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Objective: Headache is one of the most prevalent and disabling health conditions globally. We prospectively explored the urban exposome in relation to weekly occurrence of headache episodes using data from the Dutch population-based Occupational and Environmental Health Cohort Study (AMIGO). Material and Methods: Participants (N = 7,339) completed baseline and follow-up questionnaires in 2011 and 2015, reporting headache frequency. Information on the urban exposome covered 80 exposures across 10 domains, such as air pollution, electromagnetic fields, and lifestyle and socio-demographic characteristics. We first identified all relevant exposures using the Boruta algorithm and then, for each exposure separately, we estimated the average treatment effect (ATE) and related standard error (SE) by training causal forests adjusted for age, depression diagnosis, painkiller use, general health indicator, sleep disturbance index and weekly occurrence of headache episodes at baseline. Results: Occurrence of weekly headache was 12.5 % at baseline and 11.1 % at follow-up. Boruta selected five air pollutants (NO2, NOX, PM10, silicon in PM10, iron in PM2.5) and one urban temperature measure (heat island effect) as factors contributing to the occurrence of weekly headache episodes at follow-up. The estimated causal effect of each exposure on weekly headache indicated positive associations. NO2 showed the largest effect (ATE = 0.007 per interquartile range (IQR) increase; SE = 0.004), followed by PM10 (ATE = 0.006 per IQR increase; SE = 0.004), heat island effect (ATE = 0.006 per one-degree Celsius increase; SE = 0.007), NOx (ATE = 0.004 per IQR increase; SE = 0.004), iron in PM2.5 (ATE = 0.003 per IQR increase; SE = 0.004), and silicon in PM10 (ATE = 0.003 per IQR increase; SE = 0.004). Conclusion: Our results suggested that exposure to air pollution and heat island effects contributed to the reporting of weekly headache episodes in the study population.

Background: Each new generation of mobile phone technology has triggered discussions about potential carcinogenicity from exposure to radiofrequency electromagnetic fields (RF-EMF). Available evidence has been insufficient to conclude about long-term and heavy mobile phone use, limited by differential recall and selection bias, or crude exposure assessment. The Cohort Study on Mobile Phones and Health (COSMOS) was specifically designed to overcome these shortcomings. Methods: We recruited participants in Denmark, Finland, the Netherlands, Sweden, and the UK 2007–2012. The baseline questionnaire assessed lifetime history of mobile phone use. Participants were followed through population-based cancer registers to identify glioma, meningioma, and acoustic neuroma cases during follow-up. Non-differential exposure misclassification was reduced by adjusting estimates of mobile phone call-time through regression calibration methods based on self-reported data and objective operator-recorded information at baseline. Hazard ratios (HR) and 95% confidence intervals (CI) for glioma, meningioma, and acoustic neuroma in relation to lifetime history of mobile phone use were estimated with Cox regression models with attained age as the underlying time-scale, adjusted for country, sex, educational level, and marital status. Results: 264,574 participants accrued 1,836,479 person-years. During a median follow-up of 7.12 years, 149 glioma, 89 meningioma, and 29 incident cases of acoustic neuroma were diagnosed. The adjusted HR per 100 regression-calibrated cumulative hours of mobile phone call-time was 1.00 (95 % CI 0.98–1.02) for glioma, 1.01 (95 % CI 0.96–1.06) for meningioma, and 1.02 (95 % CI 0.99–1.06) for acoustic neuroma. For glioma, the HR for ≥ 1908 regression-calibrated cumulative hours (90th percentile cut-point) was 1.07 (95 % CI 0.62–1.86). Over 15 years of mobile phone use was not associated with an increased tumour risk; for glioma the HR was 0.97 (95 % CI 0.62–1.52). Conclusions: Our findings suggest that the cumulative amount of mobile phone use is not associated with the risk of developing glioma, meningioma, or acoustic neuroma.

