–2642. https://doi.org/10.4209/aaqr.2018.04.0132

  8. Chao, H.R., Que, D.E., Gou, Y.Y., Chuang, C.Y., Chang, T.Y., Hsu, Y.C. (2016). Indoor and Outdoor concentrations of polybrominated diphenyl ethers on respirable particulate in Central and Southern Taiwan. Aerosol Air Qual. Res. 16, 3187–3197. https://doi.org/10.4209/aaqr.2016.​11.0472

  9. Chen, S., Cui, K., Yu, T.Y., Chao, H.R., Hsu, Y.C., Lu, I.C., Arcega, R.D., Tsai, M.H., Lin, S.L., Chao, W.C., Chen, C., Yu, K.L.J. (2019). A big data analysis of PM2.5 and PM10 from low cost air quality sensors near traffic areas. Aerosol Air Qual. Res. 19, 1721–1733. https://doi.org/10.4209/​aaqr.2019.06.0328

  10. Chen, T., Zhang, J., Zeng, H., Zhang, Y., Zhang, Y., Zhou, X., Zhao, D., Feng, Y., Zhou, H. (2018). The impact of inflammation and cytokine expression of PM2.5 in AML. Oncol. Lett. 16, 2732–2740. https://doi.org/10.3892/ol.2018.8965

  11. Chowdhury, P.H., Honda, A., Ito, S., Okano, H., Onishi, T., Higashihara, M., Okuda, T., Tanaka, T., Hirai, S., Takano, H. (2019). Effects of ambient PM2.5 collected using cyclonic separator from Asian cities on human airway epithelial cells. Aerosol Air Qual. Res. 19, 1808–1819. https://doi.org/10.4209/aaqr.2019.01.0016

  12. Chung, M.C., Tsai, M.H., Que, D.E., Bongo, S.J., Hsu, W.L., Tayo, L.L., Chao, H.R., Lin, Y.H., Lin, S.L., Gou, Y.Y., Hsu, Y.C., Hou, W.C., Huang, K.L. (2019). Fine particulate matter-induced toxic effects in an animal model of Caenorhabditis elegans. Aerosol Air Qual. Res. 19, 1068–1078. https://doi.org/10.4209/aaqr.2019.03.0127

  13. Chung, M.C., Huang, K.L., Avelino, J.L., Tayo, L.L., Lin, C.C., Tsai, M.H., Lin, S.L., Mansor, W.N.W., Su, C.K., Huang, S.T. (2020). Toxic assessment of heavily traffic-related fine particulate matter using an in-vivo wild-type Caenorhabditis elegans Model. Aerosol Air Qual. Res. 20, 1974–1986. https://doi.org/10.4209/aaqr.2020.05.0192

  14. Dunn, G.A., Nigg, J.T., Sullivan, E.L. (2019). Neuroinflammation as a risk factor for attention deficit hyperactivity disorder. Pharmacol. Biochem. Behav. 182, 22–34. https://doi.org/10.1016/j.​pbb.2019.05.005

  15. Fann, N., Baker, K.R., Fulcher, C.M. (2012). Characterizing the PM2.5-related health benefits of emission reductions for 17 industrial, area and mobile emission sectors across the U.S. Environ. Int. 49, 141–151. https://doi.org/10.1016/j.envint.2012.08.017

  16. Feng, S., Gao, D., Liao, F., Zhou, F., Wang, X. (2016). The health effects of ambient PM2.5 and potential mechanisms. Ecotoxicol. Environ. Saf. 128, 67–74. https://doi.org/10.1016/j.ecoenv.2016.01.030

  17. Fu, H., Liu, X., Li, W., Zu, Y., Zhou, F., Shou, Q., Ding, Z. (2020). PM2.5 exposure induces inflammatory response in macrophages via the TLR4/COX-2/NF-κB pathway. Inflammation 43, 1948–1958. https://doi.org/10.1007/s10753-020-01269-y

  18. Grande, G., Wu, J., Ljungman, P.L.S., Stafoggia, M., Bellander, T., Rizzuto, D. (2021). Long-term exposure to PM2.5 and cognitive decline: A longitudinal population-based study. J. Alzheimers Dis. 80, 591–599. https://doi.org/10.3233/jad-200852

