In this population-based study from southern Sweden, associations were observed between exposure to all investigated local sources of PM2.5 (all-source PM2.5, tailpipe exhaust, vehicle wear-and-tear, and small-scale residential heating) during pregnancy and ASD.
Although only statistically significant in the partially adjusted models and not in the fully adjusted models, the results suggest that both local PM2.5 from tailpipe exhaust and vehicle wear-and-tear contribute to the observed associations with ASD. These results are in line with our previous study on prenatal exposure to ambient NOX concentrations and autism diagnosis using the same cohort (MAPSS), where children in the highest exposure quartile during the entire pregnancy period had a 40% greater risk of developing ASD compared to those in the lowest25. Under an assumption of a causal association between PM2.5 and childhood autism, a health impact assessment conducted for Scania identified 3% of autism cases to be attributable to locally emitted PM2.5, of which ~30% is derived from traffic53. Outside our study setting, two case-control studies from California found 15% increased odds54 and just over double the risk55 of autism development due to traffic-related PM2.5 exposure during pregnancy.
However, previous studies conducted in Stockholm, Sweden, did not find associations between exposure to traffic-related air pollution during pregnancy and ASD56,57. Reasons for this conflict could include that these studies considered PM10 and NOX, while ours investigated PM2.5, which has been the pollutant with the most evidence for autism development in systematic reviews23,24. One register-based study also used “symptoms of neurodevelopmental disorders” as its outcome as opposed to physician-diagnosed ASD56. Similarly, a study using four European cohorts, including a Swedish one, explored autistic traits, both symptom within the borderline/clinical range and within the clinical range using validated cut-offs, but did not find an association with air pollution, even for PM2.5, using land-use regression models, with main predictor variables being traffic, space heating, and population/household density58. Interestingly, in neighboring Denmark, researchers found that exposure to traffic-related NO2, PM10, and PM2.5 in early infancy, not during pregnancy, was associated with autism59. While our study’s emphasis is on the pregnancy period, exposure during the first year of life was also considered, and statistically significant associations with ASD and PM2.5 were found. However, associations for exposure during fetal life appeared stronger in our data (due to high correlation we could not investigate both fetal life and first year of life in the same statistical models). Systematic reviews have noted both the pregnancy period and postnatal periods as decisive exposure windows24.
To the best of our knowledge, no previous study has further separated PM2.5 from road traffic into tailpipe exhaust versus vehicle wear-and-tear and explored their associations with ASD.
No previous epidemiological studies investigating the effects of source-specific ambient wood smoke exposure from small-scale residential heating on autism development could be identified. Concerning neurodegenerative conditions in general, a longitudinal study in northern Sweden has indicated that PM2.5 from residential wood burning is associated with dementia incidence10. In an experimental study using a placental first trimester trophoblast cell line, exposure to wood smoke particles caused cytotoxicity and disrupted proliferation in exposed placenta cells, and particles detected inside cells caused structural damage to mitochondria and endoplasmic reticulum60. In line with this, source-apportionment studies on pregnancy complications found increased concentrations of Delta-C (a marker for wood smoke) and BC during wintertime to be associated with greater odds of developing early-onset preeclampsia61. Similar studies exploring birth outcomes, however, reported that PM2.5 from ambient biomass burning (i.e., wood smoke) was associated with a lower risk of preterm birth16, low birth weight17, and stillbirth18. Authors attributed these findings to the high winter seasonality of biomass burning16; it being inversely associated with most of the other PM2.5 sources explored17; and its negative correlation with re-suspended soil in particular18. In our study, however, no inverse correlations were seen between small-scale residential heating and the other PM2.5 sources.
