Long-term exposure to ambient air pollution and autism spectrum disorder in children: A case-control study in Tehran, Iran

The contribution of environmental pollutants to the risk of autism and other neurodevelopmental disorders: A systematic review of case-control studies

Exposure to environmental pollutants, such as metals, pesticides, and air pollutants during early life, is a risk factor for neurodevelopmental disorders (NDDs), including Autism Spectrum Disorder (ASD). Our systematic review aimed to select and summarize more recent case-control studies that examined the association between prenatal and early postnatal exposure to environmental pollutants and NDDs. We searched five databases (Web of Science, PubMed, Embase, Scopus, Ovid), screened 2261 records, and included 24 eligible case-control studies. Meta-analyses were conducted on subgroups of at least three studies that shared both the outcome and the exposure. A noteworthy discovery from this literature review is the existence of non-linear or non-monotonic dose-response relationships between the exposure to certain metals and the risk of ASD. The meta-analysis revealed a significant association between exposure to particular matter (PM)10 during the first year of life and the risk of ASD. Overall, studies included in our systematic review indicate that exposure to several pollutants within the first three years of life was significantly associated with the risk of NDDs.

Investigating the impact of urban-environmental factors on air pollutants: a land use regression model approach and health risk assessment

The presence of pollutants in the earth's atmosphere has a direct impact on human health and the environment. So that pollutants such as carbon monoxide (CO) and particulate matter (PM) cause respiratory diseases, cough headache, etc. Since the amount of pollutants in the air is related to environmental and urban factors, the aim of the current research is to investigate the relationship between the concentration of CO, PM2.5 and PM10 with urban-environmental factors including land use, wind speed and wind direction, topography, traffic, road network, and population through a Land use regression (LUR) model. The concentrations of CO, PM2.5 and PM10 were measured during four seasons from 26th of March 2022 to 16th of March 2023 at 25 monitoring stations and then the information about pollutant measurement points and Land use data were entered into the ArcGIS software. The annual average concentrations of CO, PM2.5 and PM10 were 0.7 ppm, 18.94 and 60.76 µg/m3, respectively, in which the values of annual average concentration of CO and PMs were outside the air quality guideline standard. The results of the health risk assessment showed that the hazard quotient values for all three investigated pollutants were lower than 1 and therefore, they were not in adverse conditions in terms of health effects. Among the urban-environmental factors affecting air pollution, the traffic variable is the most important factor affecting the annual LUR model of CO, PM2.5 and PM10, and then the topography variable is the second most effective factor on the annual LUR model of the aforementioned pollutants.

Ultrafine particulate matter exposure during second year of life, but not before, associated with increased risk of autism spectrum disorder in BKMR mixtures model of multiple air pollutants

Prenatal and early postnatal air pollution exposures have been shown to be associated with autism spectrum disorder (ASD) risk but results regarding specific air pollutants and exposure timing are mixed and no study has investigated the effects of combined exposure to multiple air pollutants using a mixtures approach. We aimed to evaluate prenatal and early life multipollutant mixtures for the drivers of associations of air pollution with ASD. This study examined 484 typically developing (TD) and 660 ASD children from the CHARGE case-control study. Daily air concentrations for NO2, O3, ultrafine (PM0.1), fine (PM0.1-2.5), and coarse (PM2.5-10) particles were predicted from chemical transport models with statistical bias adjustment based on ground-based monitors. Daily averages were calculated for each exposure period (pre-pregnancy, each trimester of pregnancy, first and second year of life) between 2000 and 2016. Air pollution variables were natural log-transformed and then standardized. Individual and joint effects of pollutant exposure with ASD, and potential interactions, were evaluated for each period using hierarchical Bayesian Kernel Machine Regression (BKMR) models, with three groups: PM size fractions (PM0.1, PM0.1-2.5, PM2.5-10), NO2, and O3. In BKMR models, the PM group was associated with ASD in year 2 (group posterior inclusion probability (gPIP) = 0.75), and marginally associated in year 1 (gPIP = 0.497). PM2.5-10 appeared to drive the association (conditional PIP (cPIP) = 0.64) in year 1, while PM0.1 appeared to drive the association in year 2 (cPIP = 0.76), with both showing a moderately strong increased risk. Pre-pregnancy O3 showed a slight J-shaped risk of ASD (gPIP = 0.55). No associations were observed for exposures during pregnancy. Pre-pregnancy O3 and year 2 p.m.0.1 exposures appear to be associated with an increased risk of ASD. Future research should examine ultrafine particulate matter in relation to ASD.

Substantial Changes in Selected Volatile Organic Compounds (VOCs) and Associations with Health Risk Assessments in Industrial Areas during the COVID-19 Pandemic

During the COVID-19 pandemic, governments in many countries worldwide, including India, imposed several restriction measures, including lockdowns, to prevent the spread of the infection. COVID-19 lockdowns led to a reduction in gaseous and particulate pollutants in ambient air. In the present study, we investigated the substantial changes in selected volatile organic compounds (VOCs) after the outbreak of the coronavirus pandemic and associations with health risk assessments in industrial areas. VOC data from 1 January 2019 to 31 December 2021 were collected from the Central Pollution Control Board (CPCB) website, to identify percentage changes in VOC levels before, during, and after COVID-19. The mean TVOC levels at all monitoring stations were 47.22 ± 30.15, 37.19 ± 37.19, and 32.81 ± 32.81 µg/m³ for 2019, 2020, and 2021, respectively. As a result, the TVOC levels gradually declined in consecutive years due to the pandemic in India. The mean TVOC levels at all monitoring stations declined from 9 to 61% during the pandemic period as compared with the pre-pandemic period. In the current study, the T/B ratio values ranged from 2.16 (PG) to 26.38 (NL), which indicated that the major pollutant contributors were traffic and non-traffic sources during the pre-pandemic period. The present findings indicated that TVOC levels had positive but low correlations with SR, BP, RF, and WD, with correlation coefficients (r) of 0.034, 0.118, 0.012, and 0.007, respectively, whereas negative correlations were observed with AT and WS, with correlation coefficients (r) of -0.168 and -0.150, respectively. The lifetime cancer risk (LCR) value for benzene was reported to be higher in children, followed by females and males, for the pre-pandemic, pandemic, and post-pandemic periods. A nationwide scale-up of this study's findings might be useful in formulating future air pollution reduction policies associated with a reduction in health risk factors. Furthermore, the present study provides baseline data for future studies on the impacts of anthropogenic activities on the air quality of a region.

Particulate matter exposure during pregnancy and infancy and risks of autism spectrum disorder in children: A systematic review and meta-analysis

PURPOSE

This meta-analysis aimed to clarify the relationship between particulate matter (PM) and autism spectrum disorder (ASD) in detail.

METHODS

A systematic literature search was performed using eight databases before April 9, 2022. The estimated effects were combined separately according to the PM type. Subgroup analyses were conducted in terms of the study design type, study location, exposure window, birth year, and sex.

