The relation between air pollution and respiratory deaths in Tehran, Iran- using generalized additive models

Air pollution we breathe: Assessing the air quality and human health impact in a megacity of Southeast Asia

With 24 million inhabitants and 6.6 million vehicles on the roads, Karachi, Pakistan ranks among the world's most polluted cities due to high levels of fine particulate matter (PM2.5). This study aims to investigate PM2.5 mass, seasonal and temporal variability, chemical characterization, source apportionment, and health risk assessment at two urban sites in Karachi. Samples were analyzed using ion chromatography and dual-wavelength optical transmissometer for various inorganic components (anions, cations, and trace elements) and black carbon (BC). Several PM2.5 pollution episodes were frequently observed, with annual mean concentrations at Kemari (140 ± 179 μg/m3) and Malir (95 ± 40.9 μg/m3) being significantly above the World Health Organization's guidelines of 5 μg/m3. Chemical composition at both sites exhibited seasonal variability, with higher pollution levels in winter and fall and lower concentrations in summer. The annual average BC concentrations were 4.86 ± 5.29 μg/m3 and 4.52 ± 3.68 μg/m3, respectively. A Positive Matrix Factorization (PMF) analysis identified 5 factors, crustal, sea salt, vehicular exhaust, fossil-fuel combustion, and industrial emission. The health risk assessment indicated a higher number of deaths in colder seasons (fall and winter) at the Kemari (328,794 and 287,814) and Malir (228,406 and 165,737) sites and potential non-carcinogenic and carcinogenic risks to children from metals. The non-carcinogenic risk of PM2.5 bound Pb, Fe, Zn, Mn, Cr, Cu and Ni via inhalation exposure were within the acceptable level (<1) for adults. However, potential non-carcinogenic and carcinogenic health risk posed by Pb and Cr through inhalation were observed for children. The findings exhibit critical levels of air pollution that exceed the safe limits in Karachi, posing significant health risks to children and sensitive groups. Our study underscores the urgent need for effective emission control strategies and policy interventions to mitigate these air pollution risks.

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.

Correlation between socioeconomic indices and epidemiological indices of thyroid cancer from 1990 to 2019 year: a global ecologic study

BACKGROUND

The incidence of thyroid cancer as the most common type of endocrine gland malignancy has risen more significantly than any malignancies in recent years. Estimated new cases of thyroid cancer in the United States in 2024 were 12,500 and 31,520 for men and women, respectively, and estimated deaths were 1,180 for women and 990 for men. Indices of socio-economic have been commonly used to measure the development of countries. Therefore, this study aimed to examine the correlation between indices of socioeconomic status and epidemiological indices of thyroid cancer throughout the world. In addition, this study has compared two indices of human development and a socio-demographic index.

METHOD

This worldwide ecological study used data on thyroid cancer incidence, mortality, human development index (HDI), and sociodemographic index (SDI) between 1990 and 2019 from the Global Burden of Disease (GBD). We evaluated the correlation between incidence and mortality rates with socioeconomic indices by using Pearson's correlation coefficient. Furthermore, for the first time, the generalized additive model (GAM) was employed for modeling. The statistical software R, version 4.2.2, was used to conduct all statistical analyses.

RESULTS

The correlation between the incidence of thyroid cancer and the HDI was significant and positive (r = 0.47, p-value < 0.001). While the correlation between thyroid cancer mortality and HDI was not statistically significant (r = 0.01, p-value = 0.076). Besides, the incidence of thyroid cancer was significantly positively correlated with SDI (r = 0.48, p-value < 0.001). The multiple GAM showed that for one unit increase in HDI, the risk of thyroid cancer was increased by 2.1 times (RR = 2.1, 95%CI = 2.04 to 2.19), and for one unit increase in SDI, the risk of thyroid cancer was shown to increase by 2.2 times. (RR = 2.2, 95%CI = 2.19 to 2.35).

CONCLUSION

It has been evident that countries with higher incidence of thyroid cancer display higher socioeconomic indices. While, countries with higher socioeconomic indices, report lower mortality rates. However, based on the modeling results, it can be concluded that the SDI is slightly more useful in this regard. Therefore, examining the epidemiological indices of thyroid cancer by socio-economic indices can be useful to reflect a clear image of the distribution of this cancer in each country, and can be used for planning cancer prevention strategies.

The impact of short-term exposures to ambient NO2, O3, and their combined oxidative potential on daily mortality

It is widely recognized that air pollution exerts substantial detrimental effects in human health and the economy. The potential for harm is closely linked to the concentrations of pollutants like nitrogen dioxide (NO2) and ozone (O3), as well as their collective oxidative potential (OX). Yet, due to the challenges of directly monitoring OX as an independent factor and the influences of different substances' varying ability to contain or convey OX, uncertainties persist regarding its actual impact. To provide further evidence to the association between short-term exposures to NO2, O3, and OX and mortality, this study conducted multi-county time-series analyses with over-dispersed generalized additive models and random-effects meta-analyses to estimate the mortality data from 2014 to 2020 in Jiangsu, China. The findings reveal that short-term exposures to these pollutants are linked to increased risks of all-cause, cardiovascular, and respiratory mortality, where NO2 demonstrates 2.11% (95% confidence interval: 1.79%, 2.42%), 2.28% (1.91%, 2.66%), and 2.91% (2.13%, 3.69%) respectively per every 10 ppb increase in concentration, and the effect of O3 is 1.11% (0.98%, 1.24%), 1.39% (1.19%, 1.59%), and 1.82% (1.39%, 2.26%), and OX is 1.77% (1.58%, 1.97%), 2.19% (1.90%, 2.48%), and 2.90% (2.29%, 3.52%). Notably, women and individuals aged over 75 years exhibit higher susceptibility to these pollutants, with NO2 showing a greater impact, especially during the warm seasons. The elevated mortality rates associated with NO2, O3, and OX underscore the significance of addressing air pollution as a pressing public health issue, especially in controlling NO2 and O3 together. Further research is needed to explore the underlying mechanisms and possible influential factors of these effects.

Short-term effects of air pollution on hospital admissions of respiratory diseases in Hamadan, Iran, 2015 to 2021

The short-term effects of air pollution on respiratory diseases have been reported in many countries. Urban areas are most affected because of the many sources of pollution and the large number of people living there. This study aims to investigate the effect of short-term exposure to air pollutants on respiratory hospital admissions in the city of Hamadan. In this ecological study, daily hospital admission data were collected from Shahid Beheshti Hospital in Hamadan. Daily information on air pollutants (CO, SO2, NO2, O3, PM2.5 and PM10) from Hamadan Department of Environment (DoE) organization and of climate factors from Hamadan Meteorological Office were collected. A negative binomial regression model was used to examine the effect of air pollution on daily respiratory hospitalizations. The effect of exposure to pollutants was measured whit different time lags (0-7 days). Furthermore, the effect of meteorological variables was controlled. Subgroup analyses were performed by sex and age group. A total of 12,454 hospitalizations for respiratory diseases were recorded. Results showed a strong and immediate effect of CO on respiratory hospital admissions with highest association at lag 7 (relative risk (RR) = 1.38, 95% CI: 1.33, 1.42). The effects of CO and SO2 on respiratory hospitalizations are greater for men than women. Regarding the short-term effects of PM2.5, SO2 and O3, adults (aged less than 65) were more prone to hospitalization for respiratory diseases. These results show that exposure to air pollution, particularly CO, may increase hospital admissions due to respiratory illness. So reducing the concentration of these pollutants can reduce the number of hospital admissions.

