Air pollution and human health risks: mechanisms and clinical manifestations of cardiovascular and respiratory diseases

Improved Z-number and fault tree analysis to predict the risk of air pollution due to ship boiler operation

Ships present a significant source of air pollution, contributing to environmental degradation and posing health risks. Boilers are a significant part of the vessels in which the water is heated to evaporate and generate steam. The boilers emit pollutants such as hazardous air pollutants (HAPs), particle pollution, and volatile organic compounds (VOC). This paper conducts an extended risk analysis for air pollution due to boiler operation on ships. An improved Z-numbers theory and Fault Tree Analysis (FTA) are adopted to predict risk. Whilst improved Z-number theory is capable of handling uncertainties inherent in risk assessment, the FTA presents systematically the causal relationships among various factors contributing to the risk of air pollution on ships. The findings show that the failure probability of air pollution during ship boiler operation is 2.08E-05 and BE-12 is the most significant event. Results provide valuable data to maritime stakeholders in fostering environmentally sustainable practices.

Environmental contamination with polycyclic aromatic hydrocarbons and contribution from biomonitoring studies to the surveillance of global health

This work presents an integrated overview of polycyclic aromatic hydrocarbons' (PAHs) ubiquity comprising environmental contamination in the air, aquatic ecosystems, and soils; characterizes the contamination in biota; and identifies main biomonitors and human exposure to PAHs and associated health risks. Urban centers and industrial areas present increased concentrations in the air (1344.4-12,300 versus 0.03-0.60 ng/m3 in industrial/urban and rural zones) and soils (0.14-1.77 × 106 versus 2.00-9.04 × 103 versus 1.59-5.87 × 103 ng/g in urban, forest, and rural soils), respectively. Increased concentrations were found in coastal zones and superficial waters as well as in sediments (7.00 × 104-1.00 × 109 ng/g). Benzo(a)pyrene, a carcinogenic PAH, was found in all environmental media. Mosses, lichens, tree leaves, bivalves, cephalopods, terrestrials' snails, and honeybees are good biomonitors of biota contamination. More studies are needed to improve characterization of PAHs' levels, distribution, and bioaccumulation in the environmental media and assess the associated risks for biota and human health. Actions and strategies to mitigate and prevent the bioaccumulation of PAHs in the environment and trophic chains toward the WHO's One-Health Perspective to promote the health of all ecosystems and human life are urgently needed.

Toxic effects due to exposure heavy metals and increased health risk assessment (leukemia)

Heavy metals (HM) are among the elements that are rare in nature and threaten human health, animals, and the environment. Fix sources including (power plants, industries, homes) and mobile sources include (cars and motorcycles) are the main sources production and emission of HM. It is important to understand the main information about sources of emission, chemical processes (reactions, oxidation, and leaching), and how they precipitate. The aim of this study was to evaluation an increased risk of leukemia due to exposure to HM. In this article narrative, the first literature search was performed with 580 articles according to different databases: Elsevier, PubMed, Web of science, Spring, and Google Scholar databases. 70 articles were included in the analysis process. Finally, 8 full-text articles were selected in this research. The search was restricted to English-language papers published between 2000 and 2021. In final stage literature, there is a notable health effect (carcinogenic) because of exposure to heavy metals. According to the results of this research natural procedures and human activities (industrial processes, car exhaust, and cigarette smoke) are the most important of ways that heavy metals can enter the natural cycle. Air, food, soil, water, and groundwater are the main sources of heavy metals that can cause severe disorders in the human body. After entering the body through ingestion heavy metals produce stable bio-toxic compounds. These compounds by disrupting biological processes, interrupt the body's functions and cause various cancers in the human body. The results of this study can help to politicians for make comprehensive decisions to solve the problem and increase public awareness of the use of protective equipment.