  19. Han, X., Zhuang, Y. (2021). PM2.5 induces autophagy-mediated cell apoptosis via PI3K/AKT/mTOR signaling pathway in mice bronchial epithelium cells. Exp. Ther. Med. 21, 1. https://doi.org/​10.3892/etm.2020.9433

  20. Hao, G., Zuo, L., Xiong, P., Chen, L., Liang, X., Jing, C. (2022). Associations of PM2.5 and road traffic noise with mental health: Evidence from UK Biobank. Environ. Res. 207, 112221. https://doi.org/​10.1016/j.envres.2021.112221

  21. Hassanvand, M.S., Naddafi, K., Faridi, S., Nabizadeh, R., Sowlat, M.H., Momeniha, F., Gholampour, A., Arhami, M., Kashani, H., Zare, A., Niazi, S., Rastkari, N., Nazmara, S., Ghani, M., Yunesian, M. (2015). Characterization of PAHs and metals in indoor/outdoor PM10/PM2.5/PM1 in a retirement home and a school dormitory. Sci. Total Environ. 527–528, 100–110. https://doi.org/​10.1016/j.scitotenv.2015.05.001

  22. He, M., Ichinose, T., Yoshida, S., Ito, T., He, C., Yoshida, Y., Arashidani, K., Takano, H., Sun, G., Shibamoto, T. (2017). PM2.5-induced lung inflammation in mice: Differences of inflammatory response in macrophages and type II alveolar cells. J. Appl. Toxicol. 37, 1203–1218. https://doi.org/10.1002/jat.3482

  23. Hisamuddin, N.H., Jalaludin, J., Yusof, A.N., Tualeka, A.R. (2020). Genotoxic effects of exposure to urban traffic related air pollutants on children in Klang Valley, Malaysia. Aerosol Air Qual. Res. 20, 2614–2623. https://doi.org/10.4209/aaqr.2020.03.0114

  24. Huang, H.B., Chen, G.W., Wang, C.J., Lin, Y.Y., Liou, S.H., Lai, C.H., Wang, S.L. (2013). Exposure to heavy metals and polycyclic aromatic hydrocarbons and DNA damage in Taiwanese traffic conductors. Cancer Epidemiol. Biomarkers Prev. 22, 102–108. https://doi.org/10.1158/1055-9965.Epi-12-0706

  25. Jalaludin, J., Noh, S.N.S., Suhaimi, N.F., Akim, A.M. (2014). Tumor necrosis factor-alpha as biomarkers of exposure to indoor pollutants among primary school children in Klang Valley. American J. Appl. Sci. 11, 1616–1630. https://doi.org/10.3844/AJASSP.2014.1616.1630

  26. Kao, C.C., Chen, C.C., Avelino, J.L., Cortez, M.P., Tayo, L.L., Lin, Y.H., Tsai, M.H., Lin, C.W., Hsu, Y.C., Hsieh, L.T., Lin, C., Wang, L.C., Yu, K.L.J., Chao, H.R. (2019). Infants' neurodevelopmental effects of PM2.5 and persistent organohalogen pollutants exposure in Southern Taiwan. Aerosol Air Qual. Res. 19, 2793–2803. https://doi.org/10.4209/aaqr.2019.10.0550

  27. Klümper, C., Krämer, U., Lehmann, I., von Berg, A., Berdel, D., Herberth, G., Beckmann, C., Link, E., Heinrich, J., Hoffmann, B., Schins, R.P. (2015). Air pollution and cytokine responsiveness in asthmatic and non-asthmatic children. Environ. Res. 138, 381–390. https://doi.org/10.1016/j.​envres.2015.02.034

  28. Lertxundi, A., Baccini, M., Lertxundi, N., Fano, E., Aranbarri, A., Martínez, M.D., Ayerdi, M., Álvarez, J., Santa-Marina, L., Dorronsoro, M., Ibarluzea, J. (2015). Exposure to fine particle matter, nitrogen dioxide and benzene during pregnancy and cognitive and psychomotor developments in children at 15 months of age. Environ. Int. 80, 33–40. https://doi.org/​10.1016/j.envint.2015.03.007

  29. Li, G., Huang, J., Wang, J., Zhao, M., Liu, Y., Guo, X., Wu, S., Zhang, L. (2021). Long-term exposure to ambient PM2.5 and increased risk of CKD prevalence in China. J. Am. Soc. Nephrol. 32, 448–458. https://doi.org/10.1681/asn.2020040517

