Search

Good quality and completeness of ambient air quality monitoring data is central in supporting actions towards mitigating the impact of ambient air pollution. In South Africa, however, availability of continuous ground-level air pollution monitoring data is scarce and incomplete. To address this issue, we developed and compared different modeling approaches to impute missing daily average particulate matter (PM10) data between 2010 and 2017 using spatiotemporal predictor variables. The random forest (RF) machine learning method was used to explore the relationship between average daily PM10 concentrations and spatiotemporal predictors like meteorological, land use and source-related variables. National (8 models), provincial (32) and site-specific (44) RF models were developed to impute missing daily PM10 data. The annual national, provincial and site-specific RF cross-validation (CV) models explained on average 78%, 70% and 55% of ground-level PM10 concentrations, respectively. The spatial components of the national and provincial CV RF models explained on average 22% and 48%, while the temporal components of the national, provincial and site-specific CV RF models explained on average 78%, 68% and 57% of ground-level PM10 concentrations, respectively. This study demonstrates a feasible approach based on RF to impute missing measurement data in areas where data collection is sparse and incomplete.

BACKGROUND: Little is known about radiofrequency electromagnetic fields (RF) from mobile technology and resulting dose in young people. We describe modeled integrated RF dose in European children and adolescents combining own mobile device use and surrounding sources. METHODS: Using an integrated RF model, we estimated the daily RF dose in the brain (whole-brain, cerebellum, frontal lobe, midbrain, occipital lobe, parietal lobe, temporal lobes) and the whole-body in 8358 children (ages 8-12) and adolescents (ages 14-18) from the Netherlands, Spain, and Switzerland during 2012-2016. The integrated model estimated RF dose from near-field sources (digital enhanced communication technology (DECT) phone, mobile phone, tablet, and laptop) and far-field sources (mobile phone base stations via 3D-radiowave modeling or RF measurements). RESULTS: Adolescents were more frequent mobile phone users and experienced higher modeled RF doses in the whole-brain (median 330.4 mJ/kg/day) compared to children (median 81.8 mJ/kg/day). Children spent more time using tablets or laptops compared to adolescents, resulting in higher RF doses in the whole-body (median whole-body dose of 81.8 mJ/kg/day) compared to adolescents (41.9 mJ/kg/day). Among brain regions, temporal lobes received the highest RF dose (medians of 274.9 and 1786.5 mJ/kg/day in children and adolescents, respectively) followed by the frontal lobe. In most children and adolescents, calling on 2G networks was the main contributor to RF dose in the whole-brain (medians of 31.1 and 273.7 mJ/kg/day, respectively). CONCLUSION: This first large study of RF dose to the brain and body of children and adolescents shows that mobile phone calls on 2G networks are the main determinants of brain dose, especially in temporal and frontal lobes, whereas whole-body doses were mostly determined by tablet and laptop use. The modeling of RF doses provides valuable input to epidemiological research and to potential risk management regarding RF exposure in young people.

Objective: To investigate the association between estimated whole-brain radiofrequency electromagnetic fields (RF-EMF) dose, using an improved integrated RF-EMF exposure model, and cognitive function in preadolescents and adolescents. Methods: Cross-sectional analysis in preadolescents aged 9-11 years and adolescents aged 17-18 years from the Dutch Amsterdam Born Children and their Development Study (n = 1664 preadolescents) and the Spanish INfancia y Medio Ambiente Project (n = 1288 preadolescents and n = 261 adolescents), two population-based birth cohort studies. Overall whole-brain RF-EMF doses (mJ/kg/day) were estimated for several RF-EMF sources together including mobile and Digital Enhanced Cordless Telecommunications phone calls (named phone calls), other mobile phone uses than calling, tablet use, laptop use (named screen activities), and far-field sources. We also estimated whole-brain RF-EMF doses in these three groups separately (i.e. phone calls, screen activities, and far-field) that lead to different patterns of RF-EMF exposure. We assessed non-verbal intelligence in the Dutch and Spanish preadolescents, information processing speed, attentional function, and cognitive flexibility in the Spanish preadolescents, and working memory and semantic fluency in the Spanish preadolescents and adolescents using validated neurocognitive tests. Results: Estimated overall whole-brain RF-EMF dose was 90.1 mJ/kg/day (interquartile range (IQR) 42.7; 164.0) in the Dutch and Spanish preadolescents and 105.1 mJ/kg/day (IQR 51.0; 295.7) in the Spanish adolescents. Higher overall estimated whole-brain RF-EMF doses from all RF-EMF sources together and from phone calls were associated with lower non-verbal intelligence score in the Dutch and Spanish preadolescents (-0.10 points, 95% CI-0.19; -0.02 per 100 mJ/kg/day increase in each exposure). However, none of the whole-brain RF-EMF doses was related to any other cognitive function outcome in the Spanish preadolescents or adolescents. Conclusions: Our results suggest that higher brain exposure to RF-EMF is related to lower non-verbal intelligence but not to other cognitive function outcomes. Given the cross-sectional nature of the study, the small effect sizes, and the unknown biological mechanisms, we cannot discard that our resultsare due to chance finding or reverse causality. Longitudinal studies on RF-EMF brain exposure and cognitive function are needed.

