The impact of social capital, land use, air pollution and noise on individual morbidity in Dutch neighbourhoods

Evidence for Environmental Noise Effects on Health for the United Kingdom Policy Context: A Systematic Review of the Effects of Environmental Noise on Mental Health, Wellbeing, Quality of Life, Cancer, Dementia, Birth, Reproductive Outcomes, and Cognition

This systematic review commissioned by the UK Department for the Environment, Food and Rural Affairs (Defra), considers how the evidence base for noise effects on health has changed following the recent reviews undertaken for the WHO Environmental Noise Guidelines. This systematic review assesses the quality of the evidence for environmental noise effects on mental health, wellbeing, and quality of life; birth and reproductive outcomes; and cognition for papers published since the WHO reviews (mid-2015 to March 2019), as well as for cancer and dementia (January 2014 to March 2019). Using the GRADE methodology (Grading of Recommendations Assessment, Development and Evaluation) most evidence was rated as low quality as opposed to very low quality in the previous reviews. There is now low-quality evidence for a harmful effect of road traffic noise on medication use and interview measures of depression and anxiety and low quality evidence for a harmful effect of road traffic noise, aircraft noise, and railway noise on some cancer outcomes. Many other conclusions from the WHO evidence reviews remain unchanged. The conclusions remain limited by the low number of studies for many outcomes. The quantification of health effects for other noise sources including wind turbine, neighbour, industrial, and combined noise remains a research priority.

Putting Co-Exposures on Equal Footing: An Ecological Analysis of Same-Scale Measures of Air Pollution and Social Factors on Cardiovascular Disease in New York City

Epidemiologic evidence consistently links urban air pollution exposures to health, even after adjustment for potential spatial confounding by socioeconomic position (SEP), given concerns that air pollution sources may be clustered in and around lower-SEP communities. SEP, however, is often measured with less spatial and temporal resolution than are air pollution exposures (i.e., census-tract socio-demographics vs. fine-scale spatio-temporal air pollution models). Although many questions remain regarding the most appropriate, meaningful scales for the measurement and evaluation of each type of exposure, we aimed to compare associations for multiple air pollutants and social factors against cardiovascular disease (CVD) event rates, with each exposure measured at equal spatial and temporal resolution. We found that, in multivariable census-tract-level models including both types of exposures, most pollutant-CVD associations were non-significant, while most social factors retained significance. Similarly, the magnitude of association was higher for an IQR-range difference in the social factors than in pollutant concentrations. We found that when offered equal spatial and temporal resolution, CVD was more strongly associated with social factors than with air pollutant exposures in census-tract-level analyses in New York City.

Road Traffic Noise Exposure and Depression/Anxiety: An Updated Systematic Review and Meta-Analysis

Unlike other World Health Organization evidence reviews, the systematic review on mental disorders could not provide a quantitative estimate of the effect of environmental noise. With that in mind, we aimed to update it with additional studies published through to 18 August 2019 in order to allow for a formal meta-analysis of the association of residential road traffic noise with anxiety and depression. The quality effects and random effects estimators were used for meta-analysis and the robustness of findings was tested in several sensitivity analyses. Ten studies were included in the qualitative synthesis, from which we extracted 15 estimates for depression (n = 1,201,168) and five for anxiety (n = 372,079). Almost all studies were cross-sectional and the risk of bias in them was generally high. We found 4% (95% CI: -3%, 11%) higher odds of depression and 12% (95% CI: -4%, 30%) of anxiety associated with a 10 dB(A) increase in day-evening-night noise level (Lden). Both models suffered from moderate heterogeneity (55% and 54%), but there was evidence of publication bias only in the depression model. These findings were robust with no evidence of study-level moderators. A sensitivity analysis on an alternative set of categorically-reported estimates supported a linear relationship between Lden and depression. Taking into account an overall quality assessment for the included studies, we conclude that there is evidence of "very low" quality that increasing exposure to road traffic noise may be associated with depression and anxiety.