How urban growth dynamics impact the air quality? A case of eight Indian metropolitan cities

Air pollution is a matter of great significance that confronts the sustainable progress of urban areas. Against India's swift urbanization, several urban areas exhibit the coexistence of escalating populace and expansion in developed regions alongside extensive spatial heterogeneity. The interaction mechanism between the growth of urban areas and the expansion of cities holds immense importance for the remediation of air pollution. Henceforth, the present investigation utilizes geographically weighted regression (GWR) to examine the influence of urban expansion and population growth on air quality. The examination will use a decade of data on the variation in PM2.5 levels from 2010 to 2020 in eight Indian metropolitan cities. The study's findings demonstrate a spatial heterogeneity between urban growth dynamics and air pollution levels. Urban growth and the expansion of cities demonstrate notable positive impacts on air quality, although the growth of infilling within expanding urban areas can significantly affect air quality. Given the unique trajectories of urban development in developing countries, this research provides many suggestions for urban administrators to foster sustainable urban growth.

Cardiovascular disease, mortality and exposure to particulate matter (PM): a systematic review and meta-analysis

One of the main factors that causes health effects in humans such as hospital admissions for cardiovascular disease (HACVD), respiratory disease (RD), lung function, cardiovascular mortality (MCVD), lung cancer, and increased mortality is air pollution especially particulate matter (PM). This a systematic review and meta-analysis aims to investigate the effects of particulate matter on the occurrence of cardiovascular disease and mortality. A systematic review and meta-analysis of the literature was done from 2011 to 2021 based on various databases. Based on the result of this study, subgroup analysis based on temperature conditions showed a different estimation in cold cities (6.24, UR (4.36-8.12)), moderate cities (4.86, UR (3.57-6.15)) and warm cities (8.96, UR (7.06-10.86)). Test of group differences showed a significant difference (Q=12.22, p-value<0.001). There was publication bias among the studies (the Egger's test; (Z=14.18, p<0.001)). According result study pooled estimation of AP% for MCVD from the random-effect meta-analysis based on DerSimonian-Laird model, overall is 5.04, UR (3.65-6.43) (Figure 4). Subgroup analysis based on temperature conditions showed the estimation in cold cities (5.47, UR (3.97-6.97)) and moderate cities (4.65, UR (0.54-8.77)). Test of group differences showed a non-significant difference (Q=0.13, p-value=0.71). There was no publication bias among the studies (the Egger's test; (Z=0.82, p=0.376)). Exposed to air pollutants and particulate matter can be increase the risk of cardiovascular disease, respiratory disease, and cardiovascular mortality.

Estimating PM2.5 utilizing multiple linear regression and ANN techniques

The accurate prediction of air pollutants, particularly Particulate Matter (PM), is critical to support effective and persuasive air quality management. Numerous variables influence the prediction of PM, and it's crucial to combine the most relevant input variables to ensure the most dependable predictions. This study aims to address this issue by utilizing correlation coefficients to select the most pertinent input and output variables for an air pollution model. In this work, PM2.5 concentration is estimated by employing concentrations of sulfur dioxide, nitrogen dioxide, and PM10 found in the air through the application of Artificial Neural Networks (ANNs). The proposed approach involves the comparison of three ANN models: one trained with the Levenberg-Marquardt algorithm (LM-ANN), another with the Bayesian Regularization algorithm (BR-ANN), and a third with the Scaled Conjugate Gradient algorithm (SCG-ANN). The findings revealed that the LM-ANN model outperforms the other two models and even surpasses the Multiple Linear Regression method. The LM-ANN model yields a higher R2 value of 0.8164 and a lower RMSE value of 9.5223.

Knowledge, attitudes, and perception of air pollution in Ireland

Aim

Air pollution remains a major global public health challenge; and Ireland is no exception to the human health implications of exposure ambient air pollutants. Accurate and timely information can be critical to mitigate the harmful effects of air pollution. This study aimed to assess the knowledge, perceptions, and attitudes to poor air quality in Ireland to assist stakeholders in developing and implementing effective communication pieces and policies about the management of air pollution.

Study design

Cross-sectional population-based cohort.

Method

Quantitative data on knowledge, attitudes, and perceptions (KAP) were collected from respondents living across Ireland, and the results were analysed with SPSS (Version 28.0).

Results

Among the 1005 respondents included in this study, the mean [SD] age was 46.1 [15.3] years; 53% were female (n = 530); and 66% and 35% of respondents were aware of air pollution and its adverse effects on health at a national and local level respectively (n = 668 and n = 353 respectively). In addition, there were significant relationships between socio-demographic and air pollution awareness. There were correlation between respondent's age, gender, socio-economic group, and locality in Ireland.