Introduction: In this study, we aimed to characterise exposure to pyrethroids, organophosphates, and tebuconazole through multiple pathways in 110 parent–child pairs participating in the CELSPAC–SPECIMEn study. Methods: First, we estimated the daily intake (EDI) of pesticides based on measured urinary metabolites. Second, we compared EDI with estimated pesticide intake from food. We used multiple linear regression to identify the main predictors of urinary pesticide concentrations. We also assessed the relationship between urinary pesticide concentrations and organic and non-organic food consumption while controlling for a range of factors. Finally, we employed a model to estimate inhalation and dermal exposure due to spray drift and volatilization after assuming pesticide application in crop fields. Results: EDI was often higher in children in comparison to adults, especially in the winter season. A comparison of food intake estimates and EDI suggested diet as a critical pathway of tebuconazole exposure, less so in the case of organophosphates. Regression models showed that consumption per g of peaches/apricots was associated with an increase of 0.37% CI [0.23% to 0.51%] in urinary tebuconazole metabolite concentrations. Consumption of white bread was associated with an increase of 0.21% CI [0.08% to 0.35%], and consumption of organic strawberries was inversely associated (-61.52% CI [-79.34% to -28.32%]), with urinary pyrethroid metabolite concentrations. Inhalation and dermal exposure seemed to represent a relatively small contribution to pesticide exposure as compared to dietary intake. Conclusion: In our study population, findings indicate diet plays a significant role in exposure to the analysed pesticides. We found an influence of potential exposure due to spray drift and volatilization among the subpopulation residing near presumably sprayed crop fields to be minimal in comparison. However, the lack of data indicating actual spraying occurred during the critical 24-hour period prior to urine sample collection could be a significant contributing factor.

Background: Maternal occupational exposure to pesticides has been linked to adverse birth outcomes but associations with residential pesticide exposures are inconclusive. Objectives: To explore associations between residential exposure to specific pesticides and birth outcomes using individual level exposure and pregnancy/birth data. Methods: From all 2009–2013 singleton births in the Dutch birth registry, we selected mothers > 16 years old living in non-urban areas, who had complete address history and changed addresses at most once during pregnancy (N = 339,947). We estimated amount (kg) of 139 active ingredients (AI) used within buffers of 50, 100, 250 and 500 m around each mother's home during pregnancy. We used generalized linear models to investigate associations between 12 AIs with evidence of reproductive toxicity and gestational age (GA), birth weight (BW), perinatal mortality, child́s sex, prematurity, low birth weight (LBW), small for gestational age (SGA) and large for gestational age (LGA), adjusting for individual and area-level confounders. For the remainder 127 AIs, we used minimax concave penalty with a stability selection step to identify those that could be related to birth outcomes. Results: Regression analyses showed that maternal residential exposure to fluroxypyr-meptyl was associated with longer GA, glufosinate-ammonium with higher risk of LBW, linuron with higher BW and higher odds of LGA, thiacloprid with lower odds of perinatal mortality and vinclozolin with longer GA. Variable selection analysis revealed that picoxystrobin was associated with higher odds of LGA. We found no evidence of associations with other AIs. Sensitivity and additional analysis supported these results except for thiacloprid. Discussion: In this exploratory study, pregnant women residing near crops where fluroxypyr-meptyl, glufosinate-ammonium, linuron, vinclozolin and picoxystrobin were applied had higher risk for certain potentially adverse birth outcomes. Our findings provide leads for confirmatory investigations on these compounds and/or compounds with similar modes of action.

Residential relocation is increasingly used as a natural experiment in epidemiological studies to assess the health impact of changes in environmental exposures. Since the likelihood of relocation can be influenced by individual characteristics that also influence health, studies may be biased if the predictors of relocation are not appropriately accounted for. Using data from Swedish and Dutch adults (SDPP, AMIGO), and birth cohorts (BAMSE, PIAMA), we investigated factors associated with relocation and changes in multiple environmental exposures across life stages.We used logistic regression to identify baseline predictors of moving, including sociodemographic and household characteristics, health behaviors and health. We identified exposure clusters reflecting three domains of the urban exposome (air pollution, grey surface, and socioeconomic deprivation) and conducted multinomial logistic regression to identify predictors of exposome trajectories among movers.On average, 7 % of the participants relocated each year. Before relocating, movers were consistently exposed to higher levels of air pollution than non-movers. Predictors of moving differed between the adult and birth cohorts, highlighting the importance of life stages. In the adult cohorts, moving was associated with younger age, smoking, and lower education and was independent of cardio-respiratory health indicators (hypertension, BMI, asthma, COPD). Contrary to adult cohorts, higher parental education and household socioeconomic position were associated with a higher probability of relocation in birth cohorts, alongside being the first child and living in a multi-unit dwelling. Among movers in all cohorts, those with a higher socioeconomic position at baseline were more likely to move towards healthier levels of the urban exposome.We provide new insights into predictors of relocation and subsequent changes in multiple aspects of the urban exposome in four cohorts covering different life stages in Sweden and the Netherlands. These results inform strategies to limit bias due to residential self-selection in epidemiological studies using relocation as a natural experiment.