RESULTS

PM_2.5 was associated with an increased risk of ASD, while PM₁₀ was not. PMc, PM₁, and diesel particulate matter (DPM) were also associated with an increased risk of ASD. Specifically, a 10 μg/m³ increase in PM_2.5 was associated with a 1.337-fold increased risk of ASD in children, and a 10 μg/m³ increase in PMc and PM₁ may increase the risk of ASD by 1.062 and 3.643 times, respectively. PM_2.5 exposure may increase the risk of ASD in boys. Exposure to PMc might increase the risk of ASD in children born after the year 2000. The combined results of different PM differed between studies with continuous and non-continuous data for different study design type, study location, and birth year. The sensitive window for PM_2.5 exposure to increase the risk of ASD may be from the first, second, and third trimesters to the first year of the postnatal period. Exposure to PMc during pregnancy was significantly associated with ASD.

CONCLUSION

Exposure to PM_2.5 may increase the risk of ASD in boys. Exposure to PM_2.5 during the first, second, and third trimesters and postnatally increased the risk of ASD.

Maternal exposure to cooking oil fumes during pregnancy and autistic-like behaviors in Chinese preschoolers

There is growing evidence that cooking oil fumes (COFs) are harmful indoor air pollutants. However, there is a dearth of research investigating whether maternal COFs exposure during pregnancy may affect children's autistic-like behaviors in China. This study aimed to explore this association, and examine the effects of different cooking fuels and ventilation methods used by mothers on the presence of autistic-like behaviors. This study analyzed the survey data of the Longhua Child Cohort Study in 2017 with a total of 62,372 mothers enrolled in this study. A self-administrative questionnaire was used to collect information on socio-demographic characteristics, cooking habits during pregnancy, and autistic-like behaviors (measured using the Autism Behavior Checklist). After adjusting for potential confounders, the results showed that compared with children whose mothers never cooked during pregnancy, children whose mothers cooked sometimes, often, always during pregnancy had the higher risk of autistic-like behaviors. As the amounts of COFs exposed to and the frequency of cooking during pregnancy increased, the risk of a child's autistic-like behaviors also increased. Mothers using natural gas as cooking fuels had a lower risk of their child having autistic-like behaviors, compared with mothers using coal or other cooking fuels. Furthermore, pregnant women using ventilation measures during cooking significantly decreased likelihood of the presence of autistic-like behaviors in their children. These results suggest that maternal exposure to COFs during pregnancy may increase the likelihood of the presence of autistic-like behaviors in offspring. These findings support a recommendation that pregnant women should avoid exposure to COFs and use clean fuels and ventilation equipment in kitchens to reduce the risk of autistic-like behaviors in children.

Effect of portable air purifier on indoor air quality: reduced exposure to particulate matter and health risk assessment

We sought to investigate the impact of air purifiers in the removal of particular matter (PM)₁₀, PM_2.5, PM_1, and particle number concentration (PNC) in the indoor air of dormitories located at Iran's largest medical university, Tehran University of Medical Sciences. Twelve rooms were selected and randomly assigned to two rooms: sham air purifier system deployed room (SR) and true air purifier system deployed room (TR). All study samples were drawn simultaneously from assigned rooms using portable GRIMM dust monitors for 24 h. The PM monitors of air were positioned in the middle of each room next to the air purifier at the height of the breathing zone (1.5 m in height). The mean PM₁₀, PM_2.5, PM₁, and PNC removal efficiency in rooms with and without a smoker were measured to be 40.7 vs 83.8%, 31.2 vs 78.4%, 29.9 vs 72.3%, and 44.3 vs 75.6%, respectively. The results showed that smoking is an important influencing factor on the indoor air quality; smoking lowered the removal efficiency of PM₁₀, PM_2.5, PM₁, and PNC by 43%, 47%, 43%, and 31%, respectively. An air purifier could decline the PM₁₀ and PM_2.5 even lower than the WHO 24-h guideline level in non-smoker rooms. This study revealed that using household air purifiers in rooms with smokers and non-smokers significantly reduces the non-carcinogenic risks of exposure to PM₁₀ and PM_2.5.

Functional Kriging for Spatiotemporal Modeling of Nitrogen Dioxide in a Middle Eastern Megacity

Long-term hour-specific air pollution exposure estimates have rarely been of interest in epidemiological research. However, this can be relevant for studies that aim to estimate the residential exposure for the hours that subjects mostly spend time there, or for those hours that they may work in another location. Here, we developed a model by spatially predicting the long-term diurnal curves of nitrogen dioxide (NO2) in Tehran, Iran, one of the most polluted and populated megacities in the Middle East. We used the statistical framework of functional data analysis (FDA) including ordinary kriging for functional data (OKFD) and functional analysis of variance (fANOVA) for modeling. The long-term NO2 diurnal curves had two distinct maxima and minima. The absolute minimum value of the city average was 40.6 ppb (around 4:00 p.m.) and the absolute maximum value was 52.0 ppb (around 10:00 p.m.). The OKFD showed the concentrations, the diurnal maximum/minimum values, and their corresponding occurring times varied across the city. The fANOVA highlighted that the effect of population density on the NO2 concentrations is not constant and depends on time within the diurnal period. The provided estimation of long-term hour-specific maps can inform future epidemiological studies to use the long-term mean for specific hour(s) of the day. Moreover, the demonstrated FDA framework can be used as a set of flexible statistical methods.

The epidemiological evidence linking exposure to ambient particulate matter with neurodevelopmental disorders: A systematic review and meta-analysis

BACKGROUND

There has been increasing attention on the associations between ambient particulate matter (PM) in early-life and neurodevelopmental disorders (NDDs). However, the associations remained unclear when considering different types of NDDs and different sizes of PM, and vulnerable exposure windows during early-life were not identified yet.

OBJECTIVE

To synthesize the published literature on the associations between ambient particulate matter (PM) and risk of different types of neurodevelopmental disorders (NDDs) in a systematic review and meta-analysis.

METHODS

A systematic search of Medline, Embase, PubMed, Cochrane Library, and Web of Science was performed from inception through 24 January 2022. Two reviewers conducted the study selection, data extraction, and quality appraisal. A random-effects model was used for meta-analyses with two quality-of-evidence assessments (the Grading of Recommendations Assessment, Development, and Evaluation system and the best evidence synthesis system).

RESULTS

A total of 6554 articles were screened, of which 31 were included in the review, and 20 provided adequate data for meta-analyses. Exposures to particulate matter of 2.5 μm or less (PM_2.5) during prenatal periods (OR, 1.32 [95%CI, 1.03-1.69]), the first year after birth (OR, 1.62 [95%CI, 1.22-2.15]) and the second year after birth (OR, 3.13 [95%CI, 1.47-6.67]) were associated with increased risk of autism spectrum disorders (ASD) in children. The quality of evidence for these associations during early postnatal periods was somewhat moderate with limited studies. We found inconsistent evidence when considering other types of NDDs and different sizes of PM.

CONCLUSIONS AND RELEVANCE

Current evidence indicated that there might be an association between PM_2.5 exposure and higher risk of ASD, and early postnatal periods appeared to be the critical exposure window. High-quality studies are needed to assess the evidence for other types of NDDs.

