Considerable investigations have been carried out to address the relationship between ambient fine particulate matter (PM₂.₅) and blood pressure (BP) in patients with hypertension. However, few studies have explored the influence of PM₂.₅ and its constituents on Trimethylamine N-oxide (TMAO), an established risk factor for hypertension and cardiovascular disease (CVD), particularly in severely air-polluted areas. To explore the potential impact of PM₂.₅ constituents on BP, plasma hormones, and TMAO, a panel study was conducted to investigate changes in BP, plasma hormones, and TMAO in response to ambient air pollution exposure in stage 1 hypertensive young adults. Linear mixed effect models were used to estimate the cumulative effects of fine particulate matters (PM₂.₅) and its constituents on BP, plasma hormones and TMAO. We found that one interquartile range (IQR) (35 μg/m³) increase in 0–1 day moving-average PM₂.₅ concentrations was statistically significantly associated with elevated systolic blood pressure (SBP), diastolic blood pressure (DBP), and mean arterial pressure (MAP) with estimated values of 0.13 (95% confidence interval (CI): 0.03 to 0.23) mmHg, 0.18 (95% CI: 0.08 to 0.28) mmHg, and 0.17 (95% CI: 0.09 to 0.26) mmHg, respectively. Hormone disturbance in the renin-angiotensin-aldosterone system was also associated with PM₂.₅ exposure. Elevated TMAO levels with an IQR increase for 0–4, 0–5, 0–6 moving-average concentrations of PM₂.₅ were found, and the increased values ranged from 26.28 (95% CI: 2.92 to 49.64) to 60.78 (31.95–89.61) ng/ml. More importantly, the PM₂.₅-bound metal constituents, such as manganese (Mn), titanium (Ti), and selenium (Se) showed robust associations with elevated BP and plasma TMAO levels. This study demonstrates associations between PM₂.₅ metal constituents and increased BP, changes in plasma hormones and TMAO, in stage 1 hypertensive young adults. Source control, aiming to reduce the emission of PM₂.₅-bound metals should be implemented to reduce the risk of hypertension and CVD.

Emerging evidence witnesses the association of air pollution exposure with sleep disorders or the risk of obstructive sleep apnea (OSA); however, the results are not consistent. OSA patients with or without a low arousal threshold (LAT) have different pathology and therapeutic schemes. No study has evaluated the potential diverse effects of air pollution on the phenotypes of OSA. The current study aimed to evaluate the associations of short-term and long-term exposure to air pollution with sleep-disordered measures and OSA phenotypes. This cross-sectional study consisted of 4634 participants from a sleep center in Taipei from January 2015 to April 2019. The personal exposure to ambient PM₂.₅ and NO₂ was assessed by a spatial-temporal model. Overnight polysomnography was used to measure the sleep parameters. According to a developed clinical tool, we defined the low arousal threshold (LAT) and identified the OSA patients with or without LAT. We applied a generalized linear model and multinomial logistic regression model to estimate the change of sleep measures and risk of the OSA phenotypes, respectively, associated with an interquartile range (IQR) increment of personal pollution exposure after adjusting for the essential confounders. In the single-pollutant model, we observed the associations of NO₂ with sleep-disordered measures by decreasing the total sleep time, sleep efficiency, extending the time of wake after sleep onset, and the association of NO₂ with the increased risk of LAT OSA by around 15%. The two-pollutant model with both long-term and short-term exposures confirmed the most robust associations of long-term NO₂ exposure with sleep measures. An IQR increment of NO₂ averaged over the past year (6.0 ppb) decreased 3.32 min of total sleep time and 0.85% of sleep efficiency. Mitigating exposure to air pollution may improve sleep quality and reduce the risk of LAT OSA.

Legionella species are the etiological agent of Legionnaires' disease, a pathology easily contracted from water circuits and by the inhalation of aerosol droplets. This bacterium mainly proliferates in water: Legionella pneumophila is the most commonly isolated specie in water environments and consequently in water system, although further Legionella species have frequently been isolated, including Legionella dumoffii. The simultaneous presence of the two species in the water system can therefore lead to the simultaneous infection of several people, giving rise to harmful outbreaks. Ultraviolet inactivation of waterborne microorganisms offers a rapid and effective treatment technique and recently is getting more attention mostly to eliminate unsafe level of contamination. To tackle the issue, the inactivation of the two species of Legionella spp., namely L. pneumophila and L. dumoffii, by means of UV-A light emitting diodes (UV-A LED) system is explored. We used a commercially available UV-A LED at 365 nm wavelength, and the UV-A dose is given incrementally to the Legionellae with a concentration of 10⁶ CFU/mL in 0.9% NaCl (aq) solution. In this study, with a UV-A-dose of 1700 mJ/cm², the log-reduction of 3-log (99.9% inactivation) for L. pneumophila and 2.1-log (99.1% inactivation) for L. dumoffii of the contaminated water are achieved. The Electrical Energy per Order (EEO) is evaluated and showed this system is more economic and efficient in comparison with UV-C and UV-B LEDs. Following the support of this preliminary study with additional tests, aiming to validate the technology, we expect this device may be installed in water plants such as cooling systems or any water purification station in either industrial or home scales to reduce the risk of this infectious disease, preventing consumers' health.

