Environmental data treatment to support exposure studies: The statistical behavior for NO2, O3, PM10 and PM2.5 air concentrations in Europe

Estimating the health impacts of exposure to Air pollutants and the evaluation of changes in their concentration using a linear model in Iran

In big and industrial cities of developing countries, illness and mortality from long-term exposure to air pollutants have become a serious issue. This research was carried out in 2019-2020 to estimate the health impacts of PM10, NO2 and O3 pollutants by using AirQ+ and R statistical programming software in Arak, Isfahan, Tabriz, Shiraz, Karaj, and Mashhad. Mortality statistics, number of people in required age groups, and amount of pollutants were gathered respectively from different agencies like Statistics and Information Technology of the Ministry of Health, Statistical Center, and Department of Environment and by using Excel, the average 24-hour and 1-hour concentration and maximum 8-hour concentration for PM10, NO2 and O3 pollutants were gathered. We used linear mixed impacts model to account for the longitudinal observations and heterogeneity of the cities. The results of the study showed high number of deaths due to chronic bronchitis in adults, premature death of infants, and respiratory diseases in Mashhad. This research highlights the importance of estimation of health impacts from exposure to air pollutants on residents of the studied cities.

Evaluating the mortality and health rate caused by the PM2.5 pollutant in the air of several important Iranian cities and evaluating the effect of variables with a linear time series model

All over the world, the level of special air pollutants that have the potential to cause diseases is increasing. Although the relationship between exposure to air pollutants and mortality has been proven, the health risk assessment and prediction of these pollutants have a therapeutic role in protecting public health, and need more research. The purpose of this research is to evaluate the ill-health caused by PM2.5 pollution using AirQ + software and to evaluate the different effects on PM2.5 with time series linear modeling by R software version 4.1.3 in the cities of Arak, Esfahan, Ahvaz, Tabriz, Shiraz, Karaj and Mashhad during 2019-2020. The pollutant hours, meteorology, population and mortality information were calculated by the Environmental Protection Organization, Meteorological Organization, Statistics Organization and Statistics and Information Technology Center of the Ministry of Health, Treatment and Medical Education for 24 h of PM2.5 pollution with Excel software. In addition, having 24 h of PM2.5 pollutants and meteorology is used to the effect of variables on PM2.5 concentration. The results showed that the highest and lowest number of deaths due to natural deaths, ischemic heart disease (IHD), lung cancer (LC), chronic obstructive pulmonary disease (COPD), acute lower respiratory infection (ALRI) and stroke in The effect of disease with PM2.5 pollutant in Ahvaz and Arak cities was 7.39-12.32%, 14.6-17.29%, 16.48-8.39%, 10.43-18.91%, 12.21-22.79% and 14.6-18.54 % respectively. Another result of this research was the high mortality of the disease compared to the mortality of the nose. The analysis of the results showed that by reducing the pollutants in the cities of Karaj and Shiraz, there is a significant reduction in mortality and linear modeling provides a suitable method for air management planning.

Using dispersion models at microscale to assess long-term air pollution in urban hot spots: A FAIRMODE joint intercomparison exercise for a case study in Antwerp

In the framework of the Forum for Air Quality Modelling in Europe (FAIRMODE), a modelling intercomparison exercise for computing NO2 long-term average concentrations in urban districts with a very high spatial resolution was carried out. This exercise was undertaken for a district of Antwerp (Belgium). Air quality data includes data recorded in air quality monitoring stations and 73 passive samplers deployed during one-month period in 2016. The modelling domain was 800 × 800 m2. Nine modelling teams participated in this exercise providing results from fifteen different modelling applications based on different kinds of model approaches (CFD - Computational Fluid Dynamics-, Lagrangian, Gaussian, and Artificial Intelligence). Some approaches consisted of models running the complete one-month period on an hourly basis, but most others used a scenario approach, which relies on simulations of scenarios representative of wind conditions combined with post-processing to retrieve a one-month average of NO2 concentrations. The objective of this study is to evaluate what type of modelling system is better suited to get a good estimate of long-term averages in complex urban districts. This is very important for air quality assessment under the European ambient air quality directives. The time evolution of NO2 hourly concentrations during a day of relative high pollution was rather well estimated by all models. Relative to high resolution spatial distribution of one-month NO2 averaged concentrations, Gaussian models were not able to give detailed information, unless they include building data and street-canyon parameterizations. The models that account for complex urban geometries (i.e. CFD, Lagrangian, and AI models) appear to provide better estimates of the spatial distribution of one-month NO2 averages concentrations in the urban canopy. Approaches based on steady CFD-RANS (Reynolds Averaged Navier Stokes) model simulations of meteorological scenarios seem to provide good results with similar quality to those obtained with an unsteady one-month period CFD-RANS simulations.