Background: Type 2 diabetes (T2D) is thought to be influenced by environmental stressors such as air pollution and noise. Although environmental factors are interrelated, studies considering the exposome are lacking. We simultaneously assessed a variety of exposures in their association with prevalent T2D by applying penalised regression Least Absolute Shrinkage and Selection Operator (LASSO), Random Forest (RF), and Artificial Neural Networks (ANN) approaches. We contrasted the findings with single-exposure models including consistently associated risk factors reported by previous studies. Methods: Baseline data (n = 14,829) of the Occupational and Environmental Health Cohort study (AMIGO) were enriched with 85 exposome factors (air pollution, noise, built environment, neighbourhood socio-economic factors etc.) using the home addresses of participants. Questionnaires were used to identify participants with T2D (n = 676(4.6 %)). Models in all applied statistical approaches were adjusted for individual-level socio-demographic variables. Results: Lower average home values, higher share of non-Western immigrants and higher surface temperatures were related to higher risk of T2D in the multivariable models (LASSO, RF). Selected variables differed between the two multi-variable approaches, especially for weaker predictors. Some established risk factors (air pollutants) appeared in univariate analysis but were not among the most important factors in multivariable analysis. Other established factors (green space) did not appear in univariate, but appeared in multivariable analysis (RF). Average estimates of the prediction error (logLoss) from nested cross-validation showed that the LASSO outperformed both RF and ANN approaches. Conclusions: Neighbourhood socio-economic and socio-demographic characteristics and surface temperature were consistently associated with the risk of T2D. For other physical-chemical factors associations differed per analytical approach.

Background: The aim of the EU-funded HERA (health and environment research agenda) project is to set priorities for the future European research agenda in the environment, climate and health nexus. We report results from a European researcher’s perspective and identify research areas that have been inadequately investigated to date. Methods: An online survey was completed by European researchers to assess, evaluate and visualise research gaps. These research gaps were identified for 21 predefined areas within 3 main categories: i) classical environment and health paradigm; ii) problem or sector-based research areas and approaches and iii) holistic research areas and concepts. All research gaps were then evaluated by expert groups with the pre-defined criteria and systematically summarized. For areas identified within the survey as under-reported, additional input was sought from a range of key selected experts. The EU project database Cordis was utilized to verify that these areas were under-researched. Results: Between May and July 2019, 318 respondents from 38 countries reported 624 research gaps. The main areas for attention identified were: urban environments; chemicals; and climate change, (combined n = 313 gaps). Biodiversity loss and health; transport, mobility, sustainable solutions and health; energy transition and health; waste and the circular economy and health; ethics and philosophy and health were areas that were acknowledged as under-researched (combined n = 27 gaps). These under-researched areas were identified as having certain commonalities, they: i) mostly fell in the category “problem or sector based approaches“; ii) they are essential for developing and implementing solutions; and iii) require trans-disciplinary and cross-sectoral collaboration. Conclusions: Currently attention is given to topical and highly researched areas in environmental health. In contrast, this paper identifies key topics and approaches that are under-researched, yet, are critical for the implementation of the EU Green Deal, related strategies and action plans, and require further investigation and investment. The findings reveal the imperative to foster solutions-oriented, trans-disciplinary and participatory research and its implementation through changes in research funding and research structures.

