Air Quality Atmosphere and Health最新文献

Field measurements were conducted at a container harbor located in Ningbo-Zhoushan Port, China. Concentrations of fifty-three PAHs in airborne PM_2.5 were quantified using gas chromatography-mass spectrometry (GC-MS). The positive matrix factorization model and potential source contribution function analysis were used to evaluate the main sources of PAHs. The PM_2.5-bound PAH–associated inhalation lung cancer risks were estimated using a point estimate approach based on relative potency factors. Average concentrations were recorded for PM_2.5 (18 ± 6.0 μg/m³) and ∑₅₃PAHs (8.83 ± 8.42 ng/m³). The 4–6 ring PAHs accounted for approximately 85% of the total PAH concentrations, with the majority of these compounds being deemed carcinogenic. Five sources and mass contributions were determined by the positive matrix factorization (PMF) model: gasoline and diesel exhaust emissions (24.8%); volatilization or spill of petroleum and petroleum-related products (22.3%); heavy fuel oil combustion (18.2%); the mixed combustion emissions composed of coal and biomass combustion (18.1%); and natural gas combustion (16.7%). The potential source contribution function analysis suggested that PAHs in the harbor were greatly affected by long-distance input, especially from the North China Plain and the Yellow Sea. The calculated incremental lifetime lung cancer risk of PAH exposure was 8.07 and 702 cases per million people using the inhalation unit risk of exposure to the BaP value recommended by the California Environmental Protection Agency and the World Health Organization, respectively. Gasoline and diesel exhaust emissions (52.1%) have contributed more to the lung cancer risk. From a health risk standpoint, this measure could help to identify relevant sources of controls in port regions.

Abstract 

By combining atmospheric environment observational data, we expounded the characteristics, types, and potential source regions of air pollution in Zhengzhou using the characteristic radar chart, potential source contribution factor analysis, and concentration weighted trajectory analysis. We also calculated the excess risk (ER) of death caused by excessive air pollutants and explored a typical pollution process. The results showed that the average PM_2.5 concentration in autumn (October and November 2020) (55.5 μg/m³) was lower than that in winter (December 2020 and January 2021) (83.5 μg/m³), and January was the month most seriously affected by dust pollution. Secondary pollution was mainly concentrated in December and January, while dust pollution was mainly concentrated in October and January. Dust pollution was mainly affected by long-distance transport in the northwest, and the remaining pollution (except comprehensive pollution) was mainly affected by short-distance transport in the eastern cities (Jining and Xuzhou). The ER values were mainly attributed to excessive PM₁₀, PM_2.5, and NO₂. Secondary pollution and dust pollution were the main contributors to ER. During the typical pollution process, in the dust period (stage I), air masses were mainly affected by long-distance transport in the northwest, which displayed higher wind speed and lower relative humidity. Moreover, the PM_2.5/PM₁₀ value was below 0.4. During the heavy PM_2.5 pollution period (stage III), air masses were mainly affected by local transport in the northeast, which displayed lower wind speed and higher relative humidity. This period showed clear secondary pollution and more severe motor vehicle emissions.

Air pollution has been suggested as a trigger of hypertension (HTN), but it has not yet been fully analyzed how it impacts short-term HTN mortality. This study aimed to assess the association between short-term air pollution exposure and HTN mortality among Wuhan residents. This study used site-based HTN data from 2013 to 2019, in addition to data on meteorology and air pollution. Associations between short-term air pollution exposure and HTN mortality were assessed using generalized additive models (GAM). Positive links were found between PM_2.5, PM_10, SO₂, and NO₂ and HTN diseases mortality. In the single-pollutant model, for every 10 μg/m³ increase in PM_2.5, PM₁₀, SO₂, and NO₂, the percent changes (PCs) for HTN mortality were 0.991% (95% CI: 0.205, 1.778), 0.835% (95% CI: 0.336, 1.334), 4.344% (95% CI: 2.021, 6.668), and 1.740% (95% CI: 0.785, 2.694), respectively. O₃ was found negatively associated with HTN mortality, and the PC with every 10 μg/m³ increase after a accumulated lag of 7 days in O₃ exposure was −1.000% (95% CI: −1.760, −0.240). All pollutants risk estimates were robust to adjustment for co-pollutants. Stratified analysis showed that females and people aged over 65 were more likely to be harmed by air pollution. In conclusion, short-term exposure to air pollution may increase the risk of HTN mortality. The negative association reported in O₃ provides further insight into the health effects of air pollution.

The coronavirus disease 2019 (COVID-19) is a result of the infection by “severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and has caused various social and economic effects over the globe. As the SARS-CoV-2 is effectively inactivated by the exposure to the UV-B radiation (shorter than 315 nm), the exposure time for inactivation of the SARS-CoV-2 was estimated using the broadband UV observation instrument over 11 observation sites in South Korea. For the limitation of the UV biometer, which has limited spectral information, the coefficient for conversion from the erythemal UV (EUV) to the radiation for virus inactivation was adopted before estimating the inactivation time. The inactivation time of SARS-CoV-2 is significantly dependent on seasonal and diurnal variations due to the temporal variations of surface incident UV irradiance. The inactivation times in summer and winter were around 10 and 50 min, respectively. The inactivation time was unidentified during winter afternoons due to the weak spectral UV solar radiation in winter. As the estimation of inactivation time using broadband observation includes the uncertainty due to the conversion coefficient and the error due to the solar irradiance, the sensitivity analysis of the inactivation time estimation was also conducted by changing the UV irradiance.