  30. Li, P., Zhao, J., Gong, C., Bo, L., Xie, Y., Kan, H., Song, W. (2014). Association between individual PM2.5 exposure and DNA damage in traffic policemen. J. Occup. Environ. Med. 56, e98–e101. https://doi.org/10.1097/jom.0000000000000287

  31. Li, R., Zhou, R., Zhang, J. (2018). Function of PM2.5 in the pathogenesis of lung cancer and chronic airway inflammatory diseases. Oncol. Lett. 15, 7506–7514. https://doi.org/10.3892/ol.2018.​8355

  32. Liu, C., Cai, J., Qiao, L., Wang, H., Xu, W., Li, H., Zhao, Z., Chen, R., Kan, H. (2017). The acute effects of fine particulate matter constituents on blood inflammation and coagulation. Environ. Sci. Technol. 51, 8128–8137. https://doi.org/10.1021/acs.est.7b00312

  33. Liu, J., Liu, B., Yuan, P., Cheng, L., Sun, H., Gui, J., Pan, Y., Huang, D., Chen, H., Jiang, L. (2021). Role of PKA/CREB/BDNF signaling in PM2.5-induced neurodevelopmental damage to the hippocampal neurons of rats. Ecotoxicol. Environ. Saf. 214, 112005. https://doi.org/10.1016/j.​ecoenv.2021.112005

  34. Liu, K., Hua, S., Song, L. (2022). PM2.5 exposure and asthma development: The key role of oxidative stress. Oxid. Med. Cell. Longevity 2022, 3618806. https://doi.org/10.1155/2022/3618806

  35. Loftus, C., Yost, M., Sampson, P., Arias, G., Torres, E., Vasquez, V.B., Bhatti, P., Karr, C. (2015). Regional PM2.5 and asthma morbidity in an agricultural community: A panel study. Environ. Res. 136, 505–512. https://doi.org/10.1016/j.envres.2014.10.030

  36. Mathias, S., Wayland, R. (2019). Fine Particulate Matter (PM2.5) Precursor Demonstration Guidance. Office of Air Quality Planning and Standards, United States Environmental Protection Agency. (accessed July 2022).

  37. Meier, R., Cascio, W.E., Ghio, A.J., Wild, P., Danuser, B., Riediker, M. (2014). Associations of short-term particle and noise exposures with markers of cardiovascular and respiratory health among highway maintenance workers. Environ. Health Perspect. 122, 726–732. https://doi.org/​10.1289/ehp.1307100

  38. Mukherjee, A., Agrawal, M. (2018). A Global Perspective of Fine Particulate Matter Pollution and Its Health Effects, in: de Voogt, P. (Ed.), Reviews of Environmental Contamination and Toxicology Volume 244, Springer International Publishing, Cham, pp. 5–51. https://doi.org/​10.1007/398_2017_3

  39. Orellano, P., Quaranta, N., Reynoso, J., Balbi, B., Vasquez, J. (2017). Effect of outdoor air pollution on asthma exacerbations in children and adults: Systematic review and multilevel meta-analysis. PLoS One 12, e0174050. https://doi.org/10.1371/journal.pone.0174050

  40. Pun, V.C., Kazemiparkouhi, F., Manjourides, J., Suh, H.H. (2017). Long-term PM2.5 exposure and respiratory, cancer, and cardiovascular mortality in older US adults. Am. J. Epidemiol. 186, 961–969. https://doi.org/10.1093/aje/kwx166

  41. Putri Anis Syahira, M.J., Karmegam, K., Nur Athirah Diyana, M.Y., Irniza, R., Shamsul Bahri, M.T., Vivien, H., Nurul Maizura, H., Sivasankar, S. (2020). Impacts of PM2.5 on respiratory system among traffic policemen. WORK 66, 25–29. https://doi.org/10.3233/WOR-203147

  42. Rahmatinia, T., Kermani, M., Farzadkia, M., Nicknam, M.H., Soleimanifar, N., Mohebbi, B., Jafari, A.J., Shahsavani, A., Fanaei, F. (2021). Potential cytotoxicity of PM2.5-bound PAHs and toxic metals collected from areas with different traffic densities on human lung epithelial cells (A549). J. Environ. Health Sci. Eng. 19, 1701–1712. https://doi.org/10.1007/s40201-021-00724-8