Optimizing landscape pattern to reduce the risk of non-point source (NPS) pollution is an effective measure to improve river water quality. The “source-sink” landscape theory is a recent research tool for landscape pattern analysis that can effectively integrate landscape type, area, spatial location, and topographic features to depict the spatial heterogeneity of NPS pollution. Based on this theory, we quantitatively analyzed the influence of “source-sink” landscape pattern on the river water quality in one of the most intensive agricultural watersheds in Southeastern China. The results indicated that the proportion of “sink” landscape (68.59%) was greater than that of “source” landscape (31.41%) in the study area. In addition, when elevation and slope increased, the “source” landscape proportion decreased, and the “sink” landscape proportion increased. Nitrogen (N) and phosphorus (P) pollutants in rivers showed significant seasonal and spatial variations. Farmland was the primary source of nitrate nitrogen (NO₃⁻-N) and total nitrogen (TN) pollution, whereas residential land was the primary source of ammonium nitrogen (NH₄⁺-N) and total phosphorus (TP) pollution. Intensively cultivated areas and densely inhabited areas degraded water quality despite high proportions of forest land. The four “source-sink” landscape indices (LWLI, LWLI'e, LWLI's, LWLI'd) had significant positive correlations with NO₃⁻-N and TN and weak correlations with NH₄⁺-N and TP. The capacity of LWLI to quantify the NPS pollution was greater in agricultural areas than in residential areas. The “source-sink” landscape thresholds resulted in abrupt changes in water quality. When LWLI was ∼0.35, the probability of river water quality degradation increased sharply. The results suggest the importance of optimizing the “source-sink” landscape pattern for mitigating agricultural NPS pollution and provide policy makers with adequate new information on the agroecosystem-environmental interface in highly developed agricultural watersheds.

Reservoirs are an important type of drinking water source for megacities, while lots of reservoirs are threatened by odor problems during certain seasons. The influencing factors of odor compounds in reservoirs are still unclear. During August 2019, a nationwide survey investigating the distribution of odor compounds in reservoirs used as drinking water sources was conducted on seven reservoirs. 2-methylisoborneol (2-MIB) and geosmin were detected in almost every reservoir, and some odor compound concentrations even exceeded the odor threshold concentration. The average concentration of 2-MIB was 2.68 ng/L, and geosmin was 3.63 ng/L. The average chlorophyll a concentration was 8.25 μg/L. The dominant genera of phytoplankton in these reservoirs belonged to cyanobacteria and diatom. Statistical analysis showed that odor compound concentration was significantly related to the chlorophyll a concentration and indicated that the odor compounds mainly came from phytoplankton. The concentration of odor compounds in the euphotic zone was significantly related to phytoplankton species and biomass. Therefore, the odor compound concentrations in the subsurface chlorophyll maxima layer was generally higher than in the surface layer. However, the odor compounds in the hypolimnion layer were related to the density current. This research suggests that both phytoplankton proliferation events and heavy storm events are important risk factors increasing odor compounds in reservoirs. Control of algal bloom, in-situ profile monitoring system and depth-adjustable pumping system will greatly reduce the risk of odor problems in reservoirs using as water supplies for large cities.

Organic and inorganic pollutants are often co-sedimentary in soils and have the same sources in the urban environment. The identification of the sources and distribution of combined pollutants is a basic step in risk management. In this study, the levels of polycyclic aromatic hydrocarbons (PAHs) and heavy metals (HMs) were measured in urban park soils in Beijing. Bivariate local Moran’s I and positive matrix factorization (PMF) source apportionment were used to identify the spatial clustering patterns and potential sources of PAHs and HMs, as well as to ultimately define a pollution risk control area. The results revealed an obvious clustered distribution of PAHs and HMs in the park soils. High-high areas were defined as sites containing a complex mixture of pollutants, which were mainly located in the center and north of Beijing. High-low and low-high areas were located outside the city center but had the potential for combined pollution, and therefore require continuous attention. Bivariate local indicators of spatial association (LISA) enabled a more accurate analyses of the mechanism controlling the spatial distribution of PAH and HM combinations in urban parks. The source apportionment indicated that industrial and traffic emissions were the most important sources of the pollutant combinations in urban parks, with traffic emissions accounting for most of the pollution.

