Apheis: Health impact assessment of long-term exposure to PM(2.5) in 23 European cities

Characteristics of number concentration, size distribution and components of particulate matter emitted from a typical large civil airport

Civil airports are recognized as significant contributors to fine particulate matter, especially ultra-fine particulate matter (UFP). The pollutants from airport activities have a notable adverse impact on global climate, urban air quality, and public health. However, there is a lack of practical observational studies on the characterization of integrated pollutant emissions from large civil airports. This study aims to focus on the combined emission characteristics of particulate number concentration (PNC), size distribution, and components at a large civil airport, especially UFP. The findings reveal that airport activities significantly contribute to elevated PNC levels during aircraft activity in downwind conditions (four times higher than background levels) and upwind conditions (7.5 times higher). UFP dominates the PNC around the airport. The particle size distribution shows two peaks occurring around 10-30 nm and 60-80 nm. Notably, particles within the ranges of 17-29 nm and 57-101 nm account for 65.9 % and 12.0 % of the total PNC respectively. Aircraft landing has the greatest impact on particles sized between 6 and 17 nm while takeoff affects particles sized between 29 and 57 nm resulting in a respective increase in PNC by factors of approximately 3.27 and 35.4-fold increase compared to background levels. Different aircraft types exhibit varying effects on PNC with A320 and A321 showing more pronounced effects during takeoff and landing.The presence of airports leads to roughly five-fold rise in elemental component concentrations with Si being highest followed by OC, Ca, Al, Fe, Ca2+, EC, and Mg2+. The OC/EC ratio under high aircraft activity in downwind conditions falls within range of approximately 2.5-3.5. These characteristic components and ratio can be considered as identifying species for civil airports. PMF model show about 75 % of the particulate emissions at the airport boundary were related to airport activities.

Health benefits of reducing ambient levels of fine particulate matter: a mortality impact assessment in Taiwan

While numerous studies have found a relationship between long-term exposure to airborne fine particulate matter (PM2.5) and higher risk of death, few investigations examined the contribution that a reduction of exposure to ambient PM2.5 levels might exert on mortality rates. This study aimed to collect data on changes in annual average ambient levels of PM2.5 from 2006 to 2020 and consequent health impact in public health in 65 municipalities in Taiwan. Avoidable premature mortality was used here as an indicator of adverse health impact or health benefits. Annual PM2.5 levels were averaged for the years 2006, 2010, and 2020. In accordance with World Health Organization (WHO) methodology, differences were estimated in the number of deaths attributed to ambient PM2.5 exposure which were derived from concentration-response data from prior epidemiological studies. PM2.5 concentrations were found to have been decreased markedly throughout Taiwan over the two-decade study. As the PM2.5 concentrations fell, so was the health burden as evidenced by number of deaths concomitantly reduced from 22.4% in 2006 to 8.47% in 2020. Data demonstrated that reducing annual mean levels of PM2.5 to PM10 ug/m3 was associated with decrease in the total burden of mortality, with a 2.22-13.18% fall in estimated number of PM2.5-related deaths between 2006 and 2020. Based upon these results, these declines in ambient PM2.5 levels were correlated with significant improvement in public health (health benefits) and diminished number of deaths in Taiwan.

Air Pollution: A Silent Key Driver of Dementia

In 2017, the Lancet Commission on Dementia Prevention, Intervention, and Care included air pollution in its list of potential risk factors for dementia; in 2018, the Lancet Commission on Pollution concluded that the evidence for a causal relationship between fine particulate matter (PM) and dementia is encouraging. However, few interventions exist to delay or prevent the onset of dementia. Air quality data are becoming increasingly available, and the science underlying the associated health effects is also evolving rapidly. Recent interest in this area has led to the publication of population-based cohort studies, but these studies have used different approaches to identify cases of dementia. The purpose of this article is to review recent evidence describing the association between exposure to air pollution and dementia with special emphasis on fine particulate matter of 2.5 microns or less. We also summarize here the proposed detailed mechanisms by which air pollutants reach the brain and activate the innate immune response. In addition, the article also provides a short overview of existing limitations in the treatment of dementia.