  43. Raz, R., Roberts, A.L., Lyall, K., Hart, J.E., Just, A.C., Laden, F., Weisskopf, M.G. (2015). Autism spectrum disorder and particulate matter air pollution before, during, and after pregnancy: A nested case-control analysis within the Nurses' Health Study II Cohort. Environ. Health Perspect. 123, 264–270. https://doi.org/10.1289/ehp.1408133

  44. Rosenquist, N.A., Metcalf, W.J., Ryu, S.Y., Rutledge, A., Coppes, M.J., Grzymski, J.J., Strickland, M.J., Darrow, L.A. (2020). Acute associations between PM2.5 and ozone concentrations and asthma exacerbations among patients with and without allergic comorbidities. J. Exposure Sci. Environ. Epidemiol. 30, 795–804. https://doi.org/10.1038/s41370-020-0213-7

  45. Sassá, A.H., Higarashi, I.H., Bercini, L.O., Arruda, D.C.d., Marcon, S.S. (2011). Bebê de risco: Acompanhando o crescimento infantil no primeiro ano de vida. Acta Paul Enferm. 24, 541–549. https://doi.org/10.1590/S0103-21002011000400015

  46. Schneider, A., Neas, L.M., Graff, D.W., Herbst, M.C., Cascio, W.E., Schmitt, M.T., Buse, J.B., Peters, A., Devlin, R.B. (2010). Association of cardiac and vascular changes with ambient PM2.5 in diabetic individuals. Part. Fibre Toxicol. 7, 14. https://doi.org/10.1186/1743-8977-7-14

  47. Shah, A.S.V., Langrish, J.P., Nair, H., McAllister, D.A., Hunter, A.L., Donaldson, K., Newby, D.E., Mills, N.L. (2013). Global association of air pollution and heart failure: A systematic review and meta-analysis. Lancet 382, 1039–1048. https://doi.org/10.1016/S0140-6736(13)60898-3

  48. Shakya, K.M., Peltier, R.E., Zhang, Y., Pandey, B.D. (2019). Roadside exposure and inflammation biomarkers among a cohort of traffic police in Kathmandu, Nepal. Int. J. Environ. Res. Public Health 16, 377. https://doi.org/10.3390/ijerph16030377

  49. Shen, H., Yang, T.M., Lu, C.C., Yuan, C.S., Hung, C.H., Lin, C.T., Lee, C.W., Jing, G., Hu, G., Lo, K.C. (2020). Chemical fingerprint and source apportionment of PM2.5 in highly polluted events of southern Taiwan. Environ. Sci. Pollut. Res. Int. 27, 6918–6935. https://doi.org/10.1007/s11356-019-07328-8

  50. Shou, Y., Huang, Y., Zhu, X., Liu, C., Hu, Y., Wang, H. (2019). A review of the possible associations between ambient PM2.5 exposures and the development of Alzheimer's disease. Ecotoxicol. Environ. Saf. 174, 344–352. https://doi.org/10.1016/j.ecoenv.2019.02.086
  51. Sram, R.J., Veleminsky, M., Jr., Veleminsky, M., Sr., Stejskalová, J. (2017). The impact of air pollution to central nervous system in children and adults. Neuro Endocrinol. Lett 38, 389–396.

  52. Suades-González, E., Gascon, M., Guxens, M., Sunyer, J. (2015). Air pollution and neuropsychological development: A review of the latest evidence. Endocrinology 156, 3473–3482. https://doi.org/​10.1210/en.2015-1403

  53. Suhaimi, N.F., Jalaludin, J., Abu Bakar, S. (2021). The influence of traffic-related air pollution (TRAP) in primary schools and residential proximity to traffic sources on histone H3 level in selected Malaysian children. Int. J. Environ. Res. Public Health 18, 7995. https://doi.org/​10.3390/ijerph18157995

  54. Talbott, E.O., Arena, V.C., Rager, J.R., Clougherty, J.E., Michanowicz, D.R., Sharma, R.K., Stacy, S.L. (2015). Fine particulate matter and the risk of autism spectrum disorder. Environ. Res. 140, 414–420. https://doi.org/10.1016/j.envres.2015.04.021

  55. Wang, C., Tu, Y., Yu, Z., Lu, R. (2015). PM2.5 and cardiovascular diseases in the elderly: An overview. Int. J. Environ. Res. Public Health 12, 8187–8197. https://doi.org/10.3390/ijerph​120708187