Exploring the Differences in Molecular Mechanisms and Key Biomarkers Between Membranous Nephropathy and Lupus Nephritis Using Integrated Bioinformatics Analysis

Background: Both membranous nephropathy (MN) and lupus nephritis (LN) are autoimmune kidney disease. In recent years, with the deepening of research, some similarities have been found in the pathogenesis of these two diseases. However, the mechanism of their interrelationship is not clear. The purpose of this study was to investigate the differences in molecular mechanisms and key biomarkers between MN and LN. Method: The expression profiles of GSE99325, GSE99339, GSE104948 and GSE104954 were downloaded from GEO database, and the differentially expressed genes (DEGs) of MN and LN samples were obtained. We used Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) for enrichment analysis of DEGs. A protein-protein interaction (PPI) network of DEGs was constructed using Metascape. We filtered DEGs with NetworkAnalyst. Finally, we used receiver operating characteristic (ROC) analysis to identify the most significant DEGs for MN and LN. Result: Compared with LN in the glomerulus, 14 DEGs were up-regulated and 77 DEGs were down-regulated in MN. Compared with LN in renal tubules, 21 DEGs were down-regulated, but no up-regulated genes were found in MN. According to the result of GO and KEGG enrichment, PPI network and Networkanalyst, we screened out six genes (IFI6, MX1, XAF1, HERC6, IFI44L, IFI44). Interestingly, among PLA2R, THSD7A and NELL1, which are the target antigens of podocyte in MN, the expression level of NELL1 in MN glomerulus is significantly higher than that of LN, while there is no significant difference in the expression level of PLA2R and THSD7A. Conclusion: Our study provides new insights into the pathogenesis of MN and LN by analyzing the differences in gene expression levels between MN and LN kidney samples, and is expected to be used to prepare an animal model of MN that is more similar to human.

Association between prenatal exposure to indoor air pollution and autistic-like behaviors among preschool children

Indoor air pollution is a recognized risk factor for a range of negative health outcomes. This study aimed to investigate the association between maternal prenatal exposure to indoor air pollution and the presence of autistic-like behaviors among preschool children. Data were obtained from the Longhua Child Cohort Study in 2017, in which we enrolled a total of 65 317 preschool children. Associations between maternal exposure to four sources of indoor air pollution (e.g., cooking, environmental tobacco smoke (ETS), mosquito coils, and home decoration) during pregnancy and preschool children's autistic traits were analyzed using multivariate logistic regression. Our results showed that maternal exposure to indoor air pollution from four different sources during pregnancy was associated with the presence of children's autistic-like behaviors. There was dose-response relationship between the accumulative exposure to the four different indoor air pollution sources and the risk of autistic-like behaviors. Furthermore, we found a significant additive interaction between prenatal exposure to both cooking and mosquito coil incense on the risk of autistic-like behaviors. Maternal prenatal exposure to the indoor air pollution from four sources might increase with the risk of autistic-like behaviors being present among preschool children, with an additive interaction effect between some pollution sources.

Effect of short-term exposure to air pollution on COVID-19 mortality and morbidity in Iranian cities

PURPOSE

The association between air pollutant (PM_2.5, PM₁₀, NO₂, and O₃) concentrations and daily number of COVID-19 confirmed cases and related deaths were evaluated in three major Iranian cities (Tehran, Mashhad, and Tabriz).

METHODS

Hourly concentrations of air pollutants and daily number of PCR-confirmed cases and deaths of COVID-19 were acquired (February 20^th, 2020 to January 4^th, 2021). A generalized additive model (GAM) assuming a quasi-Poisson distribution was used to model the associations in each city up to lag-day 7 (for mortality) and 14 (for morbidity). Then, the city-specific estimates were meta-analyzed using a fixed effect model to obtain the overall relative risks (RRs).

RESULTS

A total of 114,964 confirmed cases and 21,549 deaths were recorded in these cities. For confirmed cases, exposure to PM_2.5, NO₂, and O₃ for several lag-days showed significant associations. In case of mortality, meta-analysis estimated that the RRs for PM_2.5, PM₁₀, NO₂, and O₃ concentrations were 1.06 (95% CI: 0.99, 1.13), 1.06 (95% CI: 0.93, 1.19), 1.15 (95% CI: 0.93, 1.38), and 1.07 (95% CI: 0.84, 1.31), respectively. Despite several positive associations with all air pollutants over multiple lag-days, COVID-19 mortality was only significantly associated with NO₂ on lag-days 0-1 and 1 with the RRs of 1.35 (95% CI: 1.04, 1.67) and 1.16 (95% CI: 1.02, 1.31), respectively.

CONCLUSION

This study showed that air pollution can be a factor exacerbating COVID-19 infection and clinical outcomes. Actions should be taken to reduce the exposure of the public and particularly patients to ambient air pollutants.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s40201-021-00736-4.

A space-time analysis of disparities in age at diagnosis of autism spectrum disorder: environmental and socioeconomic risk factors

INTRODUCTION

Early diagnosis of autism is a critical step for gaining early intervention. The earlier interventions begin, the greater chance to reduce symptoms of autism over the lifespan. Despite the improvement in early diagnosis, age at diagnosis varies by residential locations. In order to improve early screening services, this study aims to identify geographic clusters of early and late diagnosis of autism, in addition, it is aimed to compare cases inside the clusters with the rest of the province on characteristics and socioeconomic factors.

MATERIALS AND METHODS

Survey data were collected from 163 autistics born from 1996 to 2011 in Isfahan Province, Iran. As this study found diagnosis of autism occur at an earlier age among children, who on average every 2.5 months increased for each year of age, distance from regression line has been used to determine how early a case was diagnosed compared to other identified cases. After dividing cases into 5 classes based on their distances from the regression line, the ordinal based space-time scan statistic in SaTScan was used to identify geographic areas within specific time periods that have significantly elevated proportions of autistic children who received diagnosis at the earlier or later stages.

RESULTS

The space-time analysis identified two geographic areas that age of diagnosis was inconsistent with the overall study area, the first area has an early diagnosis in central part of Isfahan megacity between 1998 and 2006 (P = .001), the second area shows to have a late diagnosis centered by Najafabad from 2010 through 2015 (P = .007).

CONCLUSIONS

The result of our spatial analysis can be used to evaluate the performance of diagnosis services and additionally provide information to target specific at-risk population for further interventions.