Mapping of salty aeolian dust-source potential areas: Ensemble model or benchmark models?

Considering the effects of dust on human health, environment, agriculture, and transportation, it is necessary to investigate dust emissions susceptibility. This study aimed to study the capability of different machine learning models in analyzing land susceptibility to dust emissions. At first, the dust-source areas were identified by examining the frequency of occurrence (FOO) of dusty days using the aerosol optical depth (AOD) of the MODIS sensor from 2000 to 2020 and field surveys. Then, the weighted subspace random forest (WSRF) model in comparison with three benchmark models-general linear model (GLM), boosted regression tree (BRT), and support vector machine (SVM)-was employed to predict land susceptibility to dust emissions and also to determine the importance of dust-drivers. The results revealed that the WSRF outperformed benchmark models. In a nutshell, the values of accuracy, Kappa, and probability of detection for all models were more than 97 %, and also the false alarm rate was less than 1 % for all models. Spatial analysis indicated a greater frequency of dust events in the outskirts of Urmia Lake (mainly in the eastern and southern parts). Furthermore, according to the map of land susceptibility to dust emissions produced by the WSRF model, about 4.5 %, 2.8 %, 1.8 %, 0.8 %, and 0.2 % of the salt land, rangeland, agricultural, dry-farming, and barren lands, respectively, associated with high and very high degrees of dust emissions susceptibility. Therefore, this study provided in-depth insights into the applicability of the ensemble model, WSRF, to precisely map dust emissions susceptibility.

Associations between air pollutant and pneumonia and asthma requiring hospitalization among children aged under 5 years in Ningbo, 2015-2017

Introduction

Exposure to ambient air pollutants is associated with an increased incidence of respiratory diseases such as pneumonia and asthma, especially in younger children. We investigated the relationship between rates of hospitalization of children aged under 5 years for pneumonia and asthma and the concentration of air pollutants in Ningbo between January 1, 2015 and August 29, 2017.

Methods

Data were obtained from the Ningbo Air Quality Data Real-time Publishing System and the big data platform of the Ningbo Health Information Center. A generalized additive model was established via logarithmic link function and utilized to evaluate the effect of pollutant concentration on lag dimension and perform sensitivity analysis.

Results

A total of 10,301 cases of pneumonia and 115 cases of asthma were identified over the course of this study. Results revealed that PM2.5, PM10, SO2 and NO2 were significantly associated with hospitalization for pneumonia and asthma in children under 5 years of age. For every 10-unit increase in lag03 air pollutant concentration, hospitalization for pneumonia and asthma due to PM2.5, PM10, SO2 and NO2 increased by 2.22% (95%CI: 0.64%, 3.82%), 1.94% (95%CI: 0.85%, 3.04%), 11.21% (95%CI: 4.70%, 18.10%) and 5.42% (95%CI: 3.07%, 7.82%), respectively.

Discussion

Adverse effects of air pollutants were found to be more severe in children aged 1 to 5 years and adverse effects due to PM2.5, PM10 and SO2 were found to be more severe in girls. Our findings underscore the need for implementation of effective public health measures to urgently improve air quality and reduce pediatric hospitalizations due to respiratory illness.

PAHs bound to submicron particles in rural Chinese homes burning solid fuels

Inhalation exposure to polycyclic aromatic hydrocarbons (PAHs) from indoor solid fuel combustion poses a high health risk, and PAHs bound to particles with smaller sizes (e.g., PM_1.0, aerodynamic diameter ≤ 1.0 µm) should be of particular concern since they can penetrate deep into pulmonary alveoli. However, PAHs bound to PM_1.0 was less studied compared with PAHs in total suspended particles or PM_2.5. In this study, multiple provincial field measurements were conducted to investigate 28 PAHs bound to PM_1.0 in rural Chinese homes. Daily averaged PM_1.0-PAH₂₈ concentrations ranged from 27 ng/m³ to 3795 ng/m³ (median: 233 ng/m³) and from 10 ng/m³ to 2978 ng/m³ (median: 87 ng/m³) in indoor and outdoor air, respectively. Higher concentrations were found in northern China in winter due to increased solid fuels consumption for space heating. The ambient pollution was lower during the non-heating season in Eastern China, where clean energy was preferred. Highly toxic congeners were more abundant in indoor air compared with outdoor air. The results of source apportionment revealed that solid fuel combustion was the primary contributor to rural household PM_1.0-PAHs, but other sources such as vehicles cannot be overlooked. The transition to cleaner energy can reduce the indoor PM_1.0-PAH₂₈ and BaP_eq-28 concentrations by 87% and 98%, respectively, and more efficient reduction was observed for highly toxic congeners. The estimated Incremental Lifetime Cancer Risk (ILCR) based on PM_1.0-PAH₂₈ ranged from 4.6 × 10^-5 to 3.4 × 10^-2, far exceeding the acceptable level of 10^-6. Over 60% of the ILCR could be attributed to inhalation exposure during childhood and adolescence.

City-scale assessment of long-term air quality impacts on the respiratory and cardiovascular health

Background

The impact of the urban environment on human health is a contemporary subject of environmental research. Air pollution is often considered a leading environmental driver. However, a plethora of other factors within the urban exposome may be involved. At the same time, the resolution of spatial data is also an important facet to consider. Generally, systematic tools for accurate health risk assessment in the urban environment are missing or are not implemented.

Methods

The long-term impact of air quality (PM₁₀, PM_2.5, NO₂, benzene, and SO₂) on respiratory and cardiovascular health was assessed with a log-linear model. We used the most accurate health data in high city scale spatial resolution over the period 2010 to 2018. Selected external exposome parameters were also included in the analysis.

Results

Statistically significant associations between air pollution and the health of the urban population were found. The strongest association was between benzene and the incidence of bronchitis in the adult population [RR 1.552 95% CI (1.415-1.704) per 0.5 μg/m³ change in benzene concentration]. A similar relation was observed between NO₂ and the same health condition [RR 1.483 95% CI (1.227-1.792) per 8.9 μg/m³ of change in NO₂]. Other weaker associations were also found between asthma in children and PMs, NO₂, or benzene. Cardiovascular-related hospitalizations in the general population were linked with NO₂ [RR 1.218 95% CI (1.119-1.325) per 9.7 μg/m³ change in NO₂]. The remaining pollutants were slightly less but still significantly associated with cardiovascular-related hospitalizations.

Conclusion

Our findings are mostly highly statistically significant (p ≤ 0.001) and are in line with current literature on the adverse effects of air pollution on the human population. The results highlight the need for continual improvements in air quality. We propose the implementation of this approach as a systematic tool for the investigation of possible health risks over a long period of time. However, further research involving other variables is an essential step toward understanding the complex urban exposome and its implications for human health. An increase in data spatial resolution is especially important in this respect as well as for improving city health risk management.

