Effects of air pollutant emission on the prevalence of respiratory and circulatory system diseases in Linyi, China

Geospatial analysis of short-term exposure to air pollution and risk of cardiovascular diseases and mortality-A systematic review

The cardiovascular risk associated with short-term ambient air pollution exposure is well-documented. However, recent advancements in geospatial techniques have provided new insights into this risk. This systematic review focuses on short-term exposure studies that applied advanced geospatial pollution modelling to estimate cardiovascular disease (CVD) risk and accounted for additional unconventional neighbourhood-level confounders to analyse their modifier effect on the risk. Four databases were investigated to select publications between 2018 and 2023 that met the inclusion criteria of studying the effect of particulate matter (PM2.5 and PM10), SO2, NOx, CO, and O3 on CVD mortality or morbidity, utilizing pollution modelling techniques, and considering spatial and temporal confounders. Out of 3277 publications, 285 were identified for full-text review, of which 34 satisfied the inclusion criteria for qualitative analysis, and 12 of them were chosen for additional quantitative analysis. Quality assessment revealed that 28 out of 34 included articles scored 4 or above, indicating high quality. In 30 studies, advanced pollution modelling techniques were used, while in 4 only simpler methods were applied. The most pertinent confounders identified were socio-demographic variables (e.g., socio-economic status, population percentage by race or ethnicity) and neighbourhood-level built environment variables (e.g., urban/rural area, percentage of green space, proximity to healthcare), which exhibited varying modifier effects depending on the context. In the quantitative analysis, only PM 2.5 showed a significant positive association to all-cause CVD-related hospitalisation. Other pollutants did not show any significant effect, likely due to the high inter-study heterogeneity and a limited number of cases. The application of advanced geospatial measurement and modelling of air pollution exposure, as well as its risk, is increasing. This review underscores the importance of accounting for unconventional neighbourhood-level confounders to enhance the understanding of the CVD risk associated with short-term pollution exposure.

Appropriate control measure design by rapidly identifying risk areas of volatile organic compounds during the remediation excavation at an organic contaminated site

Many organic contaminated sites require on-site remediation; excavation remediation processes can release many volatile organic compounds (VOCs) which are key atmospheric pollutants. It is therefore important to rapidly identify VOCs during excavation and map their risk areas for human health protection. In this study, we developed a rapid analysis and assessment method, aiming to and reveal the real-time distribution of VOCs, evaluate their human health risks by quantitative models, and design appropriate control measures. Through on-site diagonal distribution sampling and analysis, VOCs concentration showed a decreasing trend within 5 m from the excavation point and then increased after 5 m with the increase in distance from the excavation point (p < 0.05). The concentrations of VOCs near the dominant wind direction were higher than the concentrations of surrounding pollutants. In contrast with conventional solid-phase adsorption (SPA) and thermal desorption gas chromatography-mass spectrometry (TD-GC/MS) methods for determining the composition and concentration of VOCs, the rapid measurement of VOCs by photo-ionization detector (PID) fitted well with the chemical analysis and modeling assessment of cancer/non-cancer risk. The targeting area was assessed as mild-risk (PID < 10 ppm), moderate-risk (PID from 10 to 40 ppm), and heavy-risk (PID > 40 ppm) areas. Similarly, the human health risks also decreased gradually with the distance from the excavation point, with the main risk area located in the dominant wind direction. The results of rapid PID assessment were comparable to conventional risk evaluation, demonstrating its feasibility in rapidly identifying VOCs releases and assessing the human health risks. This study also suggested appropriate control measures that are important guidance for personal protection during the remediation excavation process.

Application of machine learning to predict hospital visits for respiratory diseases using meteorological and air pollution factors in Linyi, China

Urbanization and industrial development have resulted in increased air pollution, which is concerning for public health. This study evaluates the effect of meteorological factors and air pollution on hospital visits for respiratory diseases (pneumonia, acute upper respiratory infections, and chronic lower respiratory diseases). The test dataset comprises meteorological parameters, air pollutant concentrations, and outpatient hospital visits for respiratory diseases in Linyi, China, from January 1, 2016 to August 20, 2022. We use support vector regression (SVR) to build models that enable analysis of the effect of meteorological factors and air pollutants on the number of outpatient visits for respiratory diseases. Spearman correlation analysis and SVR model results indicate that NO2, PM2.5, and PM10 are correlated with the occurrence of respiratory diseases, with the strongest correlation relating to pneumonia. An increase in the daily average temperature and daily relative humidity decreases the number of patients with pneumonia and chronic lower respiratory diseases but increases the number of patients with acute upper respiratory infections. The SVR modeling has the potential to predict the number of respiratory-related hospital visits. This work demonstrates that machine learning can be combined with meteorological and air pollution data for disease prediction, providing a useful tool whereby policymakers can take preventive measures.

Metal contents in house geckos (Squamata: Gekkonidae) from industrial and urban areas of southern Tamaulipas, Mexico and western Andalucía, Spain, may reflect airborne metal pollution

House geckos share living quarters with humans in the tropical and subtropical regions inhabited by these reptiles. Gecko behavior, biological traits, continuous exposure to suspended particulate matter 0 µm in diameter (PM₁₀) and dust, as well as status as exotic species, motivated the choice of these species to examine environmental exposure to ambient air pollutants, in particular metals, and subsequent accumulation in these organisms. One part of the study was conducted in Tamaulipas (Mexico) where Hemydactylus frenatus is abundant in urban and industrial environments, the other part was conducted in Andalucia (Spain) where Tarentola mauritanica is found in similar environments. Adult geckos were collected on buildings in locations affected by various air pollution sources. For both species, higher metal contents were observed in whole-body (including digestive tracts) analysis and were markedly different between collection sites. Contents in tails, digestive tracts, and carcasses without digestive tracts were not correlated. Based on contamination factor values, bioaccumulation in H. frenatus tissues occurred for 12 of the 15 metals analyzed. Data suggest that H. frenatus might serve as a biomonitor for Cu, Ni, Pb, Cr, Li, and V, whereas T. mauritanica might be a biomonitor for Cu, Ni, Pb, and Cr. To our knowledge, metal contents for H. frenatus are reported here for the first time. House gecko data could be integrated into a highly representative monitoring system and health risk assessments related to air quality in residential areas.