Bacteria bioaerosol in the indoor air of educational microenvironments: Measuring exposures and assessing health effects

Exposure to bioaerosols has been identified to be linked the incidence of various health effects, i.e., infectious diseases, acute toxic effects, allergies, and cancer. The aim of this study was to determine the bacterial bioaerosols in the indoor air of the educational environments of Ardabil universities and to evaluate the exposure and to determine its health risk. In this cross-sectional study, different sections of the educational environments of Ardabil universities were studied. For differential diagnosis of bacteria, methods such as gram staining and biochemical detection methods including DNAse, catalase, oxidase, coagulase, bile esculin hydrolysis test, urease, citrate test, antibiotic resistance to novobiocin and Bacitracin, optochin, glucose uptake, and other differential tests were used. For sampling, a single-stage Anderson sampler was used at a flow rate of 28.3 l at a duration of 10 min per minute. The results showed that, in medical school of Ardabil University of Medical Sciences, the average concentration of bacteria in the outdoor air of school, halls, classes and rooms of professors and staff were 18, 88.4, 76.6, and 77.4 CFU/m³, respectively, and, in Ardabil Islamic Azad University, the average bacterial concentration was 103, 97, 124, and 132 CFU/m³ in the outdoor air of the schools, halls, classrooms, and rooms of professors and staff, respectively. The predominant bacterial species in indoor air are S. aureus, S. epidermidis, Actinomycetes, and Bacillus, respectively. As results indicated, the concentration of bacterial bioaerosols in indoor air is within the standard levels, but due to frequency of bacterial species, occurrence of different in lung and intestinal diseases can be expected among faculty, staff and students in the long-term.

Are climate change and mental health correlated?

Climate change is one of the biggest challenges of our time and is likely to affect human beings in substantial ways. Recently, researchers started paying more attention to the changes in climate and their subsequent impact on the social, environmental and economic determinants of health, and the role they play in causing or exacerbating mental health problems. The effects of climate change-related events on mental well-being could be classified into direct and indirect effects. The direct effects of climate change mostly occur after acute weather events and include post-traumatic stress disorder, anxiety, substance abuse disorder, depression and even suicidal ideation. The indirect effects include economic losses, displacement and forced migration, competition over scarce resources and collective violence. The risk factors for developing those mental health issues include young age, female gender, low socioeconomic status, loss or injury of a loved one, being a member of immigrant groups or indigenous people, pre-existing mental illness and inadequate social support. However, in some individuals, especially those undisturbed by any directly observable effects of climate change, abstract awareness and acknowledgement of the ongoing climate crisis can induce negative emotions that can be intense enough to cause mental health illness. Coping strategies should be provided to the affected communities to protect their mental health from collapse in the face of climate disasters. Awareness of the mental health impacts of climate change should be raised, especially in the high-risk groups. Social and global attention to the climate crisis and its detrimental effects on mental health are crucial.

This paper was written with the aim of trying to understand the currently, scientifically proven impact of climate change-related disasters on mental health and understanding the different methods of solving the problem at the corporate level, by trying to decrease greenhouse gas emissions to zero, and at the individual level by learning how to cope with the impacts of those disasters.

Spatio-temporal modeling of PM2.5 risk mapping using three machine learning algorithms

Urban air pollution is one of the most critical issues that affect the environment, community health, economy, and management of urban areas. From a public health perspective, PM_2.5 is one of the primary air pollutants, especially in Tehran's metropolis. Owing to the different patterns of PM_2.5 in different seasons, Spatio-temporal modeling and identification of high-risk areas to reduce its effects seems necessary. The purpose of this study was Spatio-temporal modeling and preparation of PM_2.5 risk mapping using three machine learning algorithms (random forest (RF), AdaBoost, and stochastic gradient descent (SGD)) in the metropolis of Tehran, Iran. Therefore, in the first step, to prepare the dependent variable data, the PM_2.5 average was used for the four seasons of spring, summer, autumn, and winter. Then, using remote sensing (RS) and a geographic information system (GIS), independent data such as temperature, maximum temperature, minimum temperature, wind speed, rainfall, humidity, normalized difference vegetation index (NDVI), population density, street density, and distance to industrial centers were prepared as a seasonal average. To Spatio-temporal modeling using machine learning algorithms, 70% of the data were used for training and 30% for validation. The frequency ratio (FR) model was used as input to machine learning algorithms to calculate the spatial relationship between PM_2.5 and the effective parameters. Finally, Spatio-temporal modeling and PM_2.5 risk mapping were performed using three machine learning algorithms. The receiver operating characteristic (ROC) area under the curve (AUC) results showed that the RF algorithm had the greatest modeling accuracy, with values of 0.926, 0.94, 0.949, and 0.949 for spring, summer, autumn, and winter, respectively. According to the RF model, the most important variable in spring and autumn was NDVI. Temperature and distance to industrial centers were the most important variables in the summer and winter, respectively. The results showed that autumn, winter, summer, and spring had the highest risk of PM_2.5, respectively.

Level of air BTEX in urban, rural and industrial regions of Bandar Abbas, Iran; indoor-outdoor relationships and probabilistic health risk assessment

This study focused on the measurement of BTEX (benzene, toluene, ethylbenzene and xylene) concentrations in the air of various regions and indoor-outdoor environments in Bandar Abbas, Iran. Air samples were taken actively and analyzed by gas chromatography-mass spectrometry (GC-MS) during two one-month periods i.e., Feb 2020 (period I) and Sep/Oct 2020 (period II). The mean air temperature and the levels of all BTEX compounds were higher in period II. The highest total BTEX (t-BTEX) levels (median [min-max]) were found in the urban region (18.00 [5.21-67.24] μg m^-3), followed by industrial region (7.00 [2.05-14.76] μg m^-3) and rural region (2.81 [ND-7.38] μg m^-3). The significant positive correlations between all BTEX compounds and T/B ratio >1 indicated the vehicular traffic as the main source of emission. At 95th percentile probability, the non-cancer risk of t-BTEX in urban region was only less than one order of magnitude below the threshold level of unity (1.91E-01) and the cancer risk of benzene exceeded the recommended level of 1.0E-06 by U.S. EPA in urban (7.69E-06) and industrial (2.97E-06) regions. It was found that the indoor/outdoor ratio of BTEX concentration in beauty salon and hospital was greater than 1. Overall, the current levels of BTEX in the ambient air of study area, especially near urban roadside and in some indoor environments, should not be overlooked and appropriate mitigation actions should be undertaken.

Role of atmospheric particulate matter exposure in COVID-19 and other health risks in human: A review

Due to intense industrialization and urbanization, air pollution has become a serious global concern as a hazard to human health. Epidemiological studies found that exposure to atmospheric particulate matter (PM) causes severe health problems in human and significant damage to the physiological systems. In recent days, PM exposure could be related as a carrier for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus transmission and Coronavirus disease 2019 (COVID-19) infection. Hence, it is important to understand the adverse effects of PM in human health. This review aims to provide insights on the detrimental effects of PM in various human health problems including respiratory, circulatory, nervous, and immune system along with their possible toxicity mechanisms. Overall, this review highlights the potential relationship of PM with several life-limiting human diseases and their significance for better management strategies.