Environmental Stressors and the PINE Network: Can Physical Environmental Stressors Drive Long-Term Physical and Mental Health Risks?

Both psychosocial and physical environmental stressors have been linked to chronic mental health and chronic medical conditions. The psycho-immune-neuroendocrine (PINE) network details metabolomic pathways which are responsive to varied stressors and link chronic medical conditions with mental disorders, such as major depressive disorder via a network of pathophysiological pathways. The primary objective of this review is to explore evidence of relationships between airborne particulate matter (PM, as a concrete example of a physical environmental stressor), the PINE network and chronic non-communicable diseases (NCDs), including mental health sequelae, with a view to supporting the assertion that physical environmental stressors (not only psychosocial stressors) disrupt the PINE network, leading to NCDs. Biological links have been established between PM exposure, key sub-networks of the PINE model and mental health sequelae, suggesting that in theory, long-term mental health impacts of PM exposure may exist, driven by the disruption of these biological networks. This disruption could trans-generationally influence health; however, long-term studies and information on chronic outcomes following acute exposure event are still lacking, limiting what is currently known beyond the acute exposure and all-cause mortality. More empirical evidence is needed, especially to link long-term mental health sequelae to PM exposure, arising from PINE pathophysiology. Relationships between physical and psychosocial stressors, and especially the concept of such stressors acting together to impact on PINE network function, leading to linked NCDs, evokes the concept of syndemics, and these are discussed in the context of the PINE network.

Drivers of PM2.5 in the urban agglomeration on the northern slope of the Tianshan Mountains, China

Fine particulate matter (PM_2.5) is a major source of air pollution in China. Although there have been many studies of the drivers of PM_2.5 pollution in the megacities clustered in eastern China, the behavior of PM_2.5 in the northwestern urban agglomeration is not well understood. This study used near-surface observation data for 2015-2019 obtained from the national air environmental monitoring network to examine variation in PM_2.5 in the urban agglomeration on the northern slopes of the Tianshan Mountains (UANSTM). Two-factor interaction provided new insights into the dominant factors of PM_2.5 in the study region. The annual average PM_2.5 concentrations over the study period was 54.3 μg/m³, with an exceedance rate of 23.3%. Wavelet analysis showed two dominant cycles of 320-370 d and 150-200 d with high pollution events occurring in winter. The generalized additive model (GAM) contained linear functions of pressure, non-linear functions of SO₂, NO₂, relative humidity, sunshine duration and temperature. The two most primary variables, NO₂ and SO₂, represent 20.65% and 19.54% of the total deviance explained, respectively, while the meteorological factors account for 36.1% of the total deviance explained. In addition, the interaction between NO₂ and other factors had the strongest effect on PM_2.5. The deviance explained in the two factor interaction model (88.5%) was higher than that in the single factor model (78.4%). Our study emphasized that interaction between meteorological factors and pollutant emissions enhanced the impact on PM_2.5 compared with individual factors, which can provide a scientific basis for developing effective emission reduction strategies in UANSTM.

Air Pollution and Hospital Admission for Epilepsy in Kerman, Iran

Background: This study aimed to investigate the relation between air pollution and epilepsy admissions in Kerman, Iran. Methods: In this ecological study, the concentrations of ambient air pollutants and meteorological data were obtained from Kerman Environmental Protection Agency and Kerman Meteorology Organization, respectively. Additionally, epilepsy admission data were obtained from Kerman’s Shafa hospital epilepsy registry. Generalized additive models with lags up to 7 days were used to estimate rate ratios (RRs). Results: Within 2008 to 2020, 894 epilepsy admissions occurred in Kerman, 498 cases (55.7%) of whom were male. The strongest relations of epilepsy admission were observed in the over 59-year group for carbon monoxide (CO) in lag 0 (RR = 2.1455, 95% CI: 1.5823 - 2.9091), nitrogen dioxide (NO2) in lag 0 (RR = 1.0409, 95% CI: 1.0282 - 1.0537), and particulate matter under 2.5 microns (PM2.5) in lag 5 (RR = 1.0157, 95% CI: 1.0062 - 1.0252). There were also significant associations for particulate matter under 10 microns (PM10) in the under 18-year group in lag 2 (RR = 1.0064, 95% CI: 1.0029 - 1.0098), ozone in lag 0 (RR = 0.9671, 95% CI: 0.9581 - 0.9761), and sulfur dioxide in lag 5 (RR = 0.9937, 95% CI: 0.9891 - 0.9983). Conclusions: Exposure to CO, NO2, PM2.5, or PM10 air pollutants might be a risk factor for epilepsy admissions in Kerman. Epilepsy patients should better stay away from exposure to polluted air. Staying at home on polluted days or residing in areas with less air pollution might be an option.

Short-Term Effects of PM10, NO2, SO2 and O3 on Cardio-Respiratory Mortality in Cape Town, South Africa, 2006–2015

Background: The health effect of air pollution is rarely quantified in Africa, and this is evident in global systematic reviews and multi-city studies which only includes South Africa. Methods: A time-series analysis was conducted on daily mortality (cardiovascular (CVD) and respiratory diseases (RD)) and air pollution from 2006–2015 for the city of Cape Town. We fitted single- and multi-pollutant models to test the independent effects of particulate matter (PM10), nitrogen dioxide (NO2), sulphur dioxide (SO2) and ozone (O3) from co-pollutants. Results: daily average concentrations per interquartile range (IQR) increase of 16.4 µg/m3 PM10, 10.7 µg/m3 NO2, 6 µg/m3 SO2 and 15.6 µg/m3 O3 lag 0–1 were positively associated with CVD, with an increased risk of 2.4% (95% CI: 0.9–3.9%), 2.2 (95% CI: 0.4–4.1%), 1.4% (95% CI: 0–2.8%) and 2.5% (95% CI: 0.2–4.8%), respectively. For RD, only NO2 showed a significant positive association with a 4.5% (95% CI: 1.4–7.6%) increase per IQR. In multi-pollutant models, associations of NO2 with RD remained unchanged when adjusted for PM10 and SO2 but was weakened for O3. In CVD, O3 estimates were insensitive to other pollutants showing an increased risk. Interestingly, CVD and RD lag structures of PM10, showed significant acute effect with evidence of mortality displacement. Conclusion: The findings suggest that air pollution is associated with mortality, and exposure to PM10 advances the death of frail population.

A Review of Studies Using Air Q Software for Prediction of Air Pollution Health Effects in Iran

PURPOSE OF REVIEW

Exposure to air pollutants may lead to various health effects and is a major public health issue. Concerns about these effects exist in both developed and developing countries. The Air Q software was developed to estimate the health impacts of air pollution based on reported levels of air pollutants in real world studies. In Iran several studies have been conducted to estimate human morbidity and mortality based on this software. We conducted this review to summarize articles which have predicted the effects of air pollution on human health in Iran using Air Q. We conducted a systematic search for relevant studies published until 24 April 2021 in Web of Science, PubMed, Scopus, and SID (Scientific Information Database which includes articles in Farsi language). We applied no time or language restrictions.

