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.

Lead (Pb) exposure is known to affect the health of children while soil Pb is an important contributor to human Pb exposure. To analyze the effects of both environmental and other factors, especially total and bioaccessible Pb in neighborhood soil, on school-aged urban children’s blood lead level (BLL), 75 children (6–11 years old) were recruited from an industry city in eastern China for BLL measurement and questionnaire survey. Soil samples were collected from their living neighborhoods and measured for total and bioaccessible Pb. The mean BLL was 4.82 μg dL⁻¹, with 42 out of 75 children having BLL exceeding the international guideline of 5 μg dL⁻¹. Low Pb contamination was observed in soil with total Pb ranging from 12.5 to 271 mg kg⁻¹ (mean 34.3 mg kg⁻¹). Based on the in vitro Solubility Bioaccessibility Research Consortium (SBRC) gastric fluid extraction, bioaccessible Pb in soil ranged from 0.40 to 79.1 mg kg⁻¹ (mean 7.58 mg kg⁻¹) with Pb bioaccessibility ranging from 1.74 to 68.1 (mean 19.9%). When BLL was correlated with total Pb in soil, insignificant linear relationship was observed (P > 0.05, correlation coefficient 95%CI = −0.047–0.40, R² = 0.07). However, when BLL was correlated with soil bioaccessible Pb or Pb bioaccessibility, much stronger linear relationships were observed (P < 0.01, correlation coefficient 95%CI = 0.28–0.64, R² = 0.16–0.20), suggesting that bioaccessible Pb was a much stronger predictor of BLL. In addition, strong associations were also observed between BLL and social factors such as house decoration, residence time, and personal habits, suggesting that both soil Pb contamination and social factors play important roles in elevating BLL for city children.

Rice intake is a major route of oral exposure to inorganic arsenic (iAs), a known human carcinogen. The recent risk assessment on iAs in rice conducted by the US Food & Drug Administration (FDA) didn’t propose an action level for iAs in rice mainly because of the relatively low consumption rate in the general US population. However, this decision may not be applicable to high rice-intake populations, such as the Chinese population.The objective of this study was to probabilistically characterize the exposure of iAs via rice intake in Chinese population with a focus on the urban population.With the consideration of bioaccessibility of iAs in rice, iAs exposure is mainly determined by rice intake rate and iAs concentration in rice. To estimate the daily rice consumption, a dietary survey consisting of 1873 subjects was conducted in three major Chinese cities (Beijing, Guangzhou, and Hangzhou). Speciated As concentration in rice was measured using 480 rice samples collected from markets in these three cities. Monte Carlo simulation approach was applied to distributionally estimate the average daily dose (ADD) of iAs through rice intake.The estimated distribution of daily iAs exposure of the study population has a mean of 10.5 μg/day with a 95th percentile interval from 0.1 to 75.3 μg/day. The estimated distribution of ADD has a mean of 0.179 μg/kg/day with a 95th percentile interval from 0.001 to 1.224 μg/kg/day. Greater level of iAs exposure (due to higher rice intake) was observed in males than females, and in summer than other seasons with lower temperature. Geographically, the ADD of iAs exposure from rice intake reduces from south to north.About 13% of the Chinese urban population are exposed to iAs via rice intake higher than the Reference Dose (RfD) level.

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.

An indirect estimation method was followed to derive exposure levels of fifteen phthalate congeners in urban population of Delhi, India. The exposure media samples were collected from Jawaharlal Nehru University (JNU) campus and Okhla industrial area. GC–MS analysis of the samples indicated di(2-ethylhexyl) phthalate (DEHP) to be the most abundant congener and its estimated total daily intake level reached upto 70 μg kg−1 d−1. Out of the studied congeners, intake doses for di-n-butyl phthalate (DnBP) and DEHP, reached levels near or above the established exposure limit. In JNU, DEHP, dimethyl phthalate (DMP) and butyl benzyl phthalate (BBP) had 69% share in combined daily intake of Σ15 phthalates (CDI15); whereas, in Okhla, DEHP, diethyl phthalate (DEP), diisobutyl phthalate (DIBP), DnBP and DMP shared 64% of the CDI15. Food was found to be the major source of exposure contributing 67% and 74% of the estimated CDI15 at JNU and Okhla respectively.

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.

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.

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.

The ground-level ozone (O₃) concentration in the urban regions of China has become an increasingly noticeable environmental problem in recent years. Many epidemiological studies have reported the association between O₃ pollution and mortality, only a few studies have focused on the O₃-related mortality and corresponding economic effects at the Chinese city and province level. This study reports the seasonal variation of ground-level O₃ in 338 cities of China during the year 2016 and evaluates its effect on premature mortality and economic loss. It further illustrates the differences in cause-specific mortality outcomes of the log-linear and linear model, two of the prominently used methods for estimating health effects. In 2016, the annual average daily maximum 8-h O₃ concentration in China ranged between 74 and 201 μg/m³ (138 ± 24.7 μg/m³). 30% of the total population was exposed to >160 μg/m³ O₃ concentration (Chinese national ambient air quality standard) and about 67.2% urban population lived in exposure above the WHO recommended O₃ concentrations (100 μg/m³). The estimated national O₃-attributable mortality was 74.2 × 10³ (95% CI: 16.7×10³–127×10³) in the log-linear model, whereas, the total O₃-related mortality using the linear model was 69.6 × 10³ (95% CI: 16.2 × 10³–115 × 10³). The exposure to O₃ caused a nationwide economic loss of about 7.6 billion US$ (range: 1.7–12.9) in 2016. This study uniquely provides most comprehensive coverage of the Chinese cities for O₃ associated mortality utilizing ground level measurement data for 2016 and presents a measurable assessment to the policymakers of China for streamlining their efforts on air quality improvement and O₃ containment.

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.