Background: There is increasing evidence of associations between residential proximity to livestock farms and respiratory morbidity, but less is known about potential effects on respiratory mortality among residents. Objectives: We aimed to assess potential associations between respiratory mortality and residential proximity to (intensive) livestock farming. Methods: In DUELS, a national census-based cohort, we selected all inhabitants from rural and semi-urban areas of the Netherlands, aged ≥30 years and living at the same address for five years up to baseline (2004). We followed these ∼4 million individuals for respiratory mortality (respiratory system diseases, chronic lower respiratory diseases, pneumonia) from 2005 to 2012. We computed the average number of cattle, pigs, chicken, and mink present in 500 m, 1000 m, 1500 m and 2000 m of each individual’s residence in the period 1999–2003. Analyses were conducted using Cox proportional hazards regression, adjusting for potential confounders at individual and neighbourhood level. Results: We found evidence that living up to 2000 m of pig farms was associated with respiratory mortality, namely from chronic lower respiratory diseases, with Hazard Ratios ranging from 1.06 (1.02, 1.10) in people living close to low numbers (

Background: Poorly educated smallholder farmers in low-income countries are highly exposed to pesticides. This can result in adverse mental health issues, of which sleep problems might be an underlying indicator. We aim to examine the association between sleep problems and pesticide exposure among smallholder farmers in Uganda. Methods: A cross-sectional survey with 253 smallholder farmers was conducted between October and December 2019. Sleep problems were assessed during the week before the visit using the Medical Outcomes Study Sleep Scale (MOS-SS). Exposure to pesticides was assessed as application days of any pesticide and as use of 2,4-D, glyphosate, mancozeb, organophosphates & carbamates, pyrethroids and other pesticides during the week and year prior to the visit. Associations were assessed using adjusted multivariable logistic regression models. Results: Increased odds ratio (OR) for the sleep problem index 6-items (OR [95% Confidence Interval] 1.99 [1.04; 3.84] and 3.21 [1.33; 7.82]), sleep inadequacy (1.94 [1.04; 3.66] and 2.49 [1.05–6.22]) and snoring (3.17 [1.12; 9.41] and 4.07 [1.04; 15.14]) were observed for farmers who respectively applied pesticides up to two days and three or more days in the past week compared to farmers who did not apply during the past week. Gender-stratified analyses showed a higher OR for female applicators (4.27 [1.76–11.16]) than for male applicators (1.82 [0.91–3.79]) for the association between the sleep problem index 6-items and pesticide use in the week before the visit. Increased ORs were also observed for the association between the sleep problem index 6-item and mancozeb exposure during the past year 2.28 [1.12–4.71] and past week 2.51 [0.86–7.55] and glyphosate exposure during the past week 3.75 [1.24–11.8] compared to non-applicators. Discussion: Our findings suggest an increased risk of sleep problems among smallholder farmers in a pesticide-exposure-dependent way in a low-income context. Further gender-stratified, longitudinal investigations are warranted to confirm these findings.

The implementation of One Health/EcoHealth/Planetary Health approaches has been identified as key (i) to address the strong interconnections between risk for pandemics, climate change and biodiversity loss and (ii) to develop and implement solutions to these interlinked crises. As a response to the multiple calls from scientists on that subject, we have here proposed seven long-term research questions regarding COVID-19 and emerging infectious diseases (EIDs) that are based on effective integration of environmental, ecological, evolutionary, and social sciences to better anticipate and mitigate EIDs. Research needs cover the social ecology of infectious disease agents, their evolution, the determinants of susceptibility of humans and animals to infections, and the human and ecological factors accelerating infectious disease emergence. For comprehensive investigation, they include the development of nature-based solutions to interlinked global planetary crises, addressing ethical and philosophical questions regarding the relationship of humans to nature and regarding transformative changes to safeguard the environment and human health. In support of this research, we propose the implementation of innovative multidisciplinary facilities embedded in social ecosystems locally: ecological health observatories and living laboratories. This work was carried out in the frame of the European Community project HERA (www.HERAresearchEU.eu), which aims to set priorities for an environment, climate and health research agenda in the European Union by adopting a systemic approach in the face of global environmental change.