RECENT FINDINGS

A total of 44 studies out of 525 identified articles met our inclusion criteria. The main air pollutants under investigation were particulate matter (PM), NO₂, O₃, and SO₂. Most studies were conducted in metropolitan areas, such as Ahvaz (9 studies), Tehran (9 studies), and Shiraz (7 studies). In all studies, the levels of most air pollutants were higher than the 2005 WHO guideline levels and were predicted to be related to considerable health effects. However, it was not possible to aggregate the results and report the total number of casualties during these years, because studies were done in different cities with fluctuating levels of multiple pollutants and in different years and time frames. This systematic review showed that air pollution remains at unacceptably high levels resulting in substantial detrimental health effects in various Iranian cities. Using clean renewable energies, increasing human capital, and increasing green spaces and vegetation can help improve air pollution and decrease human casualties in Iran.

Estimates of incidence, prevalence, mortality, and disability-adjusted life years of lung cancer in Iran, 1990-2019: A systematic analysis from the global burden of disease study 2019

BACKGROUND

Lung cancer is one of the leading cancers, with a high burden worldwide. As a developing country, Iran is facing with population growth, widespread tobacco use, demographic and epidemiologic changes, and environmental exposures, which lead to cancers becoming a severe concern of public health in Iran. We aimed to examine the burden of lung cancer and its risk factors in Iran.

METHODS

We utilized the Global Burden of Disease 2019 data and analyzed the total burden of the lung cancer and seven related risk factors by sex, age at national and sub-national levels from 1990 to 2019.

RESULTS

The lung cancer age-standardized death rate increased from 11.8 (95% Uncertainty Interval: 9.7-14.4) to 12.9 (11.9-13.9) per 100,000 between 1990 and 2019. This increase was among women from 5 (4.2-7.1) to 8 (7.2-8.8) per 100,000; in contrast, there was a decline among men from 18.5 (14.8-22.6) to 17.8 (16.2-19.4) per 100,000. The burden of lung cancer is concentrated in the advanced age groups. Smoking with 53.5% of total attributable deaths (51.0%-55.9%) was the leading risk factor. At the provincial level, there was a wide range between the lowest and highest, from 8.3 (7.0-10.0) to 19.1 (16.4-22.0) per 100,000 population in the incidence rate and from 8.7 (7.3-10.3) to 20.6 (17.7-24.0) per 100,000 population in mortality rate, respectively in Tehran and West Azerbaijan provinces in 2019.

CONCLUSION

The increasing trend of lung cancer burden among the entire Iranian population, the inter-provincial disparities, and the significant rise in burden of this cancer in women necessitate the urgent implementation and development of policies to prevent and manage lung cancer burden and strategies to reduce exposure to risk factors.

Effects of meteorological parameters and fuel composition on the air pollution production from motor vehicles

The aim of this study was to investigate the effect of changes in meteorological parameters and fuel composition on the emission rate of air pollutants in the vehicle fleet of the Tehran Metropolis. The results of this study can be used in management decisions to reduce the emission of air pollutants. In this paper, based on the international vehicle emission model and using mathematical equations, the effects of changing meteorological parameters and fuel composition on the emission of pollutants were modeled. The emission rates of CO, VOCs, and NOx pollutants were the most sensitive to the changes in meteorological parameters, respectively. Among all parameters studied in this research, the changes in sulfur level had the greatest effect on the emission of pollutants from the vehicle fleet of Tehran Metropolis. If the fuel was replaced with Euro 5 standard instead of Euro 3, the emission rates of CO, VOCs, NOx, PM, and SOx pollutants from the vehicle fleet of Tehran Metropolis would be reduced by 9%, 6%, 5%, 14%, and 90%, respectively. Managing and reducing the sources of production and emission of air pollution is one of the best ways to reduce the air pollution. In general, since the emission of pollutants from the fleet of Tehran Metropolis in the cold seasons of the year is greater than during hot seasons and the problem of air pollution is exacerbated by air stability, using Euro 5 fuel in cold seasons is one of the efficient ways to reduce the air pollution.

Spatiotemporal Evolution and Prediction of AOT in Coal Resource Cities: A Case Study of Shanxi Province, China

As aerosols in the air have a great influence on the health of residents of coal resource-based cities, these municipalities are confronting the dilemma of air pollution that is caused by the increase of suspended particles in the atmosphere and their development process. Aerosol optical thickness could be used to explore the aerosol temporal and spatial variations and to develop accurate prediction models, which is of great significance to the control of air pollution in coal resource-based cities. This paper explored the temporal spatial variation characteristics of aerosols in coal resource-based regions. A total of 11 typical coal-resource prefecture-level cities in the Shanxi Province were studied and inverted the aerosol optical thickness (AOT) among these cities based on MODIS (Moderate Resolution Imaging Spectroradiometer) data and analyzed the significant factors affecting AOT. Through inputting significant correlation factors as the input variables of NARX (nonlinear auto regressive models with exogenous inputs) neural network, the monthly average AOTs in the Shanxi Province were predicted between 2011 and 2019. The results showed that, in terms of time series, AOT increased from January to July and decreased from July to December, the maximum AOT was 0.66 in summer and the minimum was 0.2 in autumn, and it was related to the local monsoon, temperature, and humidity. While as far as the space alignment is concerned, the figure for AOT in Shanxi Province varied significantly. High AOT was mainly concentrated in the centre and south and low AOT was focused on the northwestern part. Among the positively correlated factors, the correlation coefficient of population density and temperature exceeded 0.8, which was highly positive, and among the negatively correlated factors, the correlation coefficient of NDVI exceeded -0.8, which was highly negative. After improving the model by adding the important factors that were mentioned before, the error between the predicted mean value and the actual mean value was no more than 0.06. Considering this charge, the NARX neural network with multiple inputs can contribute to better prediction results.

Ambient air pollution and multiple sclerosis: a systematic review

OBJECTIVES

Some studies have shown that environmental risk factors, including air pollution, might be related to the incidence or recurrence of multiple sclerosis (MS). This systematic review was conducted to investigate the relation between air pollution and MS.

METHODS

A systematic search was conducted in PubMed, Scopus, Science Direct, Embase, and Web of Science; until January 2020 with no restrictions. The search strategy was conducted with air pollution key words such as CO, PM_2.5, PM₁₀, SO₂, and NO₂, for exposure and the key word "Multiple sclerosis" as the outcome.

RESULTS

Eventually, after applying the inclusion and exclusion criteria, 17 articles were included. The methodologies and outcomes reported were heterogeneous and different metrics had been used in the results; therefore conducting a meta-analysis was not possible. Eight studies had analyzed the relation between particulate matter (PM) and the prevalence or relapse of MS and had observed a significant relation. NO₂ and NOx were associated with recurrence or prevalence of MS in three studies. But, in three cohort studies, no association was observed between air pollution and recurrence or occurrence of MS.

CONCLUSIONS

The results of this systematic review show that outdoor air pollution, especially PM and nitrogen oxides might be related to the prevalence or relapse of MS.