A limited but emerging body of evidence is suggestive for a beneficial association between contact with green spaces and glucose homeostasis in adults; however, such an evidence for children is scarce. We evaluated the association between time spent in green spaces and fasting blood glucose (FBG) levels and impaired fasting glucose (IFG, FBG≥110 mg/dL) in a population-based multicentric sample of 3844 Iranian schoolchildren aged 7–18 years (2015). Participants were instructed to report the average hours per week spent in green spaces separately during each season and in each type of green space (parks, woods/other natural green spaces, and private gardens/agricultural field) for a 12-month period preceding the interview. We developed linear and logistic mixed effects models with centre as random effect to evaluate the association of time spent in green spaces (separately for each type as well as all types together) with FBG and IFG, respectively, controlled for a wide range of covariates including household indicators of socioeconomic status. We observed inverse associations between time spent in green spaces, especially in natural green spaces, and FBG levels. Specifically, 1.83 h increase in the total time spent in green spaces was associated with −0.5 mg/dl (95% confidence intervals: −0.9, −0.1) change in FBG levels. We also observed reduced risk of IFG associated with time spent in green spaces; however, the association was statistically significant only for the time spent in natural green spaces. There were suggestions for stronger associations for those residing in urban areas and those from lower socioeconomic status groups; however, the interaction terms for socioeconomic status and urbanity were not statistically significant. Further longitudinal studies are required to replicate our findings in other settings with different climates and population susceptibilities.

Although in vitro and in vivo laboratory studies have demonstrated androgen and anti-androgen effects on male reproduction from phthalate exposures, human studies still remain inconsistent. Therefore, a case-control study (n = 289) was conducted to evaluate the associations between phthalate exposures, male infertility risks, and changes in metabolomic biomarkers. Regional participants consisted of fertile (n = 150) and infertile (n = 139) males were recruited from Nanjing Medical University’ affiliated hospitals. Seven urinary phthalate metabolites were measured using HPLC-MS/MS. Associations between levels of phthalate metabolites, infertility risks, and infertility-related biomarkers were statistically evaluated. MEHHP, one of the most abundant DEHP oxidative metabolites was significantly lower in cases than in controls (p = 0.039). When using the 1st quartile range as a reference, although statistically insignificant for odds ratios (ORs) of the 2nd, 3rd, and 4th quartiles (OR (95% CI) = 1.50 (0.34–6.48), 0.70 (0.14–3.52) and 0.42 (0.09–2.00), respectively), the MEHHP dose-dependent trend of infertility risk expressed as OR decreased significantly (p = 0.034). More interestingly, most of the phthalate metabolites, including MEHHP, were either positively associated with fertile prevention metabolic biomarkers or negatively associated with fertile hazard ones. Phthalate metabolism, along with their activated infertility-related biomarkers, may contribute to a decreased risk of male infertility at the subjects’ ongoing exposure levels. Our results may be illustrated by the low-dose related androgen effect of phthalates and can improve our understanding of the controversial epidemiological results on this issue.

Radical measures for controlling ambient air pollution sources were employed by the Chinese government during the Asia-Pacific Economic Cooperation (APEC) meeting in 2014, providing a unique case to evaluate the health effect benefits from such measures. To examine the cancer risk reduction from the source control measures during the APEC meeting, we estimated the reduction in population exposure to PM2.5 and PAHs and the reduction in PAHs-associated cancer risk if the control measures were sustained over time. We determined the population exposure to PM2.5 and PM2.5-bound PAHs for the 21.52 million Beijing residents using a Land Use Regression model to determine the spatial distribution of PM2.5 and a Monte Carlo approach to revise indoor/outdoor infiltration factor and time activity patterns. Into the model and approach, we incorporated the spatial variance and indoor/outdoor differences in the PM2.5 and PM2.5-bound PAHs concentrations, based on measurements. We then estimated lung cancer risk using the population attributable fraction (PAF), assuming the control measures were sustained over time. The mean PM2.5 exposure concentration decreased from 37.5 μg/m3 (CI:17.1–74.9 μg/m3) to 24.0 μg/m3 (CI:10.2–47.7 μg/m3), whereas the mean PM2.5-bound equivalent benzo[a]pyrene (BaPeq) exposure concentration decreased from 7.1 ng/m3 (CI:3.3–14.2 ng/m3) to 4.2 ng/m3 (CI:1.8–7.7 ng/m3), resulting in a reduction in the lung cancer PAF from 0.75% to 0.45%, if the measures were sustained over time.

Heavy metals (Cr, Co, Ni, As, Cd, and Pb) can be bound to PM adversely affecting human health. Quantifying the source impacts on heavy metals can provide source-specific estimates of the heavy metal health risk (HMHR) to guide effective development of strategies to reduce such risks from exposure to heavy metals in PM2.5 (particulate matter (PM) with aerodynamic diameter less than or equal to 2.5 μm). In this study, a method combining Multilinear Engine 2 (ME2) and a risk assessment model is developed to more effectively quantify source contributions to HMHR, including heavy metal non-cancer risk (non-HMCR) and cancer risk (HMCR). The combined model (called ME2-HMHR) has two steps: step1, source contributions to heavy metals are estimated by employing the ME2 model; step2, the source contributions in step 1 are introduced into the risk assessment model to calculate the source contributions to HMHR. The approach was applied to Huzou, China and five significant sources were identified. Soil dust is the largest source of non-HMCR. For HMCR, the source contributions of soil dust, coal combustion, cement dust, vehicle, and secondary sources are 1.0 × 10−4, 3.7 × 10−5, 2.7 × 10−6, 1.6 × 10−6 and 1.9 × 10−9, respectively. The soil dust is the largest contributor to HMCR, being driven by the high impact of soil dust on PM2.5 and the abundance of heavy metals in soil dust.