Little is known about personal and time-integrated exposure to past and current used pesticides in agricultural areas and within-family exposure similarities. We aimed to assess exposure to pesticides using silicone wristbands in child/guardian pairs living on farms and in villages within two agricultural areas in South Africa. Using silicone wristbands, we quantified 21 pesticides in child/guardian pairs in 38 households over six days in 2018. Levels (in ng/g wristband) of pesticides and their transformation products (12 current-use pesticides and nine organochlorine pesticides) were measured using GC-MS/MS. We assessed the correlation between pesticide levels and between household members using Spearman correlation coefficients (r(s)). Multivariable generalized least squares (GLS) models, using household id as intercept, were used to determine level of agreement between household members, exposure differences between children and guardians and exposure predictors (study area, household location [farm vs. village] and household pesticide use). We detected 16 pesticides with highest detection frequencies for deltamethrin (89%), chlorpyrifos (78%), boscalid (56%), cypermethrin (55%), and p,p'-DDT (48%). Most wristbands (92%) contained two or more pesticides (median seven (range one to 12)). Children had higher concentrations than guardians for four pesticides. Correlation between the pesticide levels were in most cases moderate (rs 0.30-0.68) and stronger in children than in guardians. Five pesticides showed moderate to strong correlation between household members, with the strongest correlation for boscalid (r(s) 0.84). Exposure differences between the two agricultural areas were observed for chlorpyri fas, diazinon, pmthiofos, cypermethrin, boscalid, p,p'-DDT and p,p'-DDE and within areas for cypermethrin. We showed that for several pesticides children had higher exposure levels than guardians. The positive correlations observed for child/guardian pairs living in the same household suggest non-occupational shared exposure pathways in these communities.

Background: The use of digital technology such as mobile phones is ubiquitous in adolescents. However, excessive use may have adverse health effects, possibly partially mediated by disruptions to sleep. Objective: This study aims to assess the social predictors of digital technology use and their cross-sectional association with BMI z scores and being overweight in a large sample of adolescents. Methods: We used baseline data from a subset of a large adolescent cohort from 39 schools across Greater London who participated in the Study of Cognition, Adolescents and Mobile Phones (n=1473). Digital technology use included phone calls, internet use on mobile phones, and video gaming on any device. Multilevel regression was used to assess the associations between digital technology use and age-specific and sex-specific BMI z scores and being overweight (including obesity). Measurements were derived from height and weight, obtained by the Tanita BC-418 Body Composition Analyzer. We examined whether these associations were mediated by insufficient sleep. Results: Generally, participants with lower socioeconomic status reported more use of digital technology. Controlling for socioeconomic status, internet use on mobile phones for more than 3 hours per day was associated with higher BMI z scores (adjusted β=.30, 95% CI 0.11-0.48) and greater odds of being overweight (adjusted odds ratio 1.60, 95% CI 1.09-2.34), compared with low use (≤30 minutes). Similar associations were found between video gaming and BMI z scores and being overweight. The BMI z score was more strongly related to weekday digital technology use (internet use on mobile phones and video gaming) than weekend use. Insufficient sleep partly mediated the associations between digital technology use and BMI z scores (proportion of mediation from 8.6% to 17.8%) by an indirect effect. Conclusions: We found an association between digital technology use and BMI in adolescents, partly mediated by insufficient sleep, suggesting that the underlying mechanisms may be multifactorial. Further research with longitudinal data is essential to explore the direction of the relationships.