Estimation of economic costs of air pollution caused by motor vehicles in Iran (Isfahan)

Since mobile sources are one of the most important sources of air pollution, this paper tries to estimate the health effects and economic burden due to fine particulate matter (PM_2.5) concentrations from motor vehicles. In this regard, we calculate the economic costs of air pollution emitted by vehicles in Isfahan over the period from March 2018 to March 2020. The concentration of urban traffic pollution based on the generalized additive model (GAM) as well as spatial distribution of pollution is estimated. Health effects are evaluated using AirQ⁺ updated by the WHO European Centre for Environment and Health. Economic burden of mortality attributable air pollution from traffic is calculated using value of a statistical life (VOSL), and the value of life years (VOLY) approach. The results indicated that the number of deaths attributable to PM_2.5 from motor vehicles in these two consecutive years was 136 (95%CI: 89-179), and 147 cases (95%CI: 96-194), respectively. The number of years of life lost due to premature death from air pollution was 2079 years annually. The economic costs imposed under VOSL approach were on average USD 51.7 (95%CI: 43-75) million per year, and according to VOLY approach USD 11.5 (95%CI: 9-13) million per year. These results help to analyze the cost-benefit and prioritize control measures to reduce air pollution. In addition, combination of these results with other externality cost of road traffic can take account for urban transportation planning.

Dispersion of NO2 and SO2 pollutants in the rolling industry with AERMOD model: a case study to assess human health risk

Steel and rolling industry are the most important industries polluting the environment. Therefore, aim of this study is to make an emission model for SO2 and NO2 pollutants released from the rolling industry of Sepid-Farab Kavir Steel (SKS) complex using the AERMOD model and health risk assessment. Sampling pollutants released from SKS complex was performed in January 2017 at 10 different sites. Distribution of these pollutants was investigated by AERMOD model, domain site of AERMOD was designed for area around the factory with a radius of 30 km, and also SO2 and NO2 modeling was performed for both natural gas and liquid fuel. Human health risk assessment was also studied. The results of this study demonstrated the emission of SO2 and NO2 from this complex is less than the maximum allowable, when used natural gas as the main fuel. The hourly concentration of SO2 reached about 324 μg/m3, which in higher than the standard value for 1 h. Considering the findings, the urban gas is considered as a clean source in terms of furnace air output and the concentration of emitted pollutants. Also, it has no side effects on workers' health.

Short-term exposure to nitrogen dioxide and mortality: A systematic review and meta-analysis

BACKGROUND

Ambient air pollution has been characterized as a leading cause of mortality worldwide and has been associated with cardiovascular and respiratory diseases. There is increasing evidence that short-term exposure to nitrogen dioxide (NO₂), is related to adverse health effects and mortality.

METHODS

We conducted a systematic review of short-term NO₂ and daily mortality, which were indexed in PubMed and Embase up to June 2021. We calculated random-effects estimates by different continents and globally, and tested for heterogeneity and publication bias.

RESULTS

We included 87 articles in our quantitative analysis. NO₂ and all-cause as well as cause-specific mortality were positively associated in the main analysis. For all-cause mortality, a 10 ppb increase in NO₂ was associated with a 1.58% (95%CI 1.28%-1.88%, I² = 96.3%, Eggers' test p < 0.01, N = 57) increase in the risk of death. For cause-specific mortality, a 10 ppb increase in NO₂ was associated with a 1.72% (95%CI 1.41%-2.04%, I² = 87.4%, Eggers' test p < 0.01, N = 42) increase in cardiovascular mortality and a 2.05% (95%CI 1.52%-2.59%, I² = 78.5%, Eggers' test p < 0.01, N = 38) increase in respiratory mortality. In the sensitivity analysis, the meta-estimates for all-cause mortality, cardiovascular and respiratory mortality were nearly identical. The heterogeneity would decline to varying degrees through regional and study-design stratification.

CONCLUSIONS

This study provides evidence of an association between short-term exposure to NO₂, a proxy for traffic-sourced air pollutants, and all-cause, cardiovascular and respiratory mortality.

Polycyclic aromatic hydrocarbons in PM1, PM2.5 and PM10 atmospheric particles: identification, sources, temporal and spatial variations

This study reports temporal and spatial variations of 16 different species of particulate polycyclic aromatic hydrocarbons (particle-bonded PAHs) in the indoor and outdoor environments of three sampling sites in Bandar Mahshahr city, Iran. A low-volume air sampler was employed to collect size-segregated particulate matter during winter (October to December 2015), and summer (July to September 2016). The results showed that the annual concentrations of indoor and outdoor PM10 and PM2.5 were much higher than the related World Health Organization guidelines. The concentration of total particle-bonded PAHs (TPAHs) was higher in winter than in summer and a significant difference between the two sampling seasons was observed. The indoor and outdoor carcinogenic PAHs to TPAHs concentrations ratios in the sampling sites in summer and winter were as follow: for PM10 40.15-42.51%, PM2.5 41.30-42.97%, and PM1 43.07-44.36%, respectively; furthermore, the smaller the particle size, the higher the percentage of carcinogenic PAHs. 2 ring PAHs had a very small contribution to the total PAHs (about 1%), whereas PAHs with 3-to-4 rings had much larger contributions, ranging from 71.65% to 75.17%. The results demonstrated that as PM size decreased, the proportion of 5-to-6-ring PAHs to the total PAHs increased. Since 5-to-6- ring PAHs are considered to be more toxic, hence more attention should be paid to fine particles. The diagnostic ratios of indoor and outdoor of three sampling sites in both seasons suggested that petrogenic sources, as well as combustion of petroleum and other fossil fuels were the main PAHs sources.

Multiple air pollutant exposure and lung cancer in Tehran, Iran

Lung cancer is the most rapidly increasing malignancy worldwide with an estimated 2.1 million cancer cases in the latest, 2018 World Health Organization (WHO) report. The objective of this study was to investigate the association of air pollution and lung cancer, in Tehran, Iran. Residential area information of the latest registered lung cancer cases that were diagnosed between 2014 and 2016 (N = 1,850) were inquired from the population-based cancer registry of Tehran. Long-term average exposure to PM₁₀, SO₂, NO, NO₂, NO_X, benzene, toluene, ethylbenzene, m-xylene, p-xylene, o-xylene (BTEX), and BTEX in 22 districts of Tehran were estimated using land use regression models. Latent profile analysis (LPA) was used to generate multi-pollutant exposure profiles. Negative binomial regression analysis was used to examine the association between air pollutants and lung cancer incidence. The districts with higher concentrations for all pollutants were mostly in downtown and around the railway station. Districts with a higher concentration for NOx (IRR = 1.05, for each 10 unit increase in air pollutant), benzene (IRR = 3.86), toluene (IRR = 1.50), ethylbenzene (IRR = 5.16), p-xylene (IRR = 9.41), o-xylene (IRR = 7.93), m-xylene (IRR = 2.63) and TBTEX (IRR = 1.21) were significantly associated with higher lung cancer incidence. Districts with a higher multiple air-pollution profile were also associated with more lung cancer incidence (IRR = 1.01). Our study shows a positive association between air pollution and lung cancer incidence. This association was stronger for, respectively, p-xylene, o-xylene, ethylbenzene, benzene, m-xylene and toluene.