Impact of lifetime air pollution exposure patterns on the risk of chronic disease

Long-term exposure to air pollution can lead to cardiovascular disease, metabolic syndrome, and chronic respiratory disease. However, from a lifetime perspective, the critical period of air pollution exposure in terms of health risk is unknown. This study aimed to evaluate the impact of air pollution exposure at different life stages. The study participants were recruited from community centers in Northern Taiwan between October 2018 and April 2021. Their annual averages for fine particulate matter (PM2.5) exposure were derived from a national visibility database. Lifetime PM2.5 exposures were determined using residential address information and were separated into three stages (<20, 20-40, and >40 years). We employed exponentially weighted moving averages, applying different weights to the aforementioned life stages to simulate various weighting distribution patterns. Regression models were implemented to examine associations between weighting distributions and disease risk. We applied a random forest model to compare the relative importance of the three exposure life stages. We also compared model performance by evaluating the accuracy and F1 scores (the harmonic mean of precision and recall) of late-stage (>40 years) and lifetime exposure models. Models with 89% weighting on late-stage exposure showed significant associations between PM_2.5 exposure and metabolic syndrome, hypertension, diabetes, and cardiovascular disease, but not gout or osteoarthritis. Lifetime exposure models showed higher precision, accuracy, and F1 scores for metabolic syndrome, hypertension, diabetes, and cardiovascular disease, whereas late-stage models showed lower performance metrics for these outcomes. We conclude that exposure to high-level PM_2.5 after 40 years of age may increase the risk of metabolic syndrome, hypertension, diabetes, and cardiovascular disease. However, models considering lifetime exposure showed higher precision, accuracy, and F1 scores and lower equal error rates than models incorporating only late-stage exposures. Future studies regarding long-term air pollution modelling are required considering lifelong exposure pattern. .¹.

Exposure to Air Pollution and Emergency Department Visits During the First Year of Life Among Preterm and Full-term Infants

IMPORTANCE

Previous studies have focused on exposure to fine particulate matter 2.5 μm or less in diameter (PM2.5) and on birth outcome risks; however, few studies have evaluated the health consequences of PM2.5 exposure on infants during their first year of life and whether prematurity could exacerbate such risks.

OBJECTIVE

To assess the association of PM2.5 exposure with emergency department (ED) visits during the first year of life and determine whether preterm birth status modifies the association.

DESIGN, SETTING, AND PARTICIPANTS

This individual-level cohort study used data from the Study of Outcomes in Mothers and Infants cohort, which includes all live-born, singleton deliveries in California. Data from infants' health records through their first birthday were included. Participants included 2 175 180 infants born between 2014 and 2018, and complete data were included for an analytic sample of 1 983 700 (91.2%). Analysis was conducted from October 2021 to September 2022.

EXPOSURES

Weekly PM2.5 exposure at the residential ZIP code at birth was estimated from an ensemble model combining multiple machine learning algorithms and several potentially associated variables.

MAIN OUTCOMES AND MEASURES

Main outcomes included the first all-cause ED visit and the first infection- and respiratory-related visits separately. Hypotheses were generated after data collection and prior to analysis. Pooled logistic regression models with a discrete time approach assessed PM2.5 exposure and time to ED visits during each week of the first year of life and across the entire year. Preterm birth status, sex, and payment type for delivery were assessed as effect modifiers.

RESULTS

Of the 1 983 700 infants, 979 038 (49.4%) were female, 966 349 (48.7%) were Hispanic, and 142 081 (7.2%) were preterm. Across the first year of life, the odds of an ED visit for any cause were greater among both preterm (AOR, 1.056; 95% CI, 1.048-1.064) and full-term (AOR, 1.051; 95% CI, 1.049-1.053) infants for each 5-μg/m3 increase in exposure to PM2.5. Elevated odds were also observed for infection-related ED visit (preterm: AOR, 1.035; 95% CI, 1.001-1.069; full-term: AOR, 1.053; 95% CI, 1.044-1.062) and first respiratory-related ED visit (preterm: AOR, 1.080; 95% CI, 1.067-1.093; full-term: AOR,1.065; 95% CI, 1.061-1.069). For both preterm and full-term infants, ages 18 to 23 weeks were associated with the greatest odds of all-cause ED visits (AORs ranged from 1.034; 95% CI, 0.976-1.094 to 1.077; 95% CI, 1.022-1.135).

