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.

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.

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.

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.

Increased levels of dialkylphosphates (DAP) in maternal urine are associated with a variety of adverse developmental outcomes in children. Although urinary DAP levels are usually considered to be a marker of exposure to organophosphate (OP) pesticides, excretion of DAP may also increase by ingesting preformed DAP. To date, no study has quantitatively assessed the possible contribution of the dietary intake of preformed DAP and OP pesticides to urinary levels of DAP. Therefore, we aimed to estimate the levels of 6 DAPs and 84 OP pesticides in duplicate diet samples and urine samples collected from 73 women living in urban areas of Japan in 2018. DAP and OP pesticides were detected in 94% and 45% of diet samples, while DAP was detected in 100% of urinary samples, respectively. The average daily intake of preformed DAP was significantly higher than that of parent OP pesticides in our participants. Dimethylphosphate and diethylphosphate were predominant in the preformed DAP, and the estimated average daily intake of total amount of DAP was 78.3 nmol. Fruits and vegetables were the major dietary sources of DAP. Dietary intake of DAP was positively associated with urinary DAP levels, suggesting that a considerable amount of urinary DAP was derived from ingesting preformed DAP. Our results show that attributing urinary DAP levels exclusively to OP pesticide exposure would result in a substantial overestimation of the exposure level. Therefore, the urinary levels of DAP may not be suitable for evaluating OP pesticide exposure in the general urban population.

In this study, the positive matrix factorization (PMF) source apportionment model was employed to quantify the contributions of airport activities to particle number concentrations (PNCs) at Amsterdam Schiphol. Time-resolved particle number size distributions in parallel with the concentrations of auxiliary variables, including gaseous pollutants (NOₓ and CO), black carbon, PM₂.₅ mass, and number of arrivals/departures were measured for 32 sampling days over a 6-month period near Schiphol airport to be used in the model. PMF results revealed that airport activities, cumulatively, accounted for around 79.3% of PNCs and our model segregated them into three major groups: (i) aircraft departures, (ii) aircraft arrivals, and (iii) ground service equipment (GSE) (with some contributions of local road traffic, mostly from airport parking lots). Aircraft departures and aircraft arrivals showed mode diameters <20 nm and contributed, respectively, to 46.1% and 26.7% of PNCs. The factor GSE/local road traffic, with a mode diameter of around 60–80 nm, accounted for 6.5% of the PNCs. Road traffic related mainly to the surrounding freeways was characterized with a mode diameter of 30–40 nm; this factor contributed to 18.0% of PNCs although its absolute PNCs was comparable with that of areas heavily impacted by traffic emissions. Lastly, urban background with a mode diameter at 150–225 nm, had a minimal contribution (2.7%) to PNCs while dominating the particle volume/mass concentrations with a contribution of 58.2%. These findings illustrate the dominant role of the airport activities in ambient PNCs in the surrounding areas. More importantly, the quantification of the contributions of different airport activities to PNCs is a useful tool to better control and limit the increased PNCs near the airports that could adversely impact the health of the adjacent urban communities.

Urban population exposure to tropospheric ozone is a serious health concern in Europe countries. Although there are insufficient evidence to derive a level below which ozone has no effect on mortality WHO (World Health Organization) uses SOMO35 (sum of means over 35 ppb) in their health impact assessments. Is this paper, the artificial neural network (ANN) approach was used to forecast SOMO35 at the national level for a set of 24 European countries, mostly EU members. Available ozone precursors’ emissions, population and climate data for the period 2003–2013 were used as inputs. Trend analysis had been performed using the linear regression of SOMO35 over time, and it has demonstrated that majority of the studied countries have a decreasing trend of SOMO35 values.The created models have made majority of predictions (≈60%) with satisfactory accuracy (relative error <20%) on testing, while the best performing model had R² = 0.87 and overall relative error of 33.6%. The domain of applicability of the created models was analyzed using slope/mean ratio derivate from the trend analysis, which was successful in distinguishing countries with high from countries with low prediction errors. The overall relative error was reduced to <14%, after the pool of countries was reduced based on the abovementioned criterion.