Conclusion

This study demonstrates that environmental health literacy around air pollution in critically lacking among respondents. Given that air pollution is an increasingly important global priority, opportunities need to create to improve reach and impact of communication of air quality health risk and mitigation measures.

Global Chronic obstructive pulmonary disease burden attributable to air pollution from 1990 to 2019

BACKGROUND

The disease burden attributable to chronic obstructive pulmonary disease (COPD) is significant worldwide. Some studies have linked exposure to air pollution to COPD, but there has been little research on this.

METHODS

We aimed to assess the COPD-related disease burden attributable to air pollution from multiple epidemiological perspectives. This study conducted a three-stage analysis. Firstly, we reported on the burden of disease worldwide in 2019 by different subgroups including sex, age, region, and country. Secondly, we studied the trends in disease burden from 1990 to 2019. Finally, we explored the association of some national indicators with disease burden to look for risk factors.

RESULTS

In 2019, the death number of COPD associated with air pollution accounted for 2.32% of the total global death, and the number of DALY accounted for 1.12% of the global DALY. From 1990 to 2019, the death number of COPD associated with air pollution increased peaked at 1.41 million in 1993, fluctuated, and then declined. We found the same temporal pattern of DALY. The corresponding age-standardized rates had been falling. At the same time, the burden of COPD associated with air pollution was also affected by some national indicators.

CONCLUSIONS

This study indicated that air pollution-related COPD contributed to a significant global disease burden. We called for health policymakers to take action and interventions targeting vulnerable countries and susceptible populations.

Protocatechuic Acid and Syringin from Saussurea neoserrata Nakai Attenuate Prostaglandin Production in Human Keratinocytes Exposed to Airborne Particulate Matter

Saussurea neoserrata Nakai offers a reliable and efficient source of antioxidants that can help alleviate adverse skin reactions triggered by air pollutants. Air pollutants, such as particulate matter (PM), have the ability to infiltrate the skin and contribute to the higher occurrence of cardiovascular, cerebrovascular, and respiratory ailments. Individuals with compromised skin barriers are particularly susceptible to the impact of PM since it can be absorbed more readily through the skin. This study investigated the impact of protocatechuic acid and syringin, obtained from the n-BuOH extract of S. neoserrata Nakai, on the release of PGE₂ and PGD₂ induced by PM₁₀. Additionally, it examined the gene expression of the synthesis of PGE₂ and PGD₂ in human keratinocytes. The findings of this research highlight the potential of utilizing safe and efficient plant-derived antioxidants in dermatological and cosmetic applications to mitigate the negative skin reactions caused by exposure to air pollution.

Developing PM2.5 and PM10 prediction models on a national and regional scale using open-source remote sensing data

Clean air is the precursor to a healthy life. Air quality is an issue that has been getting under its well-deserved spotlight in the last few years. From a remote sensing point of view, the first Copernicus mission with the main purpose of monitoring the atmosphere and tracking air pollutants, the Sentinel-5P TROPOMI mission, has been widely used worldwide. Particulate matter of a diameter smaller than 2.5 and 10 μm (PM_2.5 and PM₁₀) significantly determines air quality. Still, there are no available satellite sensors that allow us to track them remotely with high accuracy, but only using ground stations. This research aims to estimate PM_2.5 and PM₁₀ using Sentinel-5P and other open-source remote sensing data available on the Google Earth Engine (GEE) platform for heating (December 2021, January, and February 2022) and non-heating seasons (June, July, and August 2021) on the territory of the Republic of Croatia. Ground stations of the National Network for Continuous Air Quality Monitoring were used as a starting point and as ground truth data. Raw hourly data were matched to remote sensing data, and seasonal models were trained at the national and regional scale using machine learning. The proposed approach uses a random forest algorithm with a percentage split of 70% and gives moderate to high accuracy regarding the temporal frame of the data. The mapping gives us visual insight between the ground and remote sensing data and shows the seasonal variations of PM_2.5 and PM₁₀. The results showed that the proposed approach and models could efficiently estimate air quality.