Climate change, environment pollution, COVID-19 pandemic and mental health

Converging data would indicate the existence of possible relationships between climate change, environmental pollution and epidemics/pandemics, such as the current one due to SARS-CoV-2 virus. Each of these phenomena has been supposed to provoke detrimental effects on mental health. Therefore, the purpose of this paper was to review the available scientific literature on these variables in order to suggest and comment on their eventual synergistic effects on mental health. The available literature report that climate change, air pollution and COVID-19 pandemic might influence mental health, with disturbances ranging from mild negative emotional responses to full-blown psychiatric conditions, specifically, anxiety and depression, stress/trauma-related disorders, and substance abuse. The most vulnerable groups include elderly, children, women, people with pre-existing health problems especially mental illnesses, subjects taking some types of medication including psychotropic drugs, individuals with low socio-economic status, and immigrants. It is evident that COVID-19 pandemic uncovers all the fragility and weakness of our ecosystem, and inability to protect ourselves from pollutants. Again, it underlines our faults and neglect towards disasters deriving from climate change or pollution, or the consequences of human activities irrespective of natural habitats and constantly increasing the probability of spillover of viruses from animals to humans. In conclusion, the psychological/psychiatric consequences of COVID-19 pandemic, that currently seem unavoidable, represent a sharp cue of our misconception and indifference towards the links between our behaviour and their influence on the "health" of our planet and of ourselves. It is time to move towards a deeper understanding of these relationships, not only for our survival, but for the maintenance of that balance among man, animals and environment at the basis of life in earth, otherwise there will be no future.

Exposure to BTEX concentration and the related health risk assessment in printing and copying centers

This study was derived to investigation of BTEX (benzene, toluene, ethylbenzene, xylenes) concentrations in printing and copying centers (PCCs) in Ardabil city of Iran. Fifty-three PCCs were randomly selected from all the 136 number of PCCs and BTEX was sampled form their indoor air. The results showed that the concentration of BTEX in the indoor air PCCs is lower than the OELs (occupational exposure limit) in all cases. The obtained mean concentrations of benzene, toluene, ethylbenzene, and xylene were 93.6±63.2, 150.6±99.2, 34.3±16.8, and 29.5±15.2 μg/m³ respectively. Type of printer, number of printing and copying device, and type of ventilation system had significant influence on the BTEX concentration. The mean inhalation lifetime cancer risk (LTCR) value for benzene and ethylbenzene in the indoor air of the PCCs with LaserJet and inkjet printers was 44.4 × 10^-6 and 153.3 × 10^-6, and 23.4×10^-6 and 54.2× 10^-6, respectively, which were higher than EPA (Environmental Protection Agency) and World Health Organization (WHO) recommended limits. The hazard quotient (HQ) of benzene in the indoor air of the PCCs with inkjet printers was >1, which indicates that the non-carcinogenic risks associated with exposure to these compounds are considerable.

Autism spectrum disorder and air pollution: A systematic review and meta-analysis

Despite the widely-known effects of air pollution, pollutants exposure surrounding pregnancy and the risk for autism spectrum disorder (ASD) in newborns remains controversial. The purpose of our study was to carry out a systematic review and meta-analyses of the risk of ASD in newborns following air pollution exposure during the perinatal period (preconception to second year of life). The PubMed, Cochrane Library, Embase and ScienceDirect databases were searched for articles, published up to July 2020, with the keywords "air pollution" and "autism". Three models were used for each meta-analysis: a global model based on all risks listed in included articles, a pessimistic model based on less favorable data only, and an optimistic model based on the most favorable data only. 28 studies corresponding to a total of 758 997 newborns were included (47190 ASD and 703980 controls). Maternal exposure to all pollutants was associated with an increased risk of ASD in newborns by 3.9% using the global model and by 12.3% using the optimistic model, while the pessimistic model found no change. Each increase of 5 μg/m³ in particulate matter <2.5 μm (PM2.5) was associated with an increased risk of ASD in newborns, regardless of the model used (global +7%, pessimistic +5%, optimistic +15%). This risk increased during preconception (global +17%), during pregnancy (global +5%, and optimistic +16%), and during the postnatal period (global +11% and optimistic +16%). Evidence levels were poor for other pollutants (PM10, NOx, O3, metals, solvents, styrene, PAHs, pesticides). PM2.5 was associated with a greater risk than PM10 (coefficient 0.20, 95CI -0.02 to 0.42), NOx (0.29, 0.08 to 0.50) or solvents (0.24, 0.04 to 0.44). All models revealed that exposure to pollutants, notably PM2.5 during pregnancy, was associated with an increased risk of ASD in newborns. Pregnancy and postnatal periods seem to be the most at-risk periods.

Cardiovascular health effects of wearing a particulate-filtering respirator to reduce particulate matter exposure: a randomized crossover trial

This randomized crossover trial sought to determine whether wearing a high-efficiency particulate-filtering respirator (PFR) improves cardiovascular function over 48 h among healthy college students in Tehran. This trial was conducted from February 14th to 23rd, 2019 and twenty-six participants completed two 48-h intervention periods. Brachial blood pressure (BP) measured by 24-h ambulatory monitoring was the primary health outcome. Secondary outcomes included 48-h heart rate variability (HRV) indices, high-sensitive cardiac troponin (hs-TnT) and other biomarkers. The participants wore the PFR between 10.2 and 11.1 h while awake during the interventions. More than 80% of participants reported increased respiratory resistance while wearing the PFR due to a lack of an exhalation valve. There were no significant differences in brachial BP levels between subjects who wore PFR respirator and those did not. Except for high frequency (HF) power and heart rate (HR), no significant differences between interventions were observed for other HRV metrics. Wearing the PFR led to an increase of 66.0 ms² (95% confidence interval [CI], 9.6-110.5) and 79.6 ms² (95% CI, 19.0-140.1) in HF power during the first day when the two groups of participants wore the PFR. Night-time HR was significantly increased during the PFR intervention period. Other secondary outcomes were not significantly different between interventions. It is plausible that incomplete exposure reduction due to wearing the PFR less than half of the time or increased respiratory resistance mitigated potential health benefits. Additional trials are warranted to validate the CV protection of wearing PFRs in heavily-polluted cities.

Maternal Hypertensive Pregnancy Disorders and Mental and Behavioral Disorders in the Offspring: a Review

Purpose of Review

We review here recent original research and meta-analytic evidence on the associations of maternal hypertensive pregnancy disorders and mental and behavioral disorders in the offspring.

Recent Findings

Seven meta-analyses and 11 of 16 original research studies published since 2015 showed significant associations between maternal hypertensive pregnancy disorders and offspring mental and behavioral disorders. Evidence was most consistent in meta-analyses and high-quality cohort studies. The associations, independent of familial confounding, were observed on different mental and behavioral disorders in childhood and schizophrenia in adulthood. Preterm birth and small-for-gestational age birth emerged as possible moderators and mediators of the associations. Cross-sectional and case-control studies yielded inconsistent findings, but had lower methodological quality.

Summary

Accumulating evidence from methodologically sound studies shows that maternal hypertensive pregnancy disorders are associated with an increased risk of mental and behavioral disorders in the offspring in childhood. More studies on adult mental disorders are needed.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11906-021-01141-w.

