Search

BACKGROUND AND AIM: Air pollution exposure has been shown to damage the brain but its associations with the development of psychopathology are not fully characterized. This study tested whether ear...

Key Points Question Is exposure to outdoor air pollution in childhood and adolescence associated with the development of psychopathology at the transition to adulthood? Findings In this cohort study of 2039 UK-born children followed up for 2 decades, early-life exposure to nitrogen oxides was significantly associated with general psychopathology at 18 years of age, representing greater internalizing, externalizing, and thought disorder symptoms. The associations were not attributable to individual or family-level factors or to disadvantageous neighborhood characteristics. Meaning These findings suggest that exposure to nitrogen oxides in early life may be a nonspecific risk factor for the development of psychopathology as young people begin the transition to adulthood., Importance Air pollution exposure damages the brain, but its associations with the development of psychopathology are not fully characterized. Objective To assess whether air pollution exposure in childhood and adolescence is associated with greater psychopathology at 18 years of age. Design, Setting, and Participants The Environmental-Risk Longitudinal Twin Study is a population-based cohort study of 2232 children born from January 1, 1994, to December 4, 1995, across England and Wales and followed up to 18 years of age. Pollution data generation was completed on April 22, 2020; data were analyzed from April 27 to July 31, 2020. Exposures High-resolution annualized estimates of outdoor nitrogen oxides (NOx) and particulate matter with aerodynamic diameter less than 2.5 μm (PM2.5) linked to home addresses at the ages of 10 and 18 years and then averaged. Main Outcomes and Measures Mental health disorder symptoms assessed through structured interview at 18 years of age and transformed through confirmatory factor analysis into continuous measures of general psychopathology (primary outcome) and internalizing, externalizing, and thought disorder symptoms (secondary outcomes) standardized to a mean (SD) of 100 (15). Hypotheses were formulated after data collection, and analyses were preregistered. Results A total of 2039 participants (1070 [52.5%] female) had full data available. After adjustment for family and individual factors, each interquartile range increment increase in NOx exposure was associated with a 1.40-point increase (95% CI, 0.41-2.38; P = .005) in general psychopathology. There was no association between continuously measured PM2.5 and general psychopathology (b = 0.45; 95% CI, −0.26 to 1.11; P = .22); however, those in the highest quartile of PM2.5 exposure scored 2.04 points higher (95% CI, 0.36-3.72; P = .02) than those in the bottom 3 quartiles. Copollutant models, including both NOx and PM2.5, implicated NOx alone in these significant findings. NOx exposure was associated with all secondary outcomes, although associations were weakest for internalizing (adjusted b = 1.07; 95% CI, 0.10-2.04; P = .03), medium for externalizing (adjusted b = 1.42; 95% CI, 0.53-2.31; P = .002), and strongest for thought disorder symptoms (adjusted b = 1.54; 95% CI, 0.50-2.57; P = .004). Despite NOx concentrations being highest in neighborhoods with worse physical, social, and economic conditions, adjusting estimates for neighborhood characteristics did not change the results. Conclusions and Relevance Youths exposed to higher levels of outdoor NOx experienced greater psychopathology at the transition to adulthood. Air pollution may be a nonspecific risk factor for the development of psychopathology., This cohort study examines the association between exposure to outdoor air pollution in childhood and adolescence with the development of psychopathology at the transition to adulthood.

Knowledge about early risk factors for major depressive disorder (MDD) is critical to identify those who are at high risk. A multivariable model to predict adolescents' individual risk of future MDD has recently been developed however its performance in a UK sample was far from perfect. Given the potential role of air pollution in the aetiology of depression, we investigate whether including childhood exposure to air pollution as an additional predictor in the risk prediction model improves the identification of UK adolescents who are at greatest risk for developing MDD. We used data from the Environmental Risk (E-Risk) Longitudinal Twin Study, a nationally representative UK birth cohort of 2232 children followed to age 18 with 93% retention. Annual exposure to four pollutants - nitrogen dioxide (NO2), nitrogen oxides (NOX), particulate matter

The adverse health effects of air pollutants, especially those of PM 2.5, are well documented. However, a lack of adequate monitoring and weaknesses in modelling approaches do not allow a good assessment of health effects in many areas of the World. Advances in computational methods and the availability of new data sets, e.g. satellite remote observations, have enlarged the possibilities of modelling for application in large scale health effects studies. However, PM 2.5 monitoring is very recent in most of the World and more limited compared to other pollutants, and understanding how to use PM 10 monitors to estimate PM 2.5 exposure is therefore important. Since interest in these methods is relatively recent, there is a need for testing their performance against ambient measurements, but long term PM 2.5 datasets are less readily available than PM 10 in many regions. In the present study we report the methodology and results of using regression modelling and a machine learning method (Random Forest-RF), as well as a combination of the two, to enhance a PM 2.5 measurement data base in London using PM 10 and NO x measurements as well as other predictors and compare the relative performance of each method. We found that the combination of predictions by the regression model and the RF performs best and we obtain a cross-validation R 2 of 99.29% and 98.22% for the 5-year periods 2004–2008 and 2009–2013, respectively, and a Mean Square Error near 1. Our enhanced data base for PM 2.5 is available for use by other researchers.

IntroductionAir and noise pollution are emerging environmental health hazards in African cities, with potentially complex spatial and temporal patterns. Limited local data are a barrier to the formulation and evaluation of policies to reduce air and noise pollution.Methods and analysisWe designed a year-long measurement campaign to characterise air and noise pollution and their sources at high-resolution within the Greater Accra Metropolitan Area (GAMA), Ghana. Our design uses a combination of fixed (year-long, n=10) and rotating (week-long, n =~130) sites, selected to represent a range of land uses and source influences (eg, background, road traffic, commercial, industrial and residential areas, and various neighbourhood socioeconomic classes). We will collect data on fine particulate matter (PM2.5), nitrogen oxides (NOx), weather variables, sound (noise level and audio) along with street-level time-lapse images. We deploy low-cost, low-power, lightweight monitoring devices that are robust, socially unobtrusive, and able to function in Sub-Saharan African (SSA) climate. We will use state-of-the-art methods, including spatial statistics, deep/machine learning, and processed-based emissions modelling, to capture highly resolved temporal and spatial variations in pollution levels across the GAMA and to identify their potential sources. This protocol can serve as a prototype for other SSA cities.Ethics and disseminationThis environmental study was deemed exempt from full ethics review at Imperial College London and the University of Massachusetts Amherst; it was approved by the University of Ghana Ethics Committee (ECH 149/18-19). This protocol is designed to be implementable in SSA cities to map environmental pollution to inform urban planning decisions to reduce health harming exposures to air and noise pollution. It will be disseminated through local stakeholder engagement (public and private sectors), peer-reviewed publications, contribution to policy documents, media, and conference presentations.