Background: Characteristics of the urban environment may contain upstream drivers of obesity. However, research is lacking that considers the combination of environmental factors simultaneously. Objectives: We aimed to explore what environmental factors of the urban exposome are related to body mass index (BMI), and evaluated the consistency of findings across multiple statistical approaches. Methods: A cross-sectional analysis was conducted using baseline data from 14,829 participants of the Occupational and Environmental Health Cohort study. BMI was obtained from self-reported height and weight. Geocoded exposures linked to individual home addresses (using 6-digit postcode) of 86 environmental factors were estimated, including air pollution, traffic noise, green-space, built environmental and neighborhood socio-demographic characteristics. Exposure-obesity associations were identified using the following approaches: sparse group Partial Least Squares, Bayesian Model Averaging, penalized regression using the Minimax Concave Penalty, Generalized Additive Model-based boosting Random Forest, Extreme Gradient Boosting, and Multiple Linear Regression, as the most conventional approach. The models were adjusted for individual socio-demographic variables. Environmental factors were ranked according to variable importance scores attributed by each approach and median ranks were calculated across these scores to identify the most consistent associations. Results: The most consistent environmental factors associated with BMI were the average neighborhood value of the homes, oxidative potential of particulate matter air pollution (OP), healthy food outlets in the neighborhood (5 km buffer), low-income neighborhoods, and one-person households in the neighborhood. Higher BMI levels were observed in low-income neighborhoods, with lower average house values, lower share of one-person households and smaller amount of healthy food retailers. Higher BMI levels were observed in low-income neighborhoods, with lower average house values, lower share of one-person households, smaller amounts of healthy food retailers and higher OP levels. Across the approaches, we observed consistent patterns of results based on model’s capacity to incorporate linear or nonlinear associations. Discussion: The pluralistic analysis on environmental obesogens strengthens the existing evidence on the role of neighborhood socioeconomic position, urbanicity and air pollution.

Background: Exposure to radiofrequency electromagnetic fields (RF-EMF) is often measured with personal exposimeters, but the accuracy of measurements can be hampered as carrying the devices on-body may result in body shielding. Further, the compact design may compromise the frequency selectivity of the sensor. The aim of this study was to compare measurements obtained using a multi-band body-worn distributed-exposimeter (BWDM) with two commercially available personal exposimeters (ExpoM-RF and EmeSpy 200) under real-life conditions. Methods: The BWDM measured power density in 10 frequency bands (800, 900, 1800, 2100, 2600 MHz, DECT 1900 MHz, WiFi 2.4 GHz; with separate uplink/downlink bands for 900, 1800 and 2100 MHz); using 20 separate antennas integrated in a vest and placed on diametrically opposite locations on the body, to minimize body-shielding. RF-EMF exposure data were collected from several microenvironments (e.g. shopping areas, train stations, outdoor rural/ urban residential environments, etc.) by walking around pre-defined areas/routes in Belgium, Spain, France, the Netherlands and Switzerland. Measurements were taken every 1–4 s with the BWDM in parallel with an ExpoM-RF and an EmeSpy 200 exposimeter. We calculated medians and interquartile ranges (IQRs) and compared difference, ratios and correlations of geometric mean RF-EMF exposure levels per microenvironment as measured with the exposimeters and the BWDM. Results: Across 267 microenvironments, medians and IQR of total BWDM measured RF-EMF exposure was 0.13 (0.05–0.33) mW/m2. Difference: IQR of exposimeters minus BWDM exposure levels was −0.011 (−0.049 to 0.0095) mW/m2 for the ExpoM-RF and −0.056 (−0.14 to −0.017) for the EmeSpy 200; ratios (exposimeter/BWDM) of total exposure had an IQR of 0.79 (0.55–1.1) for the ExpoM-RF and 0.29 (0.22–0.38) for the EmeSpy 200. Spearman correlations were 0.93 for the ExpoM-RF vs the BWDM and 0.96 for the EmeSpy 200 vs the BWDM. Discussion and conclusions: Results indicate that exposimeters worn on-body provide somewhat lower total RF-EMF exposure as compared to measurements conducted with the BWDM, in line with effects from body shielding. Ranking of exposure levels of microenvironments showed high correspondence between the different device types. Our results are informative for the interpretation of existing epidemiological research results.