Due to its unique chemical composition, PM2.5 derived from wood smoke may have varying toxicity compared to other sources of ambient PM2.5. Indeed, a 2003 review stated that studies including residential wood combustion as a major source of PM reported higher relative risks for adverse health outcomes compared to general ambient PM estimations62. Conversely, a 2018 review found that most source-apportionment studies reported PM from ambient biomass to be less detrimental to health than other sources; however, the wood smoke assessed was not necessarily derived from residential wood burning only40. A noted exception from Copenhagen observed stronger point estimates for PM10 apportioned to biomass (mainly wood burning) than PM10 derived from traffic63. Examining the impact of short-term exposure to PM2.5 from traffic and wood smoke on mortality, a study also found higher statistically significant risk-increases when 24-hour average concentrations were used and stronger increased risks for deaths occurring in the cold season, both of which better represent exposure from wood burning64. In the present study, emphasis is placed on the positive associations observed for all PM2.5 sources as opposed to comparing individual point estimates.
Despite the varying results in current literature, the continued inclusion of residential wood burning in source-specific air pollution epidemiology is pertinent, as it has been shown to be a significant source of ambient PM, particularly in the wintertime. According to a recent review, residential wood combustion in developed countries may even dominate PM2.5 and PM10 concentrations during colder seasons, contribute to more than 40% of organic carbon attached to PM2.5 and PM10, and influence ambient elemental carbon (EC) concentrations, which is correlated with BC65. In Sweden, specifically, recreational wood burning accounts for approximately 34% of small houses’ heat demand66. In Scania, emissions from small-scale residential heating (mainly wood burning) have been calculated to comprise 12% of the total PM10, and 21% of the total soot in 2011; this source was even estimated to account for as much as 89% of the local PM2.5 concentrations in certain locations67. Moreover, wood burning’s relative contribution to total EC was more than three times higher at a rural site outside of Gothenburg, Sweden, than an urban one68. Interestingly, authors found that the spatial variability of wood burning aerosols between the two sites was moderate, and that high fossil fuel emissions at the urban site decreased wood smoke’s relative contribution68. This demonstrates that small-scale residential heating is also prominent in urban areas.
No studies could be identified that apportioned PM2.5 traffic emissions into its tailpipe exhaust versus vehicle wear-and-tear components and included autism spectrum disorder as an outcome, thus future ASD research should consider further source-specific separation. The subsequent findings would provide a more detailed understanding of how various PM2.5 sources affect human health. Such information could be used for government-level policy development regarding traffic-related air pollution, such as creating additional regulations on vehicle emissions, including European Emission Standards69, subsidizing electric vehicles70, establishing or revising low-emission zones within cities71,72, or improving protocols for tires, brakes, and road surfaces. Cities can also prioritize reducing traffic overall by investing in public transportation options and establishing safe, convenient active transportation options, such as a networks of bike lanes and well-maintained pedestrian sidewalks73,74. Outside of policy, such research can also inform priority areas for industry-level technological development.
Moreover, recent reviews40,42 on the health effects of wood combustion PM emissions are limited to mainly respiratory outcomes, with some cardiovascular and oncological outcomes included. As the proportion of PM2.5 attributable to residential wood burning can be substantial, future research should consider investigating additional health effects of this PM2.5 source42, especially the development of ASD where current literature is lacking. Additional source-apportionment studies on these associations are needed to substantiate our results. Recreational wood burning has been identified as a potential challenge toward air quality control due to the increase in woodstove use, particularly in urban areas according to a recent study evaluating the air pollution mitigation strategies of 10 European cities, including Malmö, Sweden75. As lighting recreational fires has been linked to positive emotional outcomes, such as decreasing stress and promoting joy76, pollution reduction from this source may need to rely on technological advancements, including combustion efficiency and heat exchanger technology65. The World Health Organization has also recommended “no burn” areas and “no wood burning” days policies as well as public information campaigns on energy efficiency77. Regardless of the mitigation method, the health impacts of reducing ambient PM2.5 and PM10 concentrations from residential wood burning in heaters and fireplaces, particularly in the wintertime, have been shown to be substantial78.