CONCLUSIONS AND RELEVANCE

Increasing PM2.5 exposure was associated with an increased ED visit risk for both preterm and full-term infants during the first year of life, which may have implications for interventions aimed at minimizing air pollution.

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.

Trends, Issues and Future Directions of Urban Health Impact Assessment Research: A Systematic Review and Bibliometric Analysis

Health impact assessment (HIA) has been regarded as an important means and tool for urban planning to promote public health and further promote the integration of health concept. This paper aimed to help scientifically to understand the current situation of urban HIA research, analyze its discipline co-occurrence, publication characteristics, partnership, influence, keyword co-occurrence, co-citation, and structural variation. Based on the ISI Web database, this paper used a bibliometric method to analyze 2215 articles related to urban HIA published from 2012 to 2021. We found that the main research directions in the field were Environmental Sciences and Public Environmental Occupational Health; China contributed most articles, the Tehran University of Medical Sciences was the most influential institution, Science of the Total Environment was the most influential journal, Yousefi M was the most influential author. The main hotspots include health risk assessment, source appointment, contamination, exposure, particulate matter, heavy metals and urban soils in 2012–2021; road dust, source apposition, polycyclic aromatic hydrocarbons, air pollution, urban topsoil and the north China plain were always hot research topics in 2012–2021, drinking water and water quality became research topics of great concern in 2017–2021. There were 25 articles with strong transformation potential during 2020–2021, but most papers carried out research on the health risk assessment of toxic elements in soil and dust. Finally, we also discussed the limitations of this paper and the direction of bibliometric analysis of urban HIA in the future.

Health impacts of air pollution exposure from 1990 to 2019 in 43 European countries

Air pollution is the fourth greatest overall risk factor for human health. Despite declining levels in Europe, air pollution still represents a major health and economic burden. We collected data from the Global Burden of Disease Study 2019 regarding overall, as well as ischemic heart disease (IHD), stroke, and tracheal, bronchus and lung cancer-specific disability adjusted life years (DALYs), years of life lost (YLL) and mortality attributable to air pollution for 43 European countries between 1990 and 2019. Concentrations of ambient particulate matter (aPM_2.5), ozone, and household air pollution from solid fuels were obtained from State of Global Air 2020. We analysed changes in air pollution parameters, as well as DALYs, YLL, and mortality related to air pollution, also taking into account gross national income (GNI) and socio-demographic index (SDI). Using a novel calculation, aPM_2.5 ratio (PMR) change and DALY rate ratio (DARR) change were used to assess each country's ability to decrease its aPM_2.5 pollution and DALYs to at least the extent of the European median decrease within the analysed period. Finally, we created a multiple regression model for reliably predicting YLL using aPM_2.5 and household air pollution. The average annual population-weighted aPM_2.5 exposure in Europe in 1990 was 20.8 μg/m³ (95% confidence interval (CI) 18.3-23.2), while in 2019 it was 33.7% lower at 13.8 μg/m³ (95% CI 12.0-15.6). There were in total 368 006 estimated deaths in Europe in 2019 attributable to air pollution, a 42.4% decrease compared to 639 052 in 1990. The majority (90.4%) of all deaths were associated with aPM_2.5. IHD was the primary cause of death making up 44.6% of all deaths attributable to air pollution. The age-standardised DALY rate and YLL rate attributable to air pollution were more than 60% lower in 2019 compared to 1990. There was a strong positive correlation (r = 0.911) between YLL rate and aPM_2.5 pollution in 2019 in Europe. Our multiple regression model predicts that for 10% increase in aPM_2.5, YLL increases by 16.7%. Furthermore, 26 of 43 European countries had a positive DARR change. 31 of 43 European countries had a negative PMR change, thus not keeping up with the European median aPM_2.5 concentration decrease. When categorising countries by SDI and GNI, countries in the higher brackets had significantly lower aPM_2.5 concentration and DALY rate for IHD and stroke. Overall, air pollution levels, air pollution-related morbidity and mortality have decreased considerably in Europe in the last three decades. However, with the growing European population, air pollution remains an important public health and economic issue. Policies targeting air pollution reduction should continue to be strongly enforced to further reduce one of the greatest risk factors for human health.