Background: There is some evidence to suggest an association between ambient air pollution and development of Parkinson's disease (PD). However, the small number of studies published to date has reported inconsistent findings. Objectives: To assess the association between long-term exposure to ambient air pollution constituents and the development of PD. Methods: Air pollution exposures (particulate matter with aerodynamic diameter 30.4 μg/m3) was 0.87 (95% CI: 0.54, 1.41). For PM2.5 where the contrast in exposure was more limited, the OR associated with an increase from the first quartile PM2.5 (22.3 μg/m3) was 0.50 (95% CI: 0.24, 1.01). In a subset of the population with long-term residential stability (n = 632), an increased risk of PD was observed (e.g. OR for Q4 vs Q1 NO2:1.37, 95% CI: 0.71, 2.67). Conclusions: We found no clear association between 16 years of residential exposure to ambient air pollution and the development of PD in The Netherlands. Keywords: Air pollution, Parkinson's disease, Long-term exposure, Land-use regression

Background: It has been suggested that children who are exposed to agricultural pesticides have an increased risk of asthma, but evidence for associations betweeen residential pesticide exposure and childhood asthma is inconsistent. Objectives: To investigate the associations of residential pesticide exposure with the prevalence of asthma and related symptoms within a Dutch birth cohort study. Methods: In this cross-sectional analysis, we included participants of the PIAMA birth cohort study with data on residential pesticide exposure and asthma from parent-completed questionnaires at age 14, collected in 2012 (N = 1473). We used spatial data on the presence of individual crops (cereals, open field vegetables, commercial crops, open field floriculture/bulbs, corn and potatoes) and pesticide application on these crops to estimate residential exposure to pesticides with known irritant properties for the respiratory system within distances of 100, 500, and 1000 m of the participants' homes. Logistic regression was used to estimate associations between exposure and outcomes, adjusting for potential confounders. Results: No associations were found between living within 100, 500 and 1000 m of agricultural fields likely treated with pesticides and symptoms of asthma. For instance, for participants living within 100 m of fields with any crops likely treated with pesticides, the adjusted odds ratios (95% confidence interval) for the prevalence of asthma, shortness of breath and dry night cough at age 14 were 0.31 (0.07, 1.32), 0.61 (0.23, 1.57) and 1.26 (0.56, 2.80), respectively. No associations were found between estimated exposure to pesticides with known irritant properties for the respiratory system and asthma or related symptoms. Conclusions: There was no association between living near agricultural fields likely treated with pesticides and asthma and related respiratory symptoms, among our study participants. Keywords: Agricultural pesticides, Respiratory outcomes, Asthma, Birth cohort, Adolescents

Background: Exposure to radiofrequency electromagnetic fields (RF-EMF) from mobile communication technologies is changing rapidly. To characterize sources and associated variability, we studied the differences and correlations in exposure patterns between children aged 8 to 18 and their parents, over the course of the day, by age, by activity pattern, and for different metrics of exposure. Methods: Using portable RF-EMF measurement devices, we collected simultaneous real-time personal measurements of RF-EMF over 24 to 72 h in 294 parent-child pairs from Denmark, the Netherlands, Slovenia, Switzerland, and Spain. The devices measured the power flux density (mW/m2) in 16 different frequency bands every 4 s, and activity diary Apps kept by the participants were used to collect time-activity information in real-time. We analyzed their exposures by activity, for the different source constituents of exposure: downlink (radiation emitted from mobile phone base stations), uplink (transmission from phone to base station), broadcast, DECT (digital enhanced cordless telecommunications) and Wi-Fi. We looked at the correlations between parents and children overall, during day (06:00–22.00) and night (22:00–06:00) and while spending time at home. Results: The mean of time-weighted average personal exposures was 0.16 mW/m2 for children and 0.15 mW/m2 for parents, on average predominantly originating from downlink sources (47% for children and 45% for parents), followed by uplink (18% and 27% respectively) and broadcast (25% and 19%). On average, exposure for downlink and uplink were highest during the day, and for Wi-Fi and DECT during the evening. Exposure during activities where most of the time is spent (home, school and work) was relatively low whereas exposure during travel and outside activities was higher. Exposure to uplink increased with age among young people, while DECT decreased slightly. Exposure to downlink, broadcast, and Wi-Fi showed no obvious trend with age. We found that exposure to total RF-EMF is correlated among children and their parents (Rspearman = 0.45), especially while at home (0.62) and during the night (0.60). Correlations were higher for environmental sources such as downlink (0.57) and broadcast (0.62) than for usage-related exposures such as uplink (0.29). Conclusion: The generation gap between children and their parents is mostly evident in uplink exposure, due to more and longer uplink and cordless phone calls among parents, and their tendency to spend slightly more time in activities with higher environmental RF-EMF exposure, such as travel. Despite these differences in personal behavior, exposure to RF-EMF is moderately correlated between children and their parents, especially exposures resulting from environmental RF-EMF sources. Keywords: Personal exposure, Electromagnetic fields, Smart phones, Cell phones, Wireless technology