  56. Wang, C., Chen, R., Shi, M., Cai, J., Shi, J., Yang, C., Li, H., Lin, Z., Meng, X., Liu, C., Niu, Y., Xia, Y., Zhao, Z., Kan, H., Weinberg, C.R. (2018). Possible mediation by methylation in acute inflammation following personal exposure to fine particulate air pollution. Am. J. Epidemiol. 187, 484–493. https://doi.org/10.1093/aje/kwx277

  57. Wang, L.J., Li, S.C., Li, S.W., Kuo, H.C., Lee, S.Y., Huang, L.H., Chin, C.Y., Yang, C.Y. (2022a). Gut microbiota and plasma cytokine levels in patients with attention-deficit/hyperactivity disorder. Transl. Psychiatry 12, 76. https://doi.org/10.1038/s41398-022-01844-x

  58. Wang, L.J., Huang, Y.H., Chou, W.J., Lee, S.Y., Chang, H.Y., Chen, C.C., Chao, H.R. (2022b). Interrelationships among growth hormone, thyroid function, and endocrine-disrupting chemicals on the susceptibility to attention-deficit/hyperactivity disorder. Eur. Child Adolesc. Psychiatry. https://doi.org/10.1007/s00787-021-01886-4

  59. Xi, T., Wu, J. (2021). A review on the mechanism between different factors and the occurrence of autism and ADHD. Psychol. Res. Behav. Manage. 14, 393–403. https://doi.org/10.2147/prbm.​S304450

  60. Xie, W., You, J., Zhi, C., Li, L. (2021). The toxicity of ambient fine particulate matter (PM2.5) to vascular endothelial cells. J. Appl. Toxicol. 41, 713–723. https://doi.org/10.1002/jat.4138

  61. Xing, J., Tang, H., Lee, W.J., Wang, L.C., Zhu, J., Huang, Q., Cui, K. (2017). Part II: PM2.5 and polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs) in the ambient air of northern China. Aerosol Air Qual. Res. 17, 2010–2026. https://doi.org/10.4209/aaqr.2017.06.0211

  62. Yang, Y., Li, X., An, X., Zhang, L., Li, X., Wang, L., Zhu, G. (2020). Continuous exposure of PM2.5 exacerbates ovalbumin-induced asthma in mouse lung via a JAK-STAT6 signaling pathway. dv. Clin. Exp. Med. 29, 825–832. https://doi.org/10.17219/acem/121009

  63. Yu, T.Y., Chao, H.R., Tsai, M.H., Lin, C.C., Lu, I.C., Chang, W.H., Chen, C.C., Wang, L.J., Lin, E.T., Chang, C.T., Chen, C., Kao, C.C., Wan Mansor, W.N., Yu, K.L.J. (2021). Big data analysis for effects of the COVID-19 outbreak on ambient PM2.5 in areas that were not locked down. Aerosol Air Qual. Res. 21, 210020. https://doi.org/10.4209/aaqr.210020

  64. Zhang, S., Faries, D.E., Vowles, M., Michelson, D. (2005). ADHD Rating Scale IV: Psychometric properties from a multinational study as a clinician-administered instrument. Int. J. Methods Psychiatr. Res. 14, 186–201. https://doi.org/10.1002/mpr.7


Share this article with your colleagues 

 

Subscribe to our Newsletter 

Aerosol and Air Quality Research has published over 2,000 peer-reviewed articles. Enter your email address to receive latest updates and research articles to your inbox every second week.

7.3
2022CiteScore
 
 
77st percentile
Powered by
Scopus
 
   SCImago Journal & Country Rank

2022 Impact Factor: 4.0
5-Year Impact Factor: 3.4

Call for Papers for the special issue on: "Carbonaceous Aerosols in the Atmosphere"

Aerosol and Air Quality Research partners with Publons

CLOCKSS system has permission to ingest, preserve, and serve this Archival Unit
CLOCKSS system has permission to ingest, preserve, and serve this Archival Unit

Aerosol and Air Quality Research (AAQR) is an independently-run non-profit journal that promotes submissions of high-quality research and strives to be one of the leading aerosol and air quality open-access journals in the world. We use cookies on this website to personalize content to improve your user experience and analyze our traffic. By using this site you agree to its use of cookies.