Forecasting PM2.5 concentration using artificial neural network and its health effects in Ahvaz, Iran

The main objective of the present study was to predict the associated health endpoint of PM_2.5 using an artificial neural network (ANN). The neural network used in this work contains a hidden layer with 27 neurons, an input layer with 8 parameters, and an output layer. First, the artificial neural network was implemented with 80% of data for training then with 90% of data for training. The value of R for the data validation of these two networks was 0.80 and 0.83 respectively. The World Health Organization AirQ ⁺ software was utilized for assessing Health effects of PM_2.5 levels. The mean PM_2.5 over the 9-year study period was 63.27(μg/m³), about six times higher than the WHO guideline. However, the PM_2.5 concentration in the last year decreased by about 25% compared to the first year, which is statistically significant (P-value = 0.0048). This reduced pollutant concentration led to a decrease in the number of deaths from 1785 in 2008 to 1059 in 2016. Moreover, a positive correlation was found between PM_2.5 concentration and temperature and wind speed. Considering the importance of predicting PM_2.5 concentration for accurate and timely decisions as well as the accuracy of the artificial neural network used in this study, the artificial neural network can be utilized as an effective instrument to reduce health and economic effects.

Spatiotemporal Analysis of Haze in Beijing Based on the Multi-Convolution Model

As a kind of air pollution, haze has complex temporal and spatial characteristics. From the perspective of time, haze has different causes and levels of pollution in different seasons. From the perspective of space, the concentration of haze in adjacent areas will affect each other, showing some correlation. In this paper, we construct a multi-convolution haze-level prediction model for predicting haze levels in different areas of Beijing, which uses the remote sensing satellite image of the Beijing divided into nine regions as input and the haze pollution level as output. We categorize the predictions into four seasons in chronological order and use frequency histograms to analyze haze levels in different regions in different seasons. The results show that the haze pollution in the southern regions is significantly different from that in the northern regions. In addition, the haze tends to be clustered in adjacent areas. We use Global Moran’s I to analyze the predictions and find that haze is related to the geographical location in summer and autumn. We also use Local Moran’s I, Moran scatter plot, and Local Indicators of Spatial Association (LISA) to study the spatial characteristics of haze in adjacent areas. The results show, for the spatial distribution of haze in Beijing, that the southern regions present a high-high agglomeration, while the northern regions exhibit a ‘low-low agglomeration. The temporal evolution of haze on the seasonal scale, according to the chronological order of winter, spring, and summer to autumn, shows that the haze gradually becomes agglomerated. The main finding is that the haze pollution in southern Beijing is significantly different from that of northern regions, and haze tends to be clustered in adjacent areas.

Effectual removal of indoor ultrafine PM using submicron water droplets

Exposure to ultrafine airborne particulate matter (PM_1.0) poses a significant risk to human health and well-being. Examining the effect of submicron water droplets on the removal of ultrafine PM is timely and important for mitigating indoor ultrafine PM, which is difficult to filter out from incoming air. In this study, submicron water droplets were made by using a nanoporous membrane and an ultrasonic module of a commercial household ultrasonic humidifier (UH) for effectual ultrafine PM removal. The effect of water droplet size on indoor PM removal was experimentally investigated. Variations in the normalized PM concentration, removal efficiency and deposition constants were evaluated by analyzing the temporal variation in PM concentration inside a test chamber. The measured PM deposition constants were compared with the results of other previous studies. As a result, submicron water droplets of 800 nm in mean diameter were generated by ultrasonic module combined passive nanoporous membrane, and PM_1.0 concentration decreased by 30% in the initial 30 min. Compared with micron-sized water droplets, PM_1.0 removal efficiency improved by approximately two times higher. Moreover, the substitution of the experimental results into a theoretical model ascertained that PM collection efficiency is increased by approximately 10³ levels as the size of water droplets decreases. These results would be utilized in the development and implementation of effective strategies for indoor PM removal.