Home environment and health: Domestic risk factors for rhinitis, throat symptoms and non-respiratory symptoms among adults across China

Few studies exist from China on associations between home environment and adult health. We studied associations between home environment factors (other than dampness and mould) and rhinitis, throat and dermal symptoms and headache and fatigue among young parents in six cities across China (N = 36,541). They were recruited as parents from day care centers selected randomly and answered a questionnaire on medical symptoms and the home environment. Odds ratios (OR) were calculated by multilevel logistic regression adjusting for gender, atopy, smoking, home size and dampness/mould. Overall, 4.8% had skin symptoms 3.1% rhinitis, 2.8% eye, 4.1% throat symptoms, 3.0% headache and 13.9% had fatigue (all as weekly symptoms). Redecoration was associated with rhinitis, eye and skin symptoms, headache and fatigue. New furniture was associated with eye, throat and skin symptoms and fatigue. Gas cooking was associated with eye and throat symptoms, headache and fatigue. Biomass cooking was associated with eye and throat symptoms and headache. Burning incense was associated with eye, throat and skin symptoms, headache and fatigue. Presence of cockroaches and mosquitos or flies was associated with all six symptoms. Rats or mice were associated with eye and dermal symptoms. Cat keeping was associated with eye symptoms while dog keepers had less fatigue. Living near major roads was associated with rhinitis, eye, throat and skin symptoms and fatigue. Daily cleaning, a mechanical ventilation system in the kitchen or in the bathroom, living in older buildings and living in less urbanized areas were protective factors. In conclusion, urbanization, traffic exhaust, indoor emissions from redecoration and new furniture, gas cooking and air pollution from burning incense and biomass may cause dermal and mucosal symptoms, headache and fatigue among adults in China. Indoor animals (cats, mice/rats, cockroaches) were other risk factors. Daily cleaning, mechanical ventilation and living in older buildings can be protective.

Using deep learning to examine street view green and blue spaces and their associations with geriatric depression in Beijing, China

BACKGROUND

Residential green and blue spaces may be therapeutic for the mental health. However, solid evidence on the linkage between exposure to green and blue spaces and mental health among the elderly in non-Western countries is scarce and limited to exposure metrics based on remote sensing images (i.e., land cover and vegetation indices). Such overhead-view measures may fail to capture how people perceive the environment on the site.

OBJECTIVE

This study aimed to compare streetscape metrics derived from street view images with satellite-derived ones for the assessment of green and blue space; and to examine associations between exposure to green and blue spaces as well as geriatric depression in Beijing, China.

METHODS

Questionnaire data on 1190 participants aged 60 or above were analyzed cross-sectionally. Depressive symptoms were assessed through the shortened Geriatric Depression Scale (GDS-15). Streetscape green and blue spaces were extracted from Tencent Street View data by a fully convolutional neural network. Indicators derived from street view images were compared with a satellite-based normalized difference vegetation index (NDVI), a normalized difference water index (NDWI), and those derived from GlobeLand30 land cover data on a neighborhood level. Multilevel regressions with neighborhood-level random effects were fitted to assess correlations between GDS-15 scores and these green and blue spaces exposure metrics.

RESULTS

The average cumulative GDS-15 score was 3.4 (i.e., no depressive symptoms). Metrics of green and blue space derived from street view images were not correlated with satellite-based ones. While NDVI was highly correlated with GlobeLand30 green space, NDWI was moderately correlated with GlobeLand30 blue space. Multilevel regressions showed that both street view green and blue spaces were inversely associated with GDS-15 scores and achieved the highest model goodness-of-fit. No significant associations were found with NDVI, NDWI, and GlobeLand30 green and blue space. Our results passed robustness tests.

CONCLUSION

Our findings provide support that street view green and blue spaces are protective against depression for the elderly in China, yet longitudinal confirmation to infer causality is necessary. Street view and satellite-derived green and blue space measures represent different aspects of natural environments. Both street view data and deep learning are valuable tools for automated environmental exposure assessments for health-related studies.