Changes in Air Quality Associated with Mobility Trends and Meteorological Conditions during COVID-19 Lockdown in Northern England, UK

The COVID-19 pandemic triggered catastrophic impacts on human life, but at the same time demonstrated positive impacts on air quality. In this study, the impact of COVID-19 lockdown interventions on five major air pollutants during the pre-lockdown, lockdown, and post-lockdown periods is analysed in three urban areas in Northern England: Leeds, Sheffield, and Manchester. A Generalised Additive Model (GAM) was implemented to eliminate the effects of meteorological factors from air quality to understand the variations in air pollutant levels exclusively caused by reductions in emissions. Comparison of lockdown with pre-lockdown period exhibited noticeable reductions in concentrations of NO (56.68–74.16%), NO2 (18.06–47.15%), and NOx (35.81–56.52%) for measured data. However, PM10 and PM2.5 levels demonstrated positive gain during lockdown ranging from 21.96–62.00% and 36.24–80.31%, respectively. Comparison of lockdown period with the equivalent period in 2019 also showed reductions in air pollutant concentrations, ranging 43.31–69.75% for NO, 41.52–62.99% for NOx, 37.13–55.54% for NO2, 2.36–19.02% for PM10, and 29.93–40.26% for PM2.5. Back trajectory analysis was performed to show the air mass origin during the pre-lockdown and lockdown periods. Further, the analysis showed a positive association of mobility data with gaseous pollutants and a negative correlation with particulate matter.

Iranian population exposures to heavy metals, PAHs, and pesticides and their intake routes: a study protocol of a national population health survey

Cancer is the second leading cause of death in the world and the third leading cause of death in Iran. It has been proven that numerous cancer cases are caused by exposure to environmental pollutants. There is a public health concern regarding an increase in exposure to carcinogens across Iran through different sources (air, food, and water) and a lack of research to address this issue. This study aims to gather data on exposure to heavy metals, polycyclic aromatic hydrocarbons (PAHs), and pesticides and their intake routes during the implementation of a national population health survey. This is a cross-sectional study of environmental pollutants in Iran, with a stratified multi-stage random sampling method, which led to 660 nationally representative samples in 132 clusters in three sequential parts. The first will be questionnaires to obtain demographics, assets, food records, air quality, and food frequency. The second will be physical measurements, including anthropometric and body composition. The third will be lab assessments that measure 26 types of environmental pollutants (7 heavy metals, 16 PAHs, and 3 pesticides) in urine, inhaled air, and consumed food and water of the population under study using ICP-MS and GS-MS devices. The results of this study will inform policymakers and the general population regarding the level of threat and will provide evidence for the development of interventional and observatory plans on the reduction of exposures to these pollutants. It could also be used to develop local standards to control contaminants through the three exposure routes. This study protocol will obtain data needed for policymakers to set surveillance systems for these pollutants at the national and provincial level to address the public concerns regarding the contamination of food, air, and water.

Ambient air pollution, meteorology, and COVID-19 infection in Korea

The outbreak of novel pneumonia coronavirus disease has become a public health concern worldwide. Here, for the first time, the association between Korean meteorological factors and air pollutants and the COVID-19 infection was investigated. Data of air pollutants, meteorological factors, and daily COVID-19 confirmed cases of seven metropolitan cities and nine provinces were obtained from 3 February 2020 to 5 May 2020 during the first wave of pandemic across Korea. We applied the generalized additive model to investigate the temporal relationship. There was a significantly nonlinear association between daily temperature and COVID-19 confirmed cases. Each 1°C increase in temperature was associated with 9% (lag 0-14; OR = 1.09; 95% CI = 1.03-1.15) increase of COVID-19 confirmed cases when the temperature was below 8°C. A 0.01 ppm increase in NO2 (lag 0-7, lag 0.14, and lag 0-21) was significantly associated with increases of COVID-19 confirmed cases, with ORs (95% CIs) of 1.13 (1.02-1.25), 1.19 (1.09-1.30), and 1.30 (1.19-1.41), respectively. A 0.1 ppm increase in CO (lag 0-21) was associated with the increase in COVID-19 confirmed cases (OR = 1.10, 95% CI = 1.04-1.16). There was a positive association between per 0.001 ppm of SO2 concentration (lag 0, lag 0-7, and lag 0-14) and COVID-19 confirmed cases, with ORs (95% CIs) of 1.13 (1.04-1.22), 1.20 (1.11-1.31), and 1.15 (1.07-1.25), respectively. There were significantly temporal associations between temperature, NO2 , CO, and SO2 concentrations and daily COVID-19 confirmed cases in Korea.

Association between short-term exposure to ambient particulate air pollution and biomarkers of oxidative stress: A meta-analysis

BACKGROUND

Exposure to ambient particulate air pollution contributes substantially to the mortality and morbidity due to cardiovascular diseases (CVD), respiratory diseases and neurodegenerative diseases. Several hypothetical mechanisms have been proposed to explain these associations, particularly oxidative stress. Malondialdehyde (MDA), 8-hydroxy-2'-deoxyguanosine (8-OHdG), and Superoxide Dismutase (SOD) are typical biomarkers of oxidative stress and have been frequently investigated. However, the association between exposure to ambient particulate matter (PM) and these biomarkers has not been well established.

OBJECTIVES

Evaluate the association between ambient particulate air pollution and biomarkers of oxidative stress based on existing epidemiological studies.

METHODS

A systematic literature search was conducted in databases of Science Direct, PubMed, Web of Science, and Scopus up to April 24, 2020 to summarize epidemiological studies reporting the association between exposure to ambient PM (PM_2.5, PM₁₀, or both) and biomarkers of oxidative stress, and a meta-analysis was performed for the associations reported in individual studies using a random-effect model.

RESULTS

This meta-analysis included 23 epidemiological studies (13 identified for 8-OHdG, 11 identified for MDA and 5 identified for SOD). A 10 μg/m³ increase in short-term exposure to ambient PM_2.5 was associated with pooled percent changes of 2.10% (95% CIs: -0.13%, 4.38%), 1.60% (95% CIs: 0.21%, 3.01%) and -0.61% (95% CIs: -1.92%, 0.72%) in 8-OHdG, MDA and SOD, respectively.

CONCLUSION

Short-term exposure to ambient PM_2.5 was associated with a significantly increased level of MDA, indicating that ambient particulate air pollution may contribute to increased oxidative stress.

Mortality rates due to respiratory tract diseases in Tehran, Iran during 2008-2018: a spatiotemporal, cross-sectional study

BACKGROUND

Tehran, the 22nd most populous city in the world, has the highest mortality rate due to respiratory system diseases (RSDs) in Iran. This study aimed to investigate spatiotemporal patterns of mortality due to these diseases in Tehran between 2008 and 2018.

METHODS

We used a dataset available from Tehran Municipality including all cases deceased due RSDs in this city between 2008 and 2018. Global Moran's I was performed to test whether the age-adjusted mortality rates were randomly distributed or had a spatial pattern. Furthermore, Anselin Local Moran's I was conducted to identify potential clusters and outliers.