Spatial patterns and temporal variations of traffic-related air pollutants and estimating its health effects in Isfahan city, Iran

Isfahan as an important industrial city has faced with air pollution recently. Thus, we assessed the spatial and temporal trends of ambient PM_2.5, CO, SO₂, and O₃ and for estimating their health effect on Isfahan citizens between March 2018 and March 2019 through the AirQ+ software. Our results showed that citizens of Isfahan in almost 240, 167, and 134 of the days in the year has exposure to PM_2.5, SO₂, and O₃ higher than the WHO daily guideline, respectively. Daily variations of PM_2.5, CO, and SO₂ concentration showed the increasing trend of pollutants in the morning to evening. The maximum concentrations of O₃ were observed in the noonday. Also, the concentrations of these pollutants on Friday due to the holiday effect were higher than the weekdays. Except for O₃, the PM_2.5, CO, and SO₂ concentrations in the cold months and cold seasons was higher compared with the hot months and hot seasons. The total number of deaths because of lung cancer, natural mortality, ischemic heart disease, chronic obstructive pulmonary disease,, stroke associated with ambient PM_2.5 with the attributable proportion (AP) 11.43%, 11.63%, 15.96%, 15.15%, and 13.1% (95% CI) were 683, 19, 2, 202, and 55 cases, respectively. Therefore, the present study provides additional data for the provincial managers and politicians useful in planning proper strategies of air pollution control to decrease exposure and attributable mortalities.

Health effects of particulate matter in major Indian cities

Background: Particulate matter (PM) is one among the crucial air pollutants and has the potential to cause a wide range of health effects. Indian cities ranked top places in the World Health Organization list of most polluted cities by PM. Objectives: Present study aims to assess the trends, short- and long-term health effects of PM in major Indian cities. Methods: PM-induced hospital admissions and mortality are quantified using AirQ+ software. Results: Annual PM concentration in most of the cities is higher than the National Ambient Air Quality Standards of India. Trend analysis showed peak PM concentration during post-monsoon and winter seasons. The respiratory and cardiovascular hospital admissions in the male (female) population are estimated to be 31,307 (28,009) and 5460 (4882) cases, respectively. PM_2.5 has accounted for a total of 1,27,014 deaths in 2017. Conclusion: Cities with high PM concentration and exposed population are more susceptible to mortality and hospital admissions.

Response of the Cardiac Autonomic Control to Exposure to Nanoparticles and Noise: A Cross-Sectional Study of Airport Ground Staff

Airport activity causes the emission of particulate matter and noise, two environmental contaminants and potential health hazards, particularly for the personnel operating nearby taxiways. We explored the association between exposure to fine/ultrafine particles (UFPs) and noise with heart rate variability (HRV), an early indicator of cardiovascular autonomic response, among a sample of airport ground staff. Between May and June 2018, thirty-four male operators (mean age = 43 years and SD = 6.7) underwent personal monitoring of exposure to nanoparticles and noise, and HRV during their work activity. We conducted univariate and multivariate analysis to test the effect of UFP and noise exposure HRV. Total Lung Deposition Surface Area (LDSA) was significantly associated with a decrease in HRV Total Power and Triangular index (β = -0.038 p = 0.016 and β = -7.8 × 10-5, p = 0.042, respectively). Noise peak level showed an opposite effect, which was significant for Total Power (β = 153.03, p = 0.027), and for Triangular index (β = 0.362, p = 0.035). Further investigation is warranted to clarify the effect of the concurrent exposure to UFPs and noise on early changes of cardiac autonomic regulation.

The relationship between air pollution and COVID-19-related deaths: An application to three French cities

Being heavily dependent to oil products (mainly gasoline and diesel), the French transport sector is the main emitter of Particulate Matter (PMs) whose critical levels induce harmful health effects for urban inhabitants. We selected three major French cities (Paris, Lyon, and Marseille) to investigate the relationship between the Coronavirus Disease 19 (COVID-19) outbreak and air pollution. Using Artificial Neural Networks (ANNs) experiments, we have determined the concentration of PM2.5 and PM10 linked to COVID-19-related deaths. Our focus is on the potential effects of Particulate Matter (PM) in spreading the epidemic. The underlying hypothesis is that a pre-determined particulate concentration can foster COVID-19 and make the respiratory system more susceptible to this infection. The empirical strategy used an innovative Machine Learning (ML) methodology. In particular, through the so-called cutting technique in ANNs, we found new threshold levels of PM2.5 and PM10 connected to COVID-19: 17.4 µg/m3 (PM2.5) and 29.6 µg/m3 (PM10) for Paris; 15.6 µg/m3 (PM2.5) and 20.6 µg/m3 (PM10) for Lyon; 14.3 µg/m3 (PM2.5) and 22.04 µg/m3 (PM10) for Marseille. Interestingly, all the threshold values identified by the ANNs are higher than the limits imposed by the European Parliament. Finally, a Causal Direction from Dependency (D2C) algorithm is applied to check the consistency of our findings.