Machine learning tools were used in the prediction of disease prevalence (bacterial, viral, and others) based on the pollutants like inhalable particulate matter, sulfur dioxide, nitrogen dioxide, carbon monoxide, and ground ozone. Random forest (RF), quadratic discriminant analysis (QDA), k-nearest neighbors (KNN), naïve Bayes (NB), and linear discriminant analysis (LDA) models were tested among others for better prediction accuracy, kappa statistic, sensitivity, and specificity. k-Nearest neighbors and linear discriminant analysis models yielded an accuracy of 85% relatively. The best model sensitivity of 100% was obtained with the k-nearest neighbor model, and a moderate kappa statistic was gained by the LDA model. As far as the model specificity is concerned, QDA yielded a value of 100%. Geographically weighted regression was applied to know the effect of spatial component across the data, and we obtained R² value of 0.63 with a moderate Akaike Information Criterion along with a minimal condition number reflecting the stability of the model. The disease prevalence variable was classified into high and low levels and was fed into the ML framework. The risk/susceptibility maps were produced with relative weights, and spatial distribution maps were presented. We conclude that though the ML and geographic information system–based tools can be used invariably, sufficient data is essential to generate a model with higher accuracy in terms of evaluation metrics, and geographically weighted regression at multiscale can also aid in knowing the characteristics of the model performance.

Alzheimer’s disease (AD) and Parkinson’s disease (PD) are important neurodegenerative disorders, especially in an aging population context that prevails in high-developed countries and Europe in particular. It is known that exposure to particulate matter (PM) leads to the production and deposition of aggregate clusters of proteins, which are linked to neurological disorders and impediments. Nonetheless, only a few works study the short-term exposure to PM and its association with hospital admissions or mortality due to AD or PD. This study assesses the association between exposure to PM and emergency hospital admissions for AD and PD in an aging metropole, serving as a case study for most European big cities. Daily emergency hospital admissions due to AD and PD data were obtained for the 2012 to 2015 period and multivariate Poisson regression models were used to evaluate the association between PM and admissions while controlling for the day of the week, seasonality, and environmental factors. Furthermore, lagged observations were assessed. Results show that an increase in exposure to PM_2.5 resulted in a percentage increase in emergency hospital admissions due to AD and PD. Also, age was an effect modifier for PD admissions. Additionally, greater effects were felt at shorter lags for AD and delayed/longer lags for PD. This study found a relationship between short-term exposure to PM and AD and PD hospital admissions in an urban context, drawing attention to the importance of air pollution for urban health, especially in areas with an aged population structure.

Dust storms are a common phenomenon in arid and semi-arid regions of the world. The erosion in the desert, agricultural, urban, and rural areas contributes to atmospheric mineral dust. Low vegetation cover drives intense dust storms in arid regions like northern Mexico and the southwestern USA. The seasonality associated with winter cold fronts from October to June regulates dust storms. The impact of dust storms is considerable, from massive soil deterioration to health problems caused by policies of changing land use from grasslands and forests to rainfed agriculture. This process has increased notably in recent decades. To identify potential dust storm events, we applied the criterion of a threshold wind speed of 9 m/s in all meteorological stations and data from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite. The analysis of wind data allowed identifying 245 cases of potential dust storms occurring between 2006 and 2018, but only 15 were chosen to be analyzed by numerical modeling. The WRF-Chem model version 3.6 was applied. Numerical experiments allowed calculating the fraction of PM10 emitted during each simulated event, where the concentration varied from 34 to 350 μg/m³. From the CALIPSO profiles, the atmospheric dust from the outputs of the numerical simulations was verified. Extensive distribution of dust revealed high contributions of PM10 that affect the air quality. The analysis of 13 years of wind data yielded 9 extreme wind events each year exceeding the speed threshold for dust removal.

Carbonyl compounds cause adverse effect on human health and play important roles in the atmospheric chemical reactions in the troposphere. However, their characteristics and complicated environmental impacts were poorly understood around petrochemical enterprises over North China. In this study, we found that acetone (2.8 ± 1.39 ppbv), acetaldehyde (2.74 ± 1.45 ppbv) and formaldehyde (2.73 ± 1.48 ppbv) were the three most abundant carbonyls, accounting for about 85% of the total concentration of the 15 carbonyls measured in the petrochemical industrial area of Jinan during the winter. The dominant sources of atmospheric carbonyls were vehicle exhaust, petrochemical processes and residential combustion as inferred by the good correlation between carbonyls and primary pollutants such as CO and NOx and the diagnostic ratios of formaldehyde to acetaldehyde (C₁/C₂, 1.00 ± 0.11). In addition, backward trajectories suggest that air mass transport also contributes to carbonyl compounds. Furthermore, carbonyls showed strong positive correlation with PM_2.5, probably due to the promoting effect of carbonyl compounds on atmospheric oxidation capacity (AOC), which in turn makes PM_2.5 concentration increase. On the other hand, PM_2.5 photochemical aging causes an increase in secondary carbonyls concentration. Overall, the present study indicates considerable impacts of carbonyls on PM_2.5 pollution in petrochemical enterprises area and suggests the urgent need for intensive study on the related processes.