Occurrence, spatial distribution, seasonal variations, potential sources, and inhalation-based health risk assessment of organic/inorganic pollutants in ambient air of Tehran

The present study evaluated the concentrations, spatial distribution, seasonal variations, potential sources, and risk assessment of organic/inorganic pollutants in ambient air of Tehran city. Totally, 180 air samples were taken from 9 sampling stations from March 2018 to March 2019 and were analyzed to determine the concentrations of organic pollutants (BTEX compounds and PM_2.5-bound PAHs) plus inorganic pollutants (PM_2.5-bound metals and asbestos fibers). The results revealed that the mean concentrations of ∑ PAHs, BTEX, ∑ heavy metals, and asbestos fibers were 5.34 ng/m³, 60.55 µg/m³, 8585.12 ng/m³, and 4.13 fiber/ml in the cold season, respectively, and 3.88 ng/m³, 33.86 µg/m³, 5682.61 ng/m³, and 3.21 fiber/ml in the warm season, respectively. Source apportionment of emission of the air pollutants showed that PAHs are emitted from diesel vehicles and industrial activities. BTEX and asbestos are also released mainly by vehicles. The results of the inhalation-based risk assessment indicated that the carcinogenic risk of PAHs, BTEX, and asbestos exceeded the recommended limit by The US environmental protection agency (US EPA) and WHO (1 × 10^-4). The risk of carcinogenesis of heavy metal of lead and chromium also exceeded the recommended limit. Thus, proper management strategies are required to control the concentration of these pollutants in Tehran's ambient air in order to maintain the health of Tehran's citizens.

Early-Life Exposure to Environmental Air Pollution and Autism Spectrum Disorder: A Review of Available Evidence

The number of children diagnosed with Autism Spectrum Disorder (ASD) has rapidly increased globally. Genetic and environmental factors both contribute to the development of ASD. Several studies showed linkage between prenatal, early postnatal air pollution exposure and the risk of developing ASD. We reviewed the available literature concerning the relationship between early-life exposure to air pollutants and ASD onset in childhood. We searched on Medline and Scopus for cohort or case-control studies published in English from 1977 to 2020. A total of 20 articles were selected for the review. We found a strong association between maternal exposure to particulate matter (PM) during pregnancy or in the first years of the children’s life and the risk of the ASD. This association was found to be stronger with PM_2.5 and less evident with the other pollutants. Current evidence suggest that pregnancy is the period in which exposure to environmental pollutants seems to be most impactful concerning the onset of ASD in children. Air pollution should be considered among the emerging risk factors for ASD. Further epidemiological and toxicological studies should address molecular pathways involved in the development of ASD and determine specific cause–effect associations.

The Relationship Between Air Pollution and Cognitive Functions in Children and Adolescents: A Systematic Review

Air pollution has a negative impact on one's health and on the central nervous system. We decided to assess studies that evaluated the relationship between air pollution and cognitive functions in children and adolescents by reviewing studies that had been published between January 2009 and May 2019. We searched three major databases for original works (26 studies) and for studies using brain imaging methods based on MRI (six studies). Adverse effects of air pollutants on selected cognitive or psychomotor functions were found in all of the studies. Exposure to nitrogen dioxide, for example, was linked to impaired working memory, general cognitive functions, and psychomotor functions; particulate matter 2.5 was linked to difficulties in working memory, short-term memory, attention, processing speed, and fine motor function; black carbon was linked to poor verbal intelligence, nonverbal intelligence, and working memory; airborne copper was linked to impaired attentiveness and fine motor skills; isophorone was linked to lower mathematical skills; and polycyclic aromatic hydrocarbons in fetal life were linked to lower intelligence scores. The studies using MRI showed that high concentrations of air pollutants were linked to changes in the brain's white matter or lower functional integration and segregation in children's brain networks. In view of the global increase in air pollution, there is a need for further research to elucidate the relationship between air pollution and cognitive and motor development in children. According to some studies, neuroinflammation, the e4 allele of the apolipoprotein E gene, and gutathione-S-transferase gene polymorphism processes may play a role.

A study of cardiorespiratory related mortality as a result of exposure to black carbon

INTRODUCTION

Air pollution is a global phenomenon which invariably leads to a serious environmental and health related sequalae. "Black carbon" (BC), a subset of fine particulate matter ≤2.5 μm (PM_2.5), is a fossil fuel emission by-product and has more recently been recognized as a major health hazard. The objective of this study is to statistically analyze the BC concentration and its correlation with cardiorespiratory related mortality and to estimate the benefits of BC reduction on the health of the population in the capital city of Tehran.

METHODS

We analyzed the ambient air BC concentration and its correlation with cardiorespiratory related mortality and conducted health impact assessment of BC in Tehran (Jan 2018-Jan 2019). The data pertaining to BC concentration was obtained from Tehran's four major pollution monitoring stations. The mortality data was obtained from Tehran's cemetery registry. We calculated and analyzed BC concentration statistics including the mean, standard deviation, coefficient of variation, skewness, and kurtosis. We then assessed the cross-correlation and temporal relationship (0-7 days) between the daily mean concentration of BC for the entire city and cardiorespiratory related mortality. The BenMAP software was utilized to estimate the potential reduction in cardiorespiratory related mortality rates if BC concentration is reduced. Three hypothetical scenarios were employed in the analysis, utilizing the BenMAP software: (I) BC concentration was completely removed from the ambient air; (II) BC concentration was eliminated, and the remaining (non-BC portion of) PM_2.5 concentration was reverted to the United States Environmental Protection Agency (EPA)'s standard level (i.e., 35 μg/m³); and (III) The BC emission during the night (22:00 h-6:00 h, when heavy-duty vehicles (HDVs) are allowed to commute in the city) was distributed throughout the whole day. Since the planetary boundary layer during daytime is much higher than that of nighttime, with the same rate of emission, lower concentrations are spread during the whole day.

RESULTS

The trend of BC concentration variation revealed a persistently higher emission of BC during the nighttime, which is consistent with the large-scale operation of HDVs during these hours in the city of Tehran. We observed a direct correlation between BC concentration and cardiorespiratory related mortality. Analysis also showed a 1.4-day lag period from the time of exposure to BC polluted air and respiratory related deaths, and 2 days for cardiovascular related deaths. As a result, the reduction in BC has significant beneficial effects in reducing potentially preventable cardiorespiratory related mortality. The aforementioned three scenarios for age groups of 30 and above yielded the following results: (I) 11,369 (126 per 100,000 population), (II) 15,386 (171 per 100,000 population), and (III) 2552 (28 per 100,000 population) potentially preventable all-cause (including cardiorespiratory) related deaths annually.

CONCLUSIONS

The BC concentration is relatively high in Tehran and HDVs have a major role in emission of this pollutant. A direct correlation between BC concentration and cardiorespiratory related mortality is observed. There are considerable health benefits in reducing BC concentration in this city. Our findings highlight the urgent need to actively curtail emissions of this harmful pollutant. This can be achieved through utilizing control mechanisms such as particulate filters or amending traffic laws.

Can respirator face masks in a developing country reduce exposure to ambient particulate matter?