Assessing economic losses of haze with uncertain probabilistic linguistic analytic hierarchy process

With the rapid development of economy and industrialization, environmental problems, especially haze pollution, are being more and more serious. When assessing the economic losses caused by haze, although the traditional quantitative method can show the amount of economic losses visually, there are also some inaccuracies in the calculation process. Based on the situation, we propose a new method called uncertain probabilistic linguistic analytic hierarchy process (UPL-AHP), which combines traditional analytic hierarchy process with uncertain probabilistic linguistic term sets to process decision information in complex problems. Firstly, we propose the concept of uncertain probabilistic linguistic comparison matrix. Then, a new approach is given to check and improve the consistency of an uncertain probabilistic linguistic comparison matrix. After that, we introduce the application of UPL-AHP in group decision making. Finally, the proposed method is used to analyze a practical case concerning the economic losses of haze. Some relevant policy recommendations are given based on the results.

Effects of air pollution particles (ultrafine and fine particulate matter) on mitochondrial function and oxidative stress - Implications for cardiovascular and neurodegenerative diseases

Environmental pollution is a major cause of global mortality and burden of disease. All chemical pollution forms together may be responsible for up to 12 million annual excess deaths as estimated by the Lancet Commission on pollution and health as well as the World Health Organization. Ambient air pollution by particulate matter (PM) and ozone was found to be associated with an all-cause mortality rate of up to 9 million in the year 2015, with the majority being of cerebro- and cardiovascular nature (e.g. stroke and ischemic heart disease). Recent evidence suggests that exposure to airborne particles and gases contributes to and accelerates neurodegenerative diseases. Especially, airborne toxic particles contribute to these adverse health effects. Whereas it is well established that air pollution in the form of PM may lead to dysregulation of neurohormonal stress pathways and may trigger inflammation as well as oxidative stress, leading to secondary damage of cardiovascular structures, the mechanistic impact of PM-induced mitochondrial damage and dysfunction is not well established. With the present review we will discuss similarities between mitochondrial damage and dysfunction observed in the development and progression of cardiovascular disease and neurodegeneration as well as those adverse mitochondrial pathomechanisms induced by airborne PM.

Premature Adult Mortality and Years of Life Lost Attributed to Long-Term Exposure to Ambient Particulate Matter Pollution and Potential for Mitigating Adverse Health Effects in Tuzla and Lukavac, Bosnia and Herzegovina

Ambient air pollution is one of eight global risk factors for deaths and accounts for 38.44 all causes death rates attributable to ambient PM pollution, while in Bosnia and Herzegovina, it is 58.37. We have estimated health endpoints and possible gains if two policy scenarios were implemented and air pollution reduction achieved. Real-world health and recorded PM pollution data for 2018 were used for assessing the health impacts and possible gains. Calculations were performed with WHO AirQ+ software against two scenarios with cut-off levels at country-legal values and WHO air quality recommendations. Ambient PM2.5 pollution is responsible for 16.20% and 22.77% of all-cause mortality among adults in Tuzla and Lukavac, respectively. Our data show that life expectancy could increase by 2.1 and 2.4 years for those cities. In the pollution hotspots, in reality, there is a wide gap in what is observed and the implementation of the legally binding air quality limit values and, thus, adverse health effects. Considerable health gains and life expectancy are possible if legal or health scenarios in polluted cities were achieved. This estimate might be useful in providing additional health burden evidence as a key component for a clean air policy and action plans.

Concentrations and mortality due to short- and long-term exposure to PM2.5 in a megacity of Iran (2014-2019)

