Carbon dioxide (CO2) is the foremost gas, emanated from human activities, and the best-known greenhouse gas, contributing to global warming, thus its negative effect on human health cannot be disregarded. The current paper investigates the relation between environmental quality variables, socio-economic factors, and human health from 1960 to 2014 in China, using Auto Regressive Distribution Lag (ARDL) Model. It selects three main environmental quality indicators (carbon emissions from coal, natural gas, and petrol) along with two representative socio-economic factors variables (per capita income and urban population) to explain the interaction mechanism. The results validate the long-term negative equilibrium impact of carbon emissions from the consumption of natural gas, coal, and petroleum on human health. The findings also reveal that migration from the countryside to cities and increase in per capita income improve quality of health. It is suggested that lowering emission of Carbon dioxide (CO2), which is the principal cause of greenhouse gas emissions, should be important in setting up the high quality of life for citizens.

A comparative study was conducted to (1) assess the potential of raw sewage used for urban agriculture to disseminate bacterial resistance in two cities of different size in Cameroon (Central Africa) and (2) compare the outcome with data obtained in Burkina Faso (West Africa). In each city, raw sewage samples were sampled from open-air canals in three neighbourhoods. After DNA extraction, the microbial population structure and function, presence of pathogens, antibiotic resistance genes and Enterobacteriaceae plasmids replicons were analysed using whole genome shotgun sequencing and bioinformatics. Forty-three pathogen-specific virulenc e factor genes were detected in the sewage. Eighteen different incompatibility groups of Enterobacteriaceae plasmid replicon types (ColE, A/C, B/O/K/Z, FIA, FIB, FIC, FII, H, I, N, P, Q, R, T, U, W, X, and Y) implicated in the spread of drug-resistance genes were present in the sewage samples. One hundred thirty-six antibiotic resistance genes commonly associated with MDR plasmid carriage were identified in both cities. Enterobacteriaceae plasmid replicons and ARGs found in Burkina Faso wastewaters were also present in Cameroon waters. The abundance of Enterobacteriaceae, plasmid replicons and antibiotic resistance genes was greater in Yaounde, the city with the greater population.In conclusion, the clinically relevant environmental resistome found in raw sewage used for urban agriculture is common in West and Central Africa. The size of the city impacts on the abundance of drug-resistant genes in the raw sewage while ESBL gene abundance is related to the prevalence of Enterobacteriaceae along with plasmid Enterobacteriaceae abundance associated to faecal pollution.

Data-driven machine learning methods present an opportunity to simultaneously assess the impact of multiple air pollutants on health outcomes. The goal of this study was to apply a two-stage, data-driven approach to identify associations between air pollutant exposure profiles and children's cognitive skills. Data from 6900 children enrolled in the Early Childhood Longitudinal Study, Birth Cohort, a national study of children born in 2001 and followed through kindergarten, were linked to estimated concentrations of 104 ambient air toxics in the 2002 National Air Toxics Assessment using ZIP code of residence at age 9 months. In the first-stage, 100 regression trees were learned to identify ambient air pollutant exposure profiles most closely associated with scores on a standardized mathematics test administered to children in kindergarten. In the second-stage, the exposure profiles frequently predicting lower math scores were included within linear regression models and adjusted for confounders in order to estimate the magnitude of their effect on math scores. This approach was applied to the full population, and then to the populations living in urban and highly-populated urban areas. Our first-stage results in the full population suggested children with low trichloroethylene exposure had significantly lower math scores. This association was not observed for children living in urban communities, suggesting that confounding related to urbanicity needs to be considered within the first-stage. When restricting our analysis to populations living in urban and highly-populated urban areas, high isophorone levels were found to predict lower math scores. Within adjusted regression models of children in highly-populated urban areas, the estimated effect of higher isophorone exposure on math scores was −1.19 points (95% CI −1.94, −0.44). Similar results were observed for the overall population of urban children. This data-driven, two-stage approach can be applied to other populations, exposures and outcomes to generate hypotheses within high-dimensional exposure data.

China's urbanization and the subsequent public vulnerability to degenerated environment is important to global public health. Among the environmental problems, fine particulate (PM2.5) pollution has become a serious hazard in rapidly urbanizing China. However, quantitative information remains inadequate. We thus collected PM2.5 concentrations and population census records, to illustrate the spatial patterns and changes in the PM2.5 hazard levels in China, and to quantify public vulnerability to the hazard during 2000–2010, following the air quality standards of World Health Organization. We found that 28% (2.72 million km2) of China's territory, including 78% of cities (154 cities) with a population of >1 million, was exposed to PM2.5 hazard in 2010; a 15% increase (1.47 million km2) from 2000 to 2010. The hazards potentially impacted the health of 72% of the total population (942 million) in 2010, including 70% of the young (206 million) and 76% of the old (71 million). This was a significant increase from the 42% of total the population (279 million) exposed in 2000. Of the total urban residents, 76% (501 million) were affected in 2010. Along with PM2.5 concentration increase, massive number of rural to urban migration also contributed greatly to China's urban public health vulnerability.

We utilize the distribution of PM2.5 concentration and population in large cities at the global scale to illustrate the relationship between urbanization and urban air quality. We found: 1) The relationship varies greatly among continents and countries. Large cities in North America, Europe, and Latin America have better air quality than those in other continents, while those in China and India have the worst air quality. 2) The relationships between urban population size and PM2.5 concentration in large cities of different continents or countries were different. PM2.5 concentration in large cities in North America, Europe, and Latin America showed little fluctuation or a small increasing trend, but those in Africa and India represent a “U” type relationship and in China represent an inverse “U” type relationship. 3) The potential contribution of population to PM2.5 concentration was higher in the large cities in China and India, but lower in other large cities.