Respirator face masks (RFMs) as a personal-level intervention is increasingly being utilized to reduce ambient particulate matter (PM) exposure, globally. We tested the effectiveness of 50 commercially available ones in reducing the exposure of ambient particle number concentrations (PNC), PM₁₀, PM_2.5, and PM₁ (PM ≤ 10, 2.5, and 1 μm in diameter, respectively) in a traffic-affected urban site in Tehran. To examine the efficiency of RFMs, we applied a specific experimental setup including vacuum pumps, dummy heads, connecting tubes, glass chambers, and GRIMM Aerosol Spectrometer to measure all metrics after dummy heads. The average effectiveness of RFMs was in the range of 0.7-83.5%, 3.5-68.1%, 0.8-46.1%, and 0.4-32.2% in reducing ambient PNC, PM₁₀, PM_2.5, and PM₁, respectively. Considering all metrics, the highest effectiveness was observed always for Biomask, followed by 3 M 9332, due to their well-designed physical characteristics (e.g., adjustable nose clip for any face/nose shape, and size, soft inner material in the nose panel to provide a secure seal against leakage, adjustable or elasticated straps/ear loops to better adjust on any face). Biomask reduced ambient PM₁₀ with a mean value of 94.6 μg m^-3 (minimum-maximum: 51.7-100.3 μg m^-3), whereas it filtered on average just 29.0 μg m^-3 (25.7-43.5 μg m^-3) of ambient PM_2.5 and 18.2 μg m^-3 (14.7-21.8 μg m^-3) of PM₁. A fuzzy analytical hierarchy process to find the most important design-related factors of RFMs affecting their effectiveness, which showed the exhalation valve and its diaphragm (20.4%), nose clip (19.7%), and cheek flaps (18.6%) are ranked as the main design-related variables. The fuzzy technique for order preference by similarity to ideal solution indicated that Biomask and 3M 9332 had scores of 1 and 0.97, the highest scores compared with other RFMs. This study provides crucial evidence-based results to elucidate the effectiveness and design-related factors of RFMs in real-environmental circumstances.

Ambient Air Pollution Increases the Risk of Cerebrovascular and Neuropsychiatric Disorders through Induction of Inflammation and Oxidative Stress

Exposure to ambient air pollution is a well-established determinant of health and disease. The Lancet Commission on pollution and health concludes that air pollution is the leading environmental cause of global disease and premature death. Indeed, there is a growing body of evidence that links air pollution not only to adverse cardiorespiratory effects but also to increased risk of cerebrovascular and neuropsychiatric disorders. Despite being a relatively new area of investigation, overall, there is mounting recent evidence showing that exposure to multiple air pollutants, in particular to fine particles, may affect the central nervous system (CNS) and brain health, thereby contributing to increased risk of stroke, dementia, Parkinson's disease, cognitive dysfunction, neurodevelopmental disorders, depression and other related conditions. The underlying molecular mechanisms of susceptibility and disease remain largely elusive. However, emerging evidence suggests inflammation and oxidative stress to be crucial factors in the pathogenesis of air pollution-induced disorders, driven by the enhanced production of proinflammatory mediators and reactive oxygen species in response to exposure to various air pollutants. From a public health perspective, mitigation measures are urgent to reduce the burden of disease and premature mortality from ambient air pollution.

Applying landscape metrics and structural equation modeling to predict the effect of urban green space on air pollution and respiratory mortality in Tehran

Green space and its spatial formation are important elements of public welfare in urban environments and green ecosystems in big cities largely contribute to the mental and physical health of citizens. Tehran is Iran's biggest and most polluted city and air pollution in this city causes loss of human lives due to respiratory diseases. The effect of green area has been less studied in former researches in Tehran, and the reducing effects of green landscape on the mortality of respiratory diseases have not yet been evaluated. To measure the effects of green area landscape patterns (fragmentation, area-edge, shape, and aggregation) on public health, the current study evaluated the pathways and effects of green space on air pollution and the mortality of respiratory diseases using structural equation modeling approach and the partial least squares method. The results of the study indicated green space has a significant mitigating effect on air pollution and mortality of respiratory diseases and also air pollution has a meaningful increasing effect on mortality due to respiratory diseases in Tehran. The most important latent variable in green space is class area that indicates more area of green space is correlated with less mortality of respiratory diseases. The most important indicator of air pollution was the PM_2.5 that needs to be considered and controlled by urban policymakers. Accordingly, maximizing the green area and its cohesion and minimizing fragmentation and green patch edge can contribute to a reduction in air pollution and consequently lower mortality of citizens.

Effects of air pollution on the nervous system and its possible role in neurodevelopmental and neurodegenerative disorders

Recent extensive evidence indicates that air pollution, in addition to causing respiratory and cardiovascular diseases, may also negatively affect the brain and contribute to central nervous system diseases. Air pollution is comprised of ambient particulate matter (PM) of different sizes, gases, organic compounds, and metals. An important contributor to PM is represented by traffic-related air pollution, mostly ascribed to diesel exhaust (DE). Epidemiological and animal studies have shown that exposure to air pollution may be associated with multiple adverse effects on the central nervous system. In addition to a variety of behavioral abnormalities, the most prominent effects caused by air pollution are oxidative stress and neuro-inflammation, which are seen in both humans and animals, and are supported by in vitro studies. Among factors which can affect neurotoxic outcomes, age is considered most relevant. Human and animal studies suggest that air pollution may cause developmental neurotoxicity, and may contribute to the etiology of neurodevelopmental disorders, including autism spectrum disorder. In addition, air pollution exposure has been associated with increased expression of markers of neurodegenerative disease pathologies, such as alpha-synuclein or beta-amyloid, and may thus contribute to the etiopathogenesis of neurodegenerative diseases, particularly Alzheimer's disease and Parkinson's disease.

Temporal variations of ambient air pollutants and meteorological influences on their concentrations in Tehran during 2012-2017

We investigated temporal variations of ambient air pollutants and the influences of meteorological parameters on their concentrations using a robust method; convergent cross mapping; in Tehran (2012–2017). Tehran citizens were consistently exposed to annual PM_2.5, PM₁₀ and NO₂ approximately 3.0–4.5, 3.5–4.5 and 1.5–2.5 times higher than the World Health Organization air quality guideline levels during the period. Except for O₃, all air pollutants demonstrated the lowest and highest concentrations in summertime and wintertime, respectively. The highest O₃ concentrations were found on weekend (weekend effect), whereas other ambient air pollutants had statistically significant (P < 0.05) daily variations in which higher concentrations were observed on weekdays compared to weekend (holiday effect). Hourly O₃ concentration reached its peak at 3.00 p.m., though other air pollutants displayed two peaks; morning and late night. Approximately 45% to 65% of AQI values were in the subcategory of unhealthy for sensitive groups and PM_2.5 was the responsible air pollutant in Tehran. Amongst meteorological factors, temperature was the key influencing factor for PM_2.5 and PM₁₀ concentrations, while nebulosity and solar radiation exerted major influences on ambient SO₂ and O₃ concentrations. Additionally, there is a moderate coupling between wind speed and NO₂ and CO concentrations.

Maternal exposure to air pollution and risk of autism in children: A systematic review and meta-analysis

BACKGROUND

The number of children diagnosed with autism spectrum disorder (ASD) has been increasing. Previous studies suggested potential association between pregnancy air pollution exposure and ASD. This systematic review and meta-analysis is intended to summarize the association between maternal exposure to outdoor air pollution and ASD in children by trimester based on recent studies.