The present study aimed to survey the spatial and temporal trends of ambient concentration of PM_2.5 and to estimate mortality attributed to short- and long-term exposure to PM_2.5 in Isfahan from March 2014 to March 2019 using the AirQ⁺ software. The hourly concentrations of PM_2.5 were obtained from the Isfahan Department of Environment and Isfahan Air Quality Monitoring Center. Then, the 24-h mean concentration of PM_2.5 for each station was calculated using the Excel software. According to the results, the annual mean concentration of PM_2.5 in 2014-2019 was 29.9-50.9 μg/m³, approximately 3-5 times higher than the WHO guideline (10 μg/m³). The data showed that people of Isfahan in almost 58% to 96% of the days of a year were exposed to PM_2.5 higher than the WHO daily guideline. The concentrations of PM_2.5 in cold months such as October, November, December and January were higher than those in the other months. The zoning of the annual concentrations of PM_2.5 in urban areas showed that the highest PM_2.5 concentrations were related to the northern, northwestern, southern and central areas of the city. On average, from 2014 to 2019, the number of deaths due to natural mortality, lung cancer (LC), chronic obstructive pulmonary disease (COPD), ischemic heart disease (IHD) and stroke associated with ambient PM_2.5 were 948, 16, 18, 281 and 60, respectively. The present study estimated that on average, 14.29% of the total mortality, 17.2% of lung cancer (LC), 15.54% of chronic obstructive pulmonary disease (COPD), 17.12% of ischemic heart disease (IHD) and 14.94% of stroke mortalities were related to long-term exposure to ambient PM_2.5. So provincial managers and politicians must adopt appropriate strategies to control air pollution and reduce the attributable health effects and economic losses.

Polymorphisms in DNA repair genes in lung cancer patients living in a coal-mining region

Air pollutants and ionizing radiation are well-known carcinogens involved in the pathogenesis of lung cancer, and residents of coal-mining regions are exposed routinely to these agents. Polymorphisms in DNA repair genes may be associated with an increased risk of malignant transformation. We investigated associations between the risk of lung cancer in residents of the coal-mining region and polymorphisms in the genes APEX1 (rs1130409), hOGG1 (rs1052133), XRCC1 (rs25489, rs25487), XRCC2 (rs3218536), XRCC3 (rs861539), ADPRT/PARP1 (rs1136410), XPD/ERCC2 (rs13181), XPG/ERCC5 (rs17655), XPC (rs2228001), ATM (rs1801516), and NBS1 (rs1805794). Three hundred and forty residents of the Kemerovo Region (a coal-mining region of western Siberia) were lung cancer patients exposed to air pollutants and ionizing radiation (case) and 335 were healthy donors (control). Genotyping was performed by real-time PCR and allele-specific PCR. We discovered that polymorphisms in the XPD gene in men [log-additive model: odds ratio (OR) = 1.64, 95% confidence interval (CI): 1.17-2.31], the ATM gene in women and nonsmokers (codominant model: OR = 0.11, 95% CI: 0.02-0.49 and OR = 0.25, 95% CI: 0.08-0.72, respectively), the APEX1 gene for smokers (recessive model: OR = 2.55, 95% CI: 1.34-4.85), and the NBS1 gene for those who work in the coal industry (overdominant model: OR = 0.40, 95% CI: 0.21-0.75) are associated with an increased risk of lung cancer. Using the multifactor dimensionality reduction method, we found a model of gene-gene interactions associated with the risk of lung cancer: NBS1 (rs1805794)-XRCC1 (rs25487)-hOGG1 (rs1052133)-XPG (rs17655). These results indicate an association between combinations of polymorphisms in the studied genes and the risk of lung cancer in residents of a coal-mining region.

Revealing the air pollution burden associated with internal Migration in Peru

This study aims to quantify changes in outdoor (ambient) air pollution exposure from different migration patterns within Peru and quantify its effect on premature mortality. Data on ambient fine particulate matter (PM2.5) was obtained from the National Aeronautics and Space Administration (NASA). Census data was used to calculate rates of within-country migration at the district level. We calculated differences in PM2.5 exposure between "current" (2016-2017) and "origin" (2012) districts for each migration patterns. Using an exposure-response relationship for PM2.5 extracted from a meta-analysis, and mortality rates from the Peruvian Ministry of Health, we quantified premature mortality attributable to each migration pattern. Changes in outdoor PM2.5 exposure were observed between 2012 and 2016 with highest levels of PM2.5 in the Department of Lima. A strong spatial autocorrelation of outdoor PM2.5 values (Moran's I = 0.847, p-value=0.001) was observed. In Greater Lima, rural-to-urban and urban-to-urban migrants experienced 10-fold increases in outdoor PM2.5 exposure in comparison with non-migrants. Changes in outdoor PM2.5 exposure due to migration drove 137.1 (95%CI: 93.2, 179.4) premature deaths related to air pollution, with rural-urban producing the highest risk of mortality from exposure to higher levels of ambient air pollution. Our results demonstrate that the rural-urban and urban-urban migrant groups have higher rates of air pollution-related deaths.