Toxicology of respiratory system: Profiling chemicals in PM10 for molecular targets and adverse outcomes

Numerous studies have shown that the increasing trend of respiratory diseases have been closely associated with the endogenous toxic chemicals (polycyclic aromatic hydrocarbons, heavy metal ions, etc.) in PM₁₀. In the present study, we aim to determine the strong correlations between the chemicals in PM₁₀ and the adverse consequences. We used the ChemView DB, the ToxRef DB and a comprehensive literature analysis to collect, identify, and evaluate the chemicals in PM₁₀ and their adverse effects on respiratory system, and then used the ToxCast DB to analyze their bioactivity and key targets through 1192 molecular targets and cell characteristic endpoints. Meanwhile, the bioinformatics analysis were carried out on the molecular targets to screen out prevention and treatment targets. A total of 310 chemicals related to the respiratory system were identified. An unsupervised two-directional heatmap was constructed based on hierarchical clustering of 227 chemicals by their effect scores. A subset of 253 chemicals with respiratory system toxicity had in vitro bioactivity on 318 molecular targets that could be described, clustered and annotated in the heatmap and bipartite network, which were analyzed based on the protein information in UniProt KB database and the software of GO, STRING, and KEGG. These results showed that the chemicals in PM₁₀ have strong correlation with different types of respiratory system injury. The main pathways of respiratory system injury caused by PM₁₀ are the Calcium signaling pathway, MAPK signaling pathway, and PI3K-AKT signaling pathway, and the core proteins in which are likely to be the molecular targets for the prevention and treatment of damage caused by PM₁₀.

Longitudinal Trends of the Annual Exposure to PM2.5 Particles in European Countries

BACKGROUND

PM_2.5 emission is known as a major challenge to environmental health and is the cause of approximately 7 million deaths annually. This study aimed at investigating the main patterns of PM_2.5 trend changes among European countries.

METHODS

The annual exposure to PM_2.5 pollutants was retrieved from the World Bank for 41 countries during 2010 to 2017, and a latent growth model was applied to identify the main patterns using Mplus 7.4 software.

RESULTS

Monitoring the overall mean annual exposure to PM_2.5 in the Europe showed a downward pattern with an annual decrease of 2.48% during the study period. Turkey had the highest PM_2.5 exposure with 43.82 μg/m³ in 2010, reaching 44.31 μg/m³ in 2017. Likewise, with 7.19 μg/m³ in 2010, Finland had the lowest exposure level which decreased to 5.86 μg/m³ in 2017. Two main patterns for the mean annual PM_2.5 exposure were identified via the latent growth model. Countries in the first pattern, including Turkey and Ukraine, had experienced a slow annual increase in the mean exposure of PM_2.5 pollutant. Likewise, the other 39 countries belonged to the second pattern with a moderate falling trend in the mean exposure to PM_2.5.

CONCLUSION

Although the trend changes of mean annual exposure to PM_2.5 in Europe were falling, Turkey and Ukraine had experienced a slow annual increase. It is advisable to take appropriate measures to curb the current raising exposure to PM_2.5 in Turkey and Ukraine.