Living in an urban environment is associated with an increased prevalence of specific mental health disorders, particularly schizophrenia. While many factors have been discussed as possible mediators of this association, most researchers favour the hypothesis that urban living stands as a proxy for an increased exposure to social stress. This factor has been recognized as one of the most powerful causes for the development of mental disorders, and appears to correlate with the markedly increased incidence of schizophrenia in urban minority groups. However, the hypothesis that the general urban population is exposed to increased levels of social stress has to be validated. Pursuing the goal of understanding how social stress acts as a risk factor for mental disorder in urban populations must include factors like social conditions, environmental pollutants, infrastructure and economic issues.

Biospectroscopy is an emerging inter-disciplinary field that exploits the application of sensor technologies [e.g., Fourier-transform infrared spectroscopy, Raman spectroscopy] to lend novel insights into biological questions. Methods involved are relatively non-destructive so samples can subsequently be analysed by more conventional approaches, facilitating deeper mechanistic insights. Fingerprint spectra are derived and these consist of wavenumber–absorbance intensities; within a typical biological experiment, a complex dataset is quickly generated. Biological samples range from biofluids to cytology to tissues derived from human or sentinel sources, and analyses can be carried out ex vivo or in situ in living tissue. A reference range of a designated normal state can be derived; anything outside this is potentially atypical and discriminating chemical entities identified. Computational approaches allow one to minimize within-category confounding factors. Because of ease of sample preparation, low-cost and high-throughput capability, biospectroscopy approaches herald a new greener means of environmental health monitoring in urban environments.

In large cities such as Berlin, human mortality rates increase during intense heat waves. Analysis of relevant data from north-eastern Germany revealed that, during the heat waves that occurred between 1990 and 2006, health risks were higher for older people in both rural and urban areas, but that, during the two main heat waves within that 17-year period of time, the highest mortality rates were from the city of Berlin, and in particular from its most densely built-up districts. Adaptation measures will need to be developed, particularly within urban areas, in order to cope with the expected future intensification of heat waves due to global climate change.

Natural environments have been linked to decreased risk of cardiovascular disease (CVD) and respiratory disease (RSD) mortality. However, few cohort studies have looked at associations of natural environments with CVD or RSD hospitalization. The aim of this study was to evaluate these associations in a cohort of U.S. Medicare beneficiaries (∼63 million individuals). Our open cohort included all fee-for-service Medicare beneficiaries (2000–2016), aged ≥65, living in the contiguous U.S. We assessed zip code-level park cover based on the United States Geological Survey Protected Areas Database, average greenness (Normalized Difference Vegetation Index, NDVI), and percent blue space cover based on Landsat satellite images. Cox-equivalent Poisson models were used to estimate associations of the exposures with first CVD and RSD hospitalization in the full cohort and among those living in urban zip codes (≥1000 persons/mile²). NDVI was weakly negatively correlated with percent park cover (Spearman ρ = −0.23) and not correlated with percent blue space (Spearman ρ = 0.00). After adjustment for potential confounders, percent park cover was not associated with CVD or RSD hospitalization in the full or urban population. An IQR (0.27) increase in NDVI was negatively associated with CVD (HR: 0.97, 95%CI: 0.96, 0.97), but not with RSD hospitalization (HR: 0.99, 95%CI: 0.98, 1.00). In urban zip codes, an IQR increase in NDVI was positively associated with RSD hospitalization (HR: 1.02, 95%CI: 1.00, 1.03). In stratified analyses, percent park cover was negatively associated with CVD and RSD hospitalization for Medicaid eligible individuals and individuals living in low socioeconomic status neighborhoods in the urban population. We observed no associations of percent blue space cover with CVD or RSD hospitalization. This study suggests that natural environments may benefit cardiorespiratory health; however, benefits may be limited to certain contexts and certain health outcomes.

Growing evidence suggests that anthropogenic noise has deleterious effects on the behavior and physiology of free-living animals. These effects may be particularly pronounced early in life, when developmental trajectories are sensitive to stressors, yet studies investigating developmental effects of noise exposure in free-living populations remain scarce. To elucidate the effects of noise exposure during development, we examined whether noise exposure is associated with shorter telomeres, duller carotenoid-based coloration and reduced body mass in nestlings of a common urban bird, the great tit (Parus major). We also assessed how the noise environment is related to reproductive success. We obtained long-term measurements of the noise environment, over a ∼24-h period, and characterized both the amplitude (measured by LAₑq, LA₉₀, LA₁₀, LAₘₐₓ) and variance in noise levels, since more stochastic, as well as louder, noise regimes might be more likely to induce stress. In our urban population, noise levels varied substantially, with louder, but less variable, noise characteristic of areas adjacent to a highway. Noise levels were also highly repeatable, suggesting that individuals experience consistent differences in noise exposure. The amplitude of noise near nest boxes was associated with shorter telomeres among smaller, but not larger, brood members. In addition, carotenoid chroma and hue were positively associated with variance in average and maximum noise levels, and average reflectance was negatively associated with variance in background noise. Independent of noise, hue was positively related to telomere length. Nestling mass and reproductive success were unaffected by noise exposure. Results indicate that multiple dimensions of the noise environment, or factors associated with the noise environment, could affect the phenotype of developing organisms, that noise exposure, or correlated variables, might have the strongest effects on sensitive groups of individuals, and that carotenoid hue could serve as a signal of early-life telomere length.