RESULTS

During the 10-year study, 519,312 people died in Tehran, 43,177 because of RSDs, which corresponds to 831.1 per 10,000 deaths and 5.0 per 10,000 population. The death rate was much higher in men (56.8%) than in women (43.2%) and the highest occurred in the > 65 age group (71.2%). Overall, three diseases dominated the mortality data: respiratory failure (44.2%), pneumonia (15.9%) and lung cancer (10.2%). The rates were significantly higher in the central and southeastern parts of the city and lower in the western areas. It increased during the period 2008-2018 and showed a clustered spatial pattern between 2008 and 2013 but presented a random geographical pattern afterwards.

CONCLUSIONS

This study provides a first report of the spatial distribution of mortality due to RSDs in Tehran and shows a significant increase in respiratory disease mortality in the last ten years. Effective control of the excess fatality rates would warrant a combination of urban prevention and treatment strategies including environmental health plans.

Prenatal exposure to ambient air pollution and adverse pregnancy outcomes in Ahvaz, Iran: a generalized additive model

OBJECTIVE

There is some evidence about the short-term effects of air pollutants on adverse pregnancy outcomes. The aim of this study was to determine the association between air pollutants and spontaneous abortion, stillbirth, gestational hypertension, preeclampsia, gestational diabetes and macrosomia in Ahvaz, which is one of the most polluted cities in the Middle East.

METHODS

Data on adverse pregnancy outcomes and air pollutants including ozone (O₃), nitric oxide (NO), nitrogen dioxide (NO₂), sulfur dioxide (SO₂), carbon monoxide (CO), particles with a diameter of less than 10 µm (PM₁₀) and particles with a diameter less than 2.5 µm (PM_2.5) were inquired from the Health Department of Ahvaz Jundishapur University of Medical Sciences and the Environmental Protection Agency of Khuzestan Province for the years 2008-2018. A time series analysis using the generalized additive model (GAM) with up to 6-day lags was used.

RESULTS

The results showed that the SO₂ pollutant on 0, 1, 3, 4, and 6-day lags and PM₁₀ on lag 0 had direct and significant associations with spontaneous abortion. NO, NO₂ and CO on 0-6-day lags, and O₃ on 6-day lags showed direct and significant associations with preeclampsia. NO and NO₂ pollutants showed significant and direct associations with gestational diabetes, during 0- and 6-day lags. NO on 0-, 3- and 4-day lags, CO in all 0-6-day lags and PM_2.5 on 1-, 3-, 5-, and 6-day lags showed direct and significant associations with macrosomia. None of the pollutants showed significant associations with stillbirth or gestational hypertension.

CONCLUSIONS

The results of this study suggest that some air pollutants are associated with spontaneous abortion, preeclampsia, gestational diabetes and macrosomia. This study further emphasizes the need to control ambient air pollution.

The simultaneous effects of thermal stress and air pollution on body temperature of Tehran traffic officers

PURPOSE

Global warming and air pollution are among the most important problems all over the world. Considering the key role of traffic officers who saliently deal with traffic management and are in full, constant and direct exposure to thermal stress and air pollution index, this study aims to investigate the simultaneous effects of these factors on the body temperature of traffic officers in the main squares of Tehran.

METHODS

This study was conducted among 119 traffic officers who were working in 29 squares of Tehran, located near the active pollutant's stations during 2017. Samples were selected by the census method. Environmental parameters such as air temperature (dry and wet), radiation temperature, the level of air pollution in the main squares and characteristics of officers such as body temperature and the Wet-Bulb-Globe-Temperature (WBGT) index were evaluated. Data were analyzed through independent samples t-test and factorial ANOVA with a p value of p ≤ 0.05 in SPSS software.

RESULTS

There was no significant relationship between air pollution and ear temperature, but there was a statistically significant difference between the wet-bulb temperature and the ear temperature (t = 26.4, P < 0.001). The interaction effect of air pollution and wet-bulb temperature on the ear temperature was also significant (F = 3.98, P = 0.048).

CONCLUSION

Exposure to heat and air pollution affects body temperature, with its greatest impact on the temperature of the ear. More studies are recommended to be conducted in these field and other factors such as demographic and environmental factors at different times of the year should be investigated. Accordingly, some interventions should be implemented to reduce the vulnerability of officers based on the findings of the research.

Short-Term Associations of Fine Particulate Matter and Synoptic Weather Types with Cardiovascular Mortality: An Ecological Time-Series Study in Shanghai, China

Background: Exposures to both ambient fine particulate matter (PM_2.5) and extreme weather conditions have been associated with cardiovascular disease (CVD) deaths in numerous epidemiologic studies. However, evidence on the associations with CVD deaths for interaction effects between PM_2.5 and weather conditions is still limited. This study aimed to investigate associations of exposures to PM_2.5 and weather conditions with cardiovascular mortality, and further to investigate the synergistic or antagonistic effects of ambient air pollutants and synoptic weather types (SWTs). Methods: Information on daily CVD deaths, air pollution, and meteorological conditions between 1 January 2012 and 31 December 2014 was obtained in Shanghai, China. Generalized additive models were used to assess the associations of daily PM_2.5 concentrations and meteorological factors with CVD deaths. A 15-day lag analysis was conducted using a polynomial distributed lag model to access the lag patterns for associations with PM_2.5. Results: During the study period, the total number of CVD deaths in Shanghai was 59,486, with a daily mean of 54.3 deaths. The average daily PM_2.5 concentration was 55.0 µg/m³. Each 10 µg/m³ increase in PM_2.5 concentration was associated with a 1.26% (95% confidence interval (CI): 0.40%, 2.12%) increase in CVD mortality. No SWT was statistically significantly associated with CVD deaths. For the interaction between PM_2.5 and SWT, statistically significant interactions were found between PM_2.5 and cold weather, with risk for PM_2.5 in cold dry SWT decreasing by 1.47% (95% CI: 0.54%, 2.39%), and in cold humid SWT the risk decreased by 1.45% (95% CI: 0.52%, 2.36%). In the lag effect analysis, statistically significant positive associations were found for PM_2.5 in the 1-3 lag days, while no statistically significant effects were found for other lag day periods. Conclusions: Exposure to PM_2.5 was associated with short-term increased risk of cardiovascular deaths with some lag effects, while the cold weather may have an antagonistic effect with PM_2.5. However, the ecological study design limited the possibility to identify a causal relationship, so prospective studies with individual level data are warranted.