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. Grant Sponsors: This research was funded by the National Research Program of the French Agency for Food, Environmental and Occupational Health and Safety (ANSES), grant No 2015-2-RF-07, and the equipment was supported by funding from the European Commission Seventh Framework Programme under grant agreement number FP7 603794 (GERoNiMO). Our funders had no involvement in the study design; data collection, analysis and interpretation of data; in the writing of the report; and in the decision to submit the article for publication.

International audience; The present framework for noise abatement in Switzerland was introduced in the 1980s in the Environmental Protection Act. It was subsequently substantiated with noise exposure limits for a range of noise sources. Due to several recent developments, the empirical foundations of the noise limits were increasingly questioned, especially regarding transportation noise. A working group of the Federal Noise Abatement Commission was appointed and commissioned with the task of updating the pertinent scientific foundations and to suggest revised exposure limits when deemed necessary. One result of these efforts was the launch of the interdisciplinary SiRENE study in 2014 with the aim of deriving up to date exposure-response relationships of health effects representative for the Swiss population. At the same time Switzerland has supported WHO's work on new environmental noise guidelines. In its recommendations regarding new noise exposure limits, the working group took into account exposure-response relationships from the (Switzerland-specific) SiRENE study, from the three WHO evidence reviews for annoyance, for sleep disturbance, and for cardiovascular and metabolic outcomes as well as from a recent update of the latter. In deriving limit values, the group followed a similar heuristic as did the WHO for informing their recommendations, but explicitly considered two classes of effects that were both given the same weight for deriving the limit values: Self-reported ("subjective") effects like annoyance and self-reported sleep disturbances on one hand, and on the other hand ("objective") cardiometabolic effects for which the evidence was considered scientifically sound enough, namely ischaemic heart disease (IHD), diabetes, and myocardial infarction mortality. In order to define acceptable risks for these outcomes, they were weighted with common Disability Weight (DW) values. Further empirical data from the SiRENE study were used to define duration and position of day and night rating periods and time-dependent penalties.

BACKGROUND A positive effect of breastfeeding on lung function has been demonstrated in cohorts of children with asthma or risk for asthma. We assessed the impact of breastfeeding on lung function and symptoms at the age of 6 years in an unselected, healthy birth cohort. METHODS We prospectively studied healthy term infants from the Bern-Basel Infant Lung Development (BILD) cohort from birth up to 6 years. Any breastfeeding was assessed by weekly phone calls during the first year of life. Risk factors (eg, smoking exposure, parental history of allergic conditions, and education) were obtained using standardized questionnaires. The primary outcomes were lung function parameters measured at 6 years of age by spirometry forced expiratory volume in 1 second, body plethysmography (functional residual capacity [FRCpleth ], the total lung capacity [TLCpleth ], and the effective respiratory airway resistance [Reff ]) and fractional exhaled nitric oxide (FeNO). Secondary outcomes included ever wheeze (between birth and 6 years), wheeze in the past 12 months, asthma, presence of allergic conditions, atopic dermatitis, rhinitis, and positive skin prick test at the age of 6 years. RESULTS In 377 children the mean breastfeeding duration was 36 weeks (SD 14.4). We found no association of breastfeeding duration with obstructive or restrictive lung function and FeNO. After adjustment for confounders, we found no associations of breastfeeding duration with respiratory symptoms or the presence of allergic conditions. CONCLUSION This study found no evidence of an association between breastfeeding and comprehensive lung function in unselected healthy children with long-term breastfeeding. Our findings do not support the hypothesis that the duration of breastfeeding has a direct impact on lung function in a healthy population with low asthmatic risk.