Influence of spatial distribution pattern of buildings on the distribution of urban gaseous pollutants

Buildings are the main component of urban, and their three-dimensional spatial patterns affect meteorological conditions and consequently, the spatial distribution of gaseous pollutants (CO, NO, NO₂, and SO₂). This study uses the Jinan Central District as the study area and constructs a building spatial distribution index system based on DEM, urban road network, and building big data. ANOVA and spatial regression models were used to study the effects of building spatial distribution indicators on the distribution of gaseous pollutants along with their spatial heterogeneity. The results showed that (1) the effects of most of spatial distribution indexes of building on the concentration distribution of the four gaseous pollutants were significant, with one-way ANOVA outcomes reaching a significance level of 0.01 or more. The DEM mean, building altitude, and their interaction with other building spatial distribution indicators are important factors affecting the distribution of gaseous pollutants; The interaction of other three-factor indicators did not have a significant effect on the distribution of gaseous pollutant concentrations. (2) The spatial distribution of CO and NO₂ is mainly influenced by the indicators of the spatial distribution of buildings in this study unit, and the effects of CO and NO₂ concentrations in adjacent study units are the result of the action of stochastic factors. The NO and SO₂ concentrations are influenced by the spatial distribution index of buildings in this study unit, the neighborhood homogeneity index, and NO and SO2 concentrations. (3) Spatial heterogeneity was observed in the effects of building spatial distribution indicators on the concentrations of different pollutants. The GWR models constructed using CO and NO concentrations and building spatial distribution indicators were well fitted globally and locally. The CO and NO concentrations were negatively correlated with the mean topographic elevation and NO concentrations were correlated with building density.

Dynamic analysis and optimal control of a class of SISP respiratory diseases

In this paper, the actual background of the susceptible population being directly patients after inhaling a certain amount of PM2.5 is taken into account. The concentration response function of PM2.5 is introduced, and the SISP respiratory disease model is proposed. Qualitative theoretical analysis proves that the existence, local stability and global stability of the equilibria are all related to the daily emission P0 of PM2.5 and PM2.5 pathogenic threshold K. Based on the sensitivity factor analysis and time-varying sensitivity analysis of parameters on the number of patients, it is found that the conversion rate β and the inhalation rate η has the largest positive correlation. The cure rate γ of infected persons has the greatest negative correlation on the number of patients. The control strategy formulated by the analysis results of optimal control theory is as follows: The first step is to improve the clearance rate of PM2.5 by reducing the PM2.5 emissions and increasing the intensity of dust removal. Moreover, such removal work must be maintained for a long time. The second step is to improve the cure rate of patients by being treated in time. After that, people should be reminded to wear masks and go out less so as to reduce the conversion rate of susceptible people becoming patients.

Seasonal variation of dissolved bioaccessibility for potentially toxic elements in size-resolved PM: Impacts of bioaccessibility on inhalable risk and uncertainty

The health effects of potentially toxic elements (PTEs) in airborne particulate matter (PM) are strongly dependent on their size distribution and dissolution. This study examined PTEs within nine distinct sizes of PM in a Chinese megacity, with a focus on their deposited and dissolved bioaccessibility in the human pulmonary region. A Multiple Path Particle Dosimetry (MPPD) model was used to estimate the deposited bioaccessibility, and an in-vitro experiment with simulated lung fluid was conducted for dissolved bioaccessibility. During the non-heating season, the dissolved bioaccessible fraction (DBF) of As, Cd, Co, Cr, Mn, Pb and V were greater in fine PM (aerodynamics less than 2.1 μm) than in coarse PM (aerodynamics between 2.1 and 10 μm), and vice versa for Ni. With the increased demand of heating, the DBF of Pb and As decreased in fine particle sizes, probably due to the presence of oxide/silicate compounds from coal combustion. Inhalation health risks based on the bioaccessible concentrations of PTEs displayed the peaks in <0.43 μm and 2.1-3.3 μm particulate sizes. The non-cancer risk was at an acceptable level (95th percentiles of hazard index (HI) was 0.49), but the cancer risk exceeded the threshold value (95th percentiles of total incremental lifetime cancer risk (TCR) was 8.91 × 10^-5). Based on the results of uncertainty analysis, except for the exposure frequency, the total concentrations and DBF of As and Cr in <0.43 μm particle size segment have a greater influence on the uncertainty of probabilistic risk.