Spatial hazard assessment of the PM10 using machine learning models in Barcelona, Spain

Air pollution, and especially atmospheric particulate matter (PM), has a profound impact on human mortality and morbidity, environment, and ecological system. Accordingly, it is very relevant predicting air quality. Although the application of the machine learning (ML) models for predicting air quality parameters, such as PM concentrations, has been evaluated in previous studies, those on the spatial hazard modeling of them are very limited. Due to the high potential of the ML models, the spatial modeling of PM can help managers to identify the pollution hotspots. Accordingly, this study aims at developing new ML models, such as Random Forest (RF), Bagged Classification and Regression Trees (Bagged CART), and Mixture Discriminate Analysis (MDA) for the hazard prediction of PM10 (particles with a diameter less than 10 µm) in the Barcelona Province, Spain. According to the annual PM10 concentration in 75 stations, the healthy and unhealthy locations are determined, and a ratio 70/30 (53/22 stations) is applied for calibrating and validating the ML models to predict the most hazardous areas for PM10. In order to identify the influential variables of PM modeling, the simulated annealing (SA) feature selection method is used. Seven features, among the thirteen features, are selected as critical features. According to the results, all the three-machine learning (ML) models achieve an excellent performance (Accuracy > 87% and precision > 86%). However, the Bagged CART and RF models have the same performance and higher than the MDA model. Spatial hazard maps predicted by the three models indicate that the high hazardous areas are located in the middle of the Barcelona Province more than in the Barcelona's Metropolitan Area.

Short-term effects of particulate matter during desert and non-desert dust days on mortality in Iran

BACKGROUND

Increased atmospheric particulate matter (PM) concentrations are commonly observed during desert dust days in Iran, but there is still no evidence of their effects on human health. We aimed to evaluate the association between daily mortality and exposure to PM₁₀ and PM_2.5 during dust and non-dust days in Tehran and Ahvaz, two major Middle Eastern cities with different sources, intensity, and frequency of desert dust days.

METHODS

We identified desert dust days based on exceeding a daily PM₁₀ concentration threshold of 150 µg/m³ between 2014 and 2017, checking for low PM_2.5/PM₁₀ ratio typical of dust days. We used a time-stratified case-crossover design to estimate the short-term effects of PM₁₀ and PM_2.5 concentrations on daily mortality during dust and non-dust days. Data was analyzed using conditional Poisson regression models.

RESULTS

Higher concentrations of PM and frequency of desert dust days were observed in Ahvaz rather than Tehran. In Ahvaz, the effect of PM₁₀ at lag 0 was much higher during dust days, an increment of 10 μg/m³ was associated with 3.28% (95%CI = [2.42, 4.15]) increase of daily mortality, than non-dust days, 1.03% (95%CI = [-0.02, 2.08]), while in Tehran, was slightly higher during non-dust days, 0.72% (95%CI = [0.23, 1.23]), than in dust days, 0.49% (95%CI = [-0.22, 1.20]). No statistically significant associations were observed between PM_2.5 and daily mortality in Ahvaz, while in Teheran the effect of PM_2.5 increased significantly during non-dust days at lag 2, 1.89% (95%CI = [0.83, 1.2.95] and lag 3, 1.88% (95%CI = [0.83, 1.2.95]).

CONCLUSION

The study provides evidence that exposure to PM during Middle East dust days is an important risk factor to human health in arid regions and areas affected by desert dust events.

Risk of temperature, humidity and concentrations of air pollutants on the hospitalization of AECOPD

AIM

To investigate the effect of temperature, humidity and the concentration of ambient air pollution on the hospitalization of AECOPD.

METHOD

Hospitalization record was obtained from Shenyang Medical Insurance Bureau, concluding patient's age, gender, income hospital time, outcome hospital; Generalized additive model was used to analyze the relationship between temperature, humidity, the concentration of ambient air pollution and the hospitalization of AECOPD.

RESULT

The effect of ozone on admission rate in male group was higher than that in female group. Ambient air pollution had a weak influence on age≤50 group. It was found that the optimal lag day for daily relative 40 humidity to age≤50 group, 50<age≤60, 60<age≤70 group and age>70 group was on lag5, lag4, lag4 and lag5, respectively.

CONCLUSION

Air pollution, relative humidity and temperature can increase the risk of admission for acute exacerbation of COPD, and in this process there was a lag effect.

Generalized additive models: Building evidence of air pollution, climate change and human health

Advances in statistical analysis in the last few decades in the area of linear models enhanced the capability of researchers to study environmental procedures. In relation to general linear models; generalized linear models (GLM) provide greater flexibility in analyzing data related to non-normal distributions. Considering this, the current review explains various applications of the generalized additive model (GAM) to link air pollution, climatic variability with adverse health outcomes. The review examines the application of GAM within the varied field, focusing on the environment and meteorological data. Further, advantages and complications of applying GAM to environmental data are also discussed. Application of GAM allowed for specification for the error pattern and found to be an appropriate fit for the data sets having non-normal distributions; this results in lower and more reliable p-values. Since most environmental data is non-normal, GAM provides a more effective analytical method than traditional linear models. This review highlights on ambient air pollutants, climate change, and health by evaluating studies related to GAM. Additionally, an insight into the application of GAM in R software is provided, which is open source software with the extensive application for any type of dataset.

The relationship between air quality and respiratory pathogens among children in Suzhou City

OBJECTIVE

We studied the short-term effects of air pollutant concentrations in Suzhou City on respiratory infections in children of different age groups.

METHODS

We employed clinical data from children hospitalized with respiratory infections at the Children's Hospital of Soochow University during 2014-2016, and air quality for Suzhou City covering the same period.We investigated the relationships between the air pollutant concentrations and respiratory tract infections in children by causative pathogen using time series models with lagged effects.

RESULTS

The results of single-pollutant models showed that PM2.5, PM10, NO2, SO2 and CO had statistically significant associations with respiratory tract infections in children under 3 years, with the largest effect sizes at a lag of 3 weeks. Notably, the multi-pollutant model found PM2.5 was significantly associated with viral respiratory in children under 7 months, and bacterial respiratory infections in other age groups, while PM10 concentrations were associated with viral infections in preschool children.

CONCLUSION

PM2.5, PM10 and NO2 are the main atmospheric pollutants in Suzhou. The associations between pollutant concentrations and viral and bacterial respiratory infections were stronger among children under 3 years than for older age group.s PM2.5 had the strongest influence on viral and Mycoplasma pneumoniae respiratory infections when multiple pollutants were tested together.

PM2.5 Prediction Based on Random Forest, XGBoost, and Deep Learning Using Multisource Remote Sensing Data

In recent years, air pollution has become an important public health concern. The high concentration of fine particulate matter with diameter less than 2.5 µm (PM2.5) is known to be associated with lung cancer, cardiovascular disease, respiratory disease, and metabolic disease. Predicting PM2.5 concentrations can help governments warn people at high risk, thus mitigating the complications. Although attempts have been made to predict PM2.5 concentrations, the factors influencing PM2.5 prediction have not been investigated. In this work, we study feature importance for PM2.5 prediction in Tehran’s urban area, implementing random forest, extreme gradient boosting, and deep learning machine learning (ML) approaches. We use 23 features, including satellite and meteorological data, ground-measured PM2.5, and geographical data, in the modeling. The best model performance obtained was R2 = 0.81 (R = 0.9), MAE = 9.93 µg/m3, and RMSE = 13.58 µg/m3 using the XGBoost approach, incorporating elimination of unimportant features. However, all three ML methods performed similarly and R2 varied from 0.63 to 0.67, when Aerosol Optical Depth (AOD) at 3 km resolution was included, and 0.77 to 0.81, when AOD at 3 km resolution was excluded. Contrary to the PM2.5 lag data, satellite-derived AODs did not improve model performance.