Radiofrequency electromagnetic fields (EMFs) are used to enable a number of modern devices, including mobile telecommunications infrastructure and phones, Wi-Fi, and Bluetooth. As radiofrequency EMFs at sufficiently high power levels can adversely affect health, ICNIRP published Guidelines in 1998 for human exposure to time-varying EMFs up to 300 GHz, which included the radiofrequency EMF spectrum. Since that time, there has been a considerable body of science further addressing the relation between radiofrequency EMFs and adverse health outcomes, as well as significant developments in the technologies that use radiofrequency EMFs. Accordingly, ICNIRP has updated the radiofrequency EMF part of the 1998 Guidelines. This document presents these revised Guidelines, which provide protection for humans from exposure to EMFs from 100 kHz to 300 GHz. Health Phys. 118(5):483–524; 2020. Copyright © 2020 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the Health Physics Society. This is an open-access article distributed under the terms of the Creative Commons AttributionNon Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal.

South Africa is the leading pesticide user in Sub-Saharan Africa. However, little is known about the occurrence of pesticide mixtures in surface water and potential environmental risks in Africa. This study investigated the occurrence of pesticides mixtures in three watersheds during a drought year in South Africa. The study was conducted in the Krom River, Berg River and Hex River watersheds within larger agriculture systems in the Western Cape. Pesticide spray records were collected from 38 farms. A total of 21 passive water samplers (styrenedivinylbenzene disks (SDB)) were deployed, each for two weeks per month, over seven sampling rounds during the main pesticide application period between July 2017 and January 2018. Samples were analyzed for 248 pesticide compounds using LC-HR-MS/MS. Pesticide occurrence was analyzed for temporal agreement with pesticide spraying events (Cohen's κ) and correlation with rainfall patterns and river discharge (Pearson correlation (r p )). Pesticide time-weighted average concentrations were estimated and compared to environmental quality standards (EQS). According to the farm spray records, 96 different pesticides were sprayed during the sampling period and differed considerably between the three study areas, seasons and crops grown. In total, 53 compounds were detected in river water. We detected 39% of compounds from the spraying records and demonstrated close temporal correlations of seasonal patterns for 11 pesticide compounds between reported on spraying records and observations in the streams (κ = 0.90). However, 23 detected pesticides were not found on spray records, many of them being herbicides. Most of the estimated two-week average pesticide concentrations were below 40 ng/L. The insecticides imidacloprid, thiacloprid, chlorpyrifos and acetamiprid and the herbicide terbuthylazine exceeded at least once their EQS 58-fold (EQS 13 ng/L), 12-fold (EQS 10 ng/L), 9-fold (EQS 0.46 ng/L), 5-fold (EQS 24 ng/L) and 3-fold (EQS 220 ng/L), respectively. Our study substantially widens the view on pesticide pollution in surface water compared to previous studies in Sub-Saharan Africa by targeting more than 200 pesticides using passive sampling systems. This broad assessment revealed the presence of 53 compounds, some of them in high concentrations, indicating possible adverse effects on biota and the quality of the ecosystem. Whether the observed concentration levels in the year 2017 were exceptional due to the lowest ever recorded rainfall and river discharge needs to be tested with additional data to better understand how pesticide pollution levels manifest under average rainfall and river discharge conditions.

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.

Introduction Medulloblastoma is a malignant embryonal tumor of the cerebellum that occurs predominantly in children. To find germline genetic variants associated with medulloblastoma risk, we conducted a genome-wide association study (GWAS) including 244 medulloblastoma cases and 247 control subjects from Sweden and Denmark. Methods Genotyping was performed using Illumina BeadChips, and untyped variants were imputed using IMPUTE2. Results Fifty-nine variants in 11 loci were associated with increased medulloblastoma risk (p –5), but none were statistically significant after adjusting for multiple testing (p –8). Thirteen of these variants were genotyped, whereas 46 were imputed. Genotyped variants were further investigated in a validation study comprising 249 medulloblastoma cases and 629 control subjects. In the validation study, rs78021424 (18p11.23, PTPRM) was associated with medulloblastoma risk with OR in the same direction as in the discovery cohort (ORT = 1.59, pvalidation = 0.02). We also selected seven medulloblastoma predisposition genes for investigation using a candidate gene approach: APC, BRCA2, PALB2, PTCH1, SUFU, TP53, and GPR161. The strongest evidence for association was found for rs201458864 (PALB2, ORT = 3.76, p = 3.2 × 10–4) and rs79036813 (PTCH1, ORA = 0.42, p = 2.6 × 10–3). Conclusion The results of this study, including a novel potential medulloblastoma risk loci at 18p11.23, are suggestive but need further validation in independent cohorts.