The outbreak of COVID-19 raised numerous questions on the interactions between the occurrence of new infections, the environment, climate and health. The European Union requested the H2020 HERA project which aims at setting priorities in research on environment, climate and health, to identify relevant research needs regarding Covid-19. The emergence and spread of SARS-CoV-2 appears to be related to urbanization, habitat destruction, live animal trade, intensive livestock farming and global travel. The contribution of climate and air pollution requires additional studies. Importantly, the severity of COVID-19 depends on the interactions between the viral infection, ageing and chronic diseases such as metabolic, respiratory and cardiovascular diseases and obesity which are themselves influenced by environmental stressors. The mechanisms of these interactions deserve additional scrutiny. Both the pandemic and the social response to the disease have elicited an array of behavioural and societal changes that may remain long after the pandemic and that may have long term health effects including on mental health. Recovery plans are currently being discussed or implemented and the environmental and health impacts of those plans are not clearly foreseen. Clearly, COVID-19 will have a long-lasting impact on the environmental health field and will open new research perspectives and policy needs.

Objective: To assess the association between estimated whole-brain and lobe-specific radiofrequency electromagnetic fields (RF-EMF) doses, using an improved integrated RF-EMF exposure model, and brain volumes in preadolescents at 9–12 years old. Methods: Cross-sectional analysis in preadolescents aged 9–12 years from the Generation R Study, a population-based birth cohort set up in Rotterdam, The Netherlands (n = 2592). An integrated exposure model was used to estimate whole-brain and lobe-specific RF-EMF doses (mJ/kg/day) from different RF-EMF sources including mobile and Digital Enhanced Cordless Telecommunications (DECT) phone calls, other mobile phone uses than calling, tablet use, laptop use, and far-field sources. Whole-brain and lobe-specific RF-EMF doses were estimated for all RF-EMF sources together (i.e. overall) and for three groups of RF-EMF sources that lead to a different pattern of RF-EMF exposure. Information on brain volumes was extracted from magnetic resonance imaging scans. Results: Estimated overall whole-brain RF-EMF dose was 84.3 mJ/kg/day. The highest overall lobe-specific dose was estimated in the temporal lobe (307.1 mJ/kg/day). Whole-brain and lobe-specific RF-EMF doses from all RF-EMF sources together, from mobile and DECT phone calls, and from far-field sources were not associated with global, cortical, or subcortical brain volumes. However, a higher whole-brain RF-EMF dose from mobile phone use for internet browsing, e-mailing, and text messaging, tablet use, and laptop use while wirelessly connected to the internet was associated with a smaller caudate volume. Conclusions: Our results suggest that estimated whole-brain and lobe-specific RF-EMF doses were not related to brain volumes in preadolescents at 9–12 years old. Screen activities with mobile communication devices while wirelessly connected to the internet lead to low RF-EMF dose to the brain and our observed association may thus rather reflect effects of social or individual factors related to these specific uses of mobile communication devices. However, we cannot discard residual confounding, chance finding, or reverse causality. Further studies on mobile communication devices and their potential negative associations with brain development are warranted, regardless whether associations are due to RF-EMF exposure or to other factors related to their use.