A key strength of this study is its large sample population derived from the MAPSS birth cohort, comprising 98% of all births occurring in the hospital catchment areas in Scania. With this, power and validity were increased. Utilizing a validated, high resolution dispersion model to estimate PM2.5 exposure was also an important strength that improved the reliability of results. Further, this dispersion model incorporates contributions from the various PM2.5 sources separately at baseline, which bypasses the difficult process of disentangling aggregated emissions. Obtaining the exact geographic coordinates of each woman’s home residence was also vital for accurate exposure estimates at the individual level. An additional strength includes the thorough outcome assessment performed by Departments of Child and Adolescent Psychiatry. It should be mentioned, however, that the prerequisites for autism diagnosis have likely changed over time worldwide. A Swedish study, for instance, reported that the presence of significantly fewer autism symptoms appeared to be necessary to receive a clinical autism diagnosis for children diagnosed after preschool (ages 7-12)79. Nevertheless, any such trends in our data should not have affected the results because the analyses were adjusted for birth year.
Furthermore, health care systems in Sweden are tax-subsidized and used by virtually all residents, which increases the ability to identify physician-diagnosed cases of ASD and childhood autism (ICD-10 codes F84 and F84.0, respectively) and record them in high quality healthcare databases. With this, outcome misclassification, response-bias, recall-bias, and selection bias were avoided. Information on covariates incorporated into the adjusted models were similarly collected from well-managed, precise registers. These results are also considered generalizable to pregnant women in other similar study areas, where the sources of air pollution are comparable. The findings are relevant to both public health in general and the clinical setting specifically because they indicate that even small changes in locally produced PM2.5 concentrations can affect the risk of ASD among children. Finally, this study contributes evidence to an emerging research area investigating the health effects of local, source-specific air pollution exposure. Interestingly, accumulating evidence suggests that locally produced PM may be more hazardous to human health than regional, background concentrations80.
This study also has several limitations. For example, quite a large proportion of the study population had missing data on exposure, outcome, and/or covariates. Missing observations were able to be imputed in a sensitivity analysis, which resulted in increased precision of the corresponding estimates, as illustrated by narrower CIs, and relatively unchanged point estimates. Moreover, data on parental diagnoses was not available. Given that genetic factors account for a considerable part of the variation in autism development and emergence81, our results could partly be explained by heredity, if parents with autism were more likely to reside in areas characterized by higher levels of air pollution than parents without autism. Residual confounding due to other risk factors for autism, which are also associated with the exposure and not accounted for in our statistical models, may also be present. However, relevant risk factors have been included, which was based on current literature as well as a directed acyclic graph. Exposure misclassification may exist, as exposure was assessed at each woman’s home residence, and participants’ total exposure, including indoor, behaviour-related, transport-related and occupational, was not considered. This is deemed standard practice in air pollution epidemiology research, with the assumption that the resulting misclassification is non-differential. Results pertaining to the source-specific fractions of PM2.5 derived from tailpipe exhaust produced high point estimates and notably wide confidence intervals. The levels of locally produced PM2.5 in the study area are quite low, which makes the 1 µg/m3 increase in PM2.5 a sizeable increment and can contribute to high point estimates; nevertheless, the study results emphasize that despite being low, the local contribution to PM2.5 may still have detrimental health effects. As the differences in ORs between the various sources were not formally tested and some sources are highly correlated with one another, direct comparisons between our source-specific risk estimates cannot be made. Instead, more studies, preferably in a multi-cohort setting, are needed to increase statistical power. When considering only mothers born in Sweden, the associations tended to be lower than for that of the entire study population. Findings from previous research on environmental injustice in Scania, showed that non-Swedish-born persons had higher odds of being exposed to greater concentrations of air pollution82. In Sweden, children born to women who emigrated from Sub-Saharan Africa and the Middle East are, furthermore, more commonly diagnosed with ASD51. Our results may also suggest that there is some residual confounding with respect to SES in the statistical models.