The Process and Platform for Predicting PM2.5 Inhalation and Retention during Exercise

In recent years, people have been increasingly concerned about air quality and pollution since a number of studies have proved that air pollution, especially PM2.5 (particulate matter), can affect human health drastically. Though the research on air quality prediction has become a mainstream research field, most of the studies focused only on the prediction of urban air quality and pollution. These studies did not predict the actual impact of these pollutants on people. According to the researchers’ best knowledge, the amount of polluted air inhaled by people and the amount of polluted air that remains inside their body are two important factors that affect their health. In order to predict the quantity of PM2.5 inhaled by people and what they have retained in their body, a process and a platform have been proposed in the current research work. In this research, the experimental process is as follows: (1) First, a personalized PM2.5 sensor is designed and developed to sense the quantity of PM2.5 around people. (2) Then, the Bruce protocol is applied to collect the information and calculate the relationship between heart rate and air intake under different activities. (3) The amount of PM2.5 retained in the body is calculated in this step using the International Commission on Radiological Protection (ICRP) air particle retention formula. (4) Then, a cloud platform is designed to collect people’s heart rate under different activities and PM2.5 values at respective times. (5) Finally, an APP is developed to show the daily intake of PM2.5. The result reveals that the developed app can show a person’s daily PM2.5 intake and retention in a specific population.

Influencing factors of PM2.5 and O3 from 2016 to 2020 based on DLNM and WRF-CMAQ

In this study, distributed lag nonlinear models (DLNM) were built to characterize the non-linear exposure-lag-response relationship between the concentration of PM_2.5 and O₃ and multiple influencing factors, including basic meteorological elements and precursors. Then, a stratified analysis of different years, seasons, pollution levels, and wind direction was conducted. DLNMs and coupled Weather Research and Forecasting Model-Community Multi-scale Air Quality Model (WRF-CMAQ) were used to evaluate PM_2.5 and O₃ changes attributed to meteorological conditions and anthropogenic emissions comparing 2020 with 2016. As DLNMs showed, PM_2.5 pollution was promoted by low wind speed, high temperature, low humidity, and high concentrations of SO₂, NO₂, and O₃, among which NO₂ tended to be the dominant influencing factor. O₃ pollution was promoted by low wind speed, high temperature, low humidity, high concentration of PM_2.5 and low concentration of NO₂, among which temperature tended to be the dominant influencing factor. Moreover, north-south and easterly winds showed the greatest contribution to PM_2.5 and O₃, respectively. Both DLNMs and CMAQ showed that anthropogenic factors alleviated PM_2.5 pollution but aggravated O₃ pollution in 2020 in comparison with 2016, so did meteorological factors, but with smaller impacts. And anthropogenic influences were more evident in heavily polluted seasons for both PM_2.5 and O₃. This research may help understand the influencing factors of PM_2.5 and O₃ and provide scientific guide for abatement policies. Moreover, the good consistency in the results obtained from DLNMs and CMAQ indicated the reliability of the two models.

Spatiotemporal variations and risk assessment of ambient air O3, PM10 and PM2.5 in a coastal city of China

Rapid industrialization and urbanization has created significant air pollution problems that have recently begin to impact the lives and health of human beings in China. This study systematically investigated the spatiotemporal variations and the associated health risks of ambient O₃, PM₁₀ and PM_2.5 between 2016 and 2019. The relationships between the target air pollutants and meteorological conditions were further analyzed using the Spearman rank correlation coefficient method. The results demonstrated that the annual mean concentrations of PM₁₀ and PM_2.5 experienced a decreasing trend overall, and PM_2.5 significantly decreased from 1.54 μg/m³ in 2016 to 1.48 μg/m³ in 2019. In contrast, the annual mean concentrations of O₃ were nearly constant during the study period with a slight increasing trend. The pollutants exhibited different seasonal variations and cyclical diurnal variations. The most highest O₃ pollution was seen in spring and summer, while spring and winter were the seasons with the most PM₁₀ and PM_2.5 pollution. The highest concentrations of O₃ appeared in periods of strong solar radiation intensity and photochemical reactions. The highest concentrations of PM₁₀ and PM_2.5 appeared at commuting time. The pollutant concentrations were significantly affected by meteorological conditions. Finally, the non-carcinogenic risks from exposure to O₃, PM₁₀ and PM_2.5 were at an acceptable level (HI < 0.96) and O₃ accounted for ~50% of the total non-carcinogenic risks. However, PM_2.5 posed highly carcinogenic risks (2.5 × 10^-4 < CR < 1.6 × 10^-1) and O₃ exposure showed high potential ecological impacts on vegetation (AOT40: 23.3 ppm-h; W126: 29.0 ppm-h).