The impact of urbanization on mood disorders: an update of recent evidence

PURPOSE OF REVIEW

Mood disorders are highly prevalent and represent a leading cause of global disability. Urbanization holds great public health implications spanning various environmental, lifestyle behavioural, economic and social domains. Underlying risk factors for mood disorders are heterogeneous but the psychiatric literature has extended beyond individual-level risk, to account for population-level environment and social-related precursors to mental ill health. This review summarizes recent studies published since 2017 examining the impact of urbanization and associated environmental, social and lifestyle risks for mood disorders, specifically depression.

RECENT FINDINGS

All identified studies examined depression or subclinical mood-related symptomatology. Recent evidence suggests individuals residing in urban areas experience increased risk of depression. Mechanistic pathways include increased exposure to noise, light and air pollution, poor quality housing, reduced diet quality, physical inactivity, economic strain and diminished social networks. The role of the gut microbiome in the development of mood disorders represents a novel research domain expected to hold potential for the psychiatric and environmental field. Further research is needed to extrapolate the relationship between increased sedentary lifestyles and technology use and depression in urban societies.

SUMMARY

Recent evidence highlights the complexity and reciprocity of underlying driving factors in the relationship between urbanization and mood disorders. Future epidemiological research should continue to untangle such complexity. There was a dearth of evidence relating to urbanization and mood disorders other than depression. Future research should identify the unique experiences of vulnerable subgroups who experience disproportionate increased risk of adverse health experiences associated with urbanization.

A Study of Noise Pollution Measurements and Possible Effects on Public Health in Ota Metropolis, Nigeria

BACKGROUND:

Noise pollution has become a major environmental problem leading to nuisances and health issues.

AIM:

This paper aims to study and analyse the noise pollution levels in major areas in Ota metropolis. A probability model which is capable of predicting the noise pollution level is also determined.

METHODS:

Datasets on the noise pollution level in 41 locations across Ota metropolis were used in this research. The datasets were collected thrice per day; morning, afternoon and evening. Descriptive statistics were performed, and analysis of variance was also conducted using Minitab version 17.0 software. Easy fit software was however used to select the appropriate probability model that would best describe the dataset.

RESULTS:

The noise levels are way far from the WHO recommendations. Also, there is no significant difference in the effects of the noise pollution level for all the times of the day considered. The log-logistic distribution provides the best fit to the dataset based on the Kolmogorov Smirnov goodness of fit test.

CONCLUSION:

The fitted probability model can help in the prediction of noise pollution and act as a yardstick in the reduction of noise pollution, thereby improving the public health of the populace.

Sustainability criteria for assessing nanotechnology applicability in industrial wastewater treatment: Current status and future outlook

Application of engineered nanomaterials for the treatment of industrial effluents and to deal with recalcitrant pollutants has been noticeably promoted in recent years. Laboratory, pilot and full-scale studies emphasize the potential of this technology to offer promising treatment options to meet the future needs for clean water resources and to comply with stringent environmental regulations. The technology is now in the stage of being transferred to the real applications. Therefore, the assessment of its performance according to sustainability criteria and their incorporation into the decision-making process is a key task to ensure that long term benefits are achieved from the nano-treatment technologies. In this study, the importance of sustainability criteria for the conventional and novel technologies for the treatment of industrial effluents was determined in a general approach assisted by a fuzzy-Delphi method. The criteria were categorized in technical, economic, environmental and social branches and the current situation of the nanotechnology regarding the criteria was critically discussed. The results indicate that the efficiency and safety are the most important parameters to make sustainable choices for the treatment of industrial effluents. Also, in addition to the need for scaling-up the nanotechnology in various stages, the study on their environmental footprint must continue in deeper scales under expected environmental conditions, in particular the synthesis of engineered nanomaterials and the development of reactors with the ability of recovery and reuse the nanomaterials. This paper will aid to select the most sustainable types of nanomaterials for the real applications and to guide the future studies in this field.