METHODS

A systematic literature search in 3 databases (Medline, Embase, and Web of Science) was performed using subject headings related to ASD and air pollution since 2007. Eligible studies were screened and evaluated based on predetermined criteria. For meta-analyses, the studies were grouped by air pollutant and exposure time (prenatal period and trimesters). Within-group studies were standardized by log odds ratio (OR) and then combined by three meta-analysis methods: frequentist fixed and random effects models, and Bayesian random effects model.

RESULTS

Initial search identified 1564 papers, of which 25 studies remained for final analysis after duplicates and ineligible studies were removed. Of the 25 studies, 13, 14, 12, and 7 studies investigated ASD in children associated with PM_2.5, PM₁₀, NO₂, and ozone, respectively. The frequentist and Bayesian random effects models resulted in different statistical significance. For prenatal period, frequentist meta-analysis returned significant pooled ORs with 95% confidence intervals, 1.06(1.01,1.11) for PM_2.5 and 1.02(1.01,1.04) for NO₂, whereas Bayesian meta-analysis showed similar ORs with wider 95% posterior intervals, 1.06(1.00,1.13) for PM_2.5 and 1.02(1.00,1.05) for NO₂. Third trimester appeared to have higher pooled ORs for PM_2.5, PM₁₀, and ozone, but patterns in the time-varying associations over the trimester were inconsistent.

CONCLUSIONS

For positive association between maternal exposure to ambient air pollution and ASD in children, there is some evidence for PM_2.5, weak evidence for NO₂ and little evidence for PM₁₀ and ozone. However, patterns in associations over trimesters were inconsistent among studies and among air pollutants.

Spatial homogeneity and heterogeneity of ambient air pollutants in Tehran

To investigate spatial inequality of ambient air pollutants and comparison of their heterogeneity and homogeneity across Tehran, the following quantitative indicators were utilized: coefficient of divergence (COD), the 90^th percentile of the absolute differences between ambient air pollutant concentrations and coefficient of variation (CV). Real-time hourly concentrations of particulate matter (PM) and gaseous air pollutants (GAPs) of twenty-two air quality monitoring stations (AQMSs) were obtained from Tehran Air Quality Control Company (TAQCC) in 2017. Annual mean concentrations of PM_2.5, PM_10-2.5, and PM₁₀ (PM_X) ranged from 21.7 to 40.5, 37.3 to 75.0 and 58.0 to 110.4 μg m^-3, respectively. Annual mean PM_2.5 and PM₁₀ concentrations were higher than the World Health Organization air quality guideline (WHO AQG) and national standard levels. NO₂, O₃, SO₂ and CO annual mean concentrations ranged from 27.0 to 76.8, 15.5 to 25.1, 4.6 to 12.2 ppb, and 1.9 to 3.8 ppm over AQMSs, respectively. Our generated spatial maps exhibited that ambient PM_X concentrations increased from the north into south and south-western areas as the hotspots of ambient PM_X in Tehran. O₃ hotspots were observed in the north and south-west, while NO₂ hotspots were in the west and south. COD values of PM_X demonstrated more results lower than the 0.2 cut off compared to GAPs; indicating high to moderate spatial homogeneity for PM_X and moderate to high spatial heterogeneity for GAPs. Regarding CV approach, the spatial variabilities of air pollutants followed in the order of O₃ (87.3%) > SO₂ (65.2%) > CO (61.8%) > PM_10-2.5 (52.5%) > PM_2.5 (48.9%) > NO₂ (48.1%) > PM₁₀ (42.9%), which were mainly in agreement with COD results, except for NO₂. COD values observed a statistically (P < 0.05) positive correlation with the values of the 90^th percentile across AQMSs. Our study, for the first time, highlights spatial inequality of ambient PM_X and GAPs in Tehran in detail to better facilitate establishing new intra-urban control policies.

Characteristics of cohort studies of long-term exposure to PM2.5: a systematic review

This study systematically reviewed all the cohort studies investigating the relationship between long-term exposure to PM_2.5 and any health outcome until February 2018. We searched ISI Web of Knowledge, Pubmed, and Scopus databases for peer-reviewed journal research articles published in English. We only extracted the results of the single-pollutant main analysis of each study, excluding the effect modifications and sensitivity analyses. Out of the initial 9523 articles, 203 articles were ultimately included for analysis. Based on the different characteristics of studies such as study design, outcome, exposure assessment method, and statistical model, we calculated the number and relative frequency of analyses with statistically significant and insignificant results. Most of the studies were prospective (84.8%), assessed both genders (66.5%), and focused on a specific age range (86.8%). Most of the articles (78.1%) had used modeling techniques for exposure assessment of cohorts' participants. Among the total of 317 health outcomes, the most investigated outcomes include mortality due to cardiovascular disease (6.19%), all causes (5.48%), lung cancer (4.00%), ischemic heart disease (3.50%), and non-accidental causes (3.50%). The percentage of analyses with statistically significant results were higher among studies that used prospective design, mortality as the outcome, fixed stations as exposure assessment method, hazard ratio as risk measure, and no covariate adjustment. We can somehow conclude that the choice of right characteristics for cohort studies can make a difference in their results.

Effects of neonatal inhalation exposure to ultrafine carbon particles on pathology and behavioral outcomes in C57BL/6J mice

Background

Recent epidemiological studies indicate early-life exposure to air pollution is associated with adverse neurodevelopmental outcomes. Previous studies investigating neonatal exposure to ambient fine and ultrafine particles have shown sex specific inflammation-linked pathological changes and protracted learning deficits. A potential contributor to the adverse phenotypes from developmental exposure to particulate matter observed in previous studies may be elemental carbon, a well-known contributor to pollution particulate. The present study is an evaluation of pathological and protracted behavioral alterations in adulthood following subacute neonatal exposure to ultrafine elemental carbon. C57BL/6J mice were exposed to ultrafine elemental carbon at 50 μg/m³ from postnatal days 4–7 and 10–13 for 4 h/day. Behavioral outcomes measured were locomotor activity, novel object recognition (short-term memory), elevated plus maze (anxiety-like behavior), fixed interval (FI) schedule of food reward (learning, timing) and differential reinforcement of low rate (DRL) schedule of food reward (impulsivity, inability to inhibit responding). Neuropathology was assessed by measures of inflammation (glial fibrillary-acidic protein), myelin basic protein expression in the corpus callosum, and lateral ventricle area.

Results

Twenty-four hours following the final exposure day, no significant differences in anogenital distance, body weight or central nervous system pathological markers were observed in offspring of either sex. Nor were significant changes observed in novel object recognition, elevated plus maze performance, FI, or DRL schedule-controlled behavior in either females or males.

Conclusion

The limited effect of neonatal exposure to ultrafine elemental carbon suggests this component of air pollution is not a substantial contributor to the behavioral alterations and neuropathology previously observed in response to ambient pollution particulate exposures. Rather, other more reactive constituent species, organic and/or inorganic, gas-phase components, or combinations of constituents may be involved. Defining these neurotoxic components is critical to the formulation of better animal models, more focused mechanistic assessments, and potential regulatory policies for air pollution.

Electronic supplementary material

The online version of this article (10.1186/s12989-019-0293-5) contains supplementary material, which is available to authorized users.