Cytotoxicity and toxicoproteomic analyses of human lung epithelial cells exposed to extracts of atmospheric particulate matters on PTFE filters using acetone and water

Differences of cytotoxicity associated with exposure to different extracts of atmospheric particulate matters (PMs) are still not well characterized by in vitro toxicoproteomics. In this study, in vitro cytotoxicity assays and toxicoproteomic analyses were carried out to investigate toxic effects of PM collected using polytetrafluoroethylene (PTFE) filters extracted with acetone for PM_2.1 and water for PM_2.1 and PM₁₀ on A549 human lung epithelial cells. The cytotoxicity assays based on cell viability, cell apoptosis and reactive oxygen species generation indicated that PM_2.1 extracted with acetone had the highest toxicity. iTRAQ labeling and LC-MS/MS analyses indicated that the number of differentially expressed proteins in A549 cells affected by PM_2.1 extracted with acetone was noticeably higher than that of the other two groups. Hierarchical cluster analyses showed that the influences of the extracts of PM_2.1 and PM₁₀ using water on the proteome of A549 cells were similar, whereas significantly different from the effect of PM_2.1 extracted with acetone. Pathways analyses indicated that PM_2.1 extracted with acetone influenced the expression of proteins involved in 14 pathways including glycolysis/gluconeogenesis, pentose phosphate pathway, proteasome, etc. PM_2.1 extracted with water affected the expression of proteins involved in 3 pathways including non-homologous end-joining, ribosome and endocytosis. However, PM₁₀ extracted with water affected the expression of proteins involved in only spliceosome pathway. The extracts of PM using different extractants to detach PM from PTFE filters influenced the cytotoxic effects of PM and the proteome of A549 cells. Therefore, extractants should be assessed carefully before the investigations on cytotoxicity to improve the compatibility of experimental results among research teams.

Assessment of German population exposure levels to PM10 based on multiple spatial-temporal data

Particulate matter is the key to increasing urban air pollution, and research into pollution exposure assessment is an important part of environmental health. In order to classify PM₁₀ air pollution and to investigate the population exposure to the distribution of PM₁₀, daily and monthly PM₁₀ concentrations of 379 air pollution monitoring stations were obtained for a period from 01/01/2017 to 31/12/2017. Firstly, PM₁₀ concentrations were classified using the head/tail break clustering algorithm to identify locations with elevated PM₁₀ levels. Subsequently, population exposure levels were calculated using population-weighted PM₁₀ concentrations. Finally, the power-law distribution was used to test the distribution of PM₁₀ polluted areas. Our results indicate that the head/tail break algorithm, with an appropriate segmentation threshold, can effectively identify areas with high PM₁₀ concentrations. The distribution of the population according to exposure level shows that the majority of people is living in polluted areas. The distribution of heavily PM₁₀ polluted areas in Germany follows the power-law distribution well, but their boundaries differ from the boundaries of administrative cities; some even cross several administrative cities. These classification results can guide policymakers in dividing the country into several areas for pollution control.

Exposure levels of air pollution (PM2.5) and associated health risk in Kuwait

It is well established that respiratory and cardiovascular mortality and morbidity rates are associated with poor air quality as measured by high concentrations of fine particulate matter such as PM_2.5 parameters. Since such information is lacking for the State of Kuwait, this study examined the exposure levels of PM_2.5 and the associated health risk as evaluated by five mortality measures embodied in ischemic heart disease, stroke, lung cancer, chronic obstructive pulmonary disease and acute lower respiratory infection as well as two morbidity outcomes related to both cardiovascular and respiratory diseases. The measurement models utilized in this investigation followed the WHO guidelines. Over a span of a four-year period (2014-2017), the annual PM_2.5 concentration levels ranged from 38.0 μg/m³ to 75.2 μg/m³. In general, exposure levels tended to fluctuate throughout the day with the higher levels recorded during rush hours (early morning and early evening), weekends (particularly Saturdays), and summer (i.e., August and September). The highest number of excess cases and attributable proportions of premature mortalities were related to ischemic heart disease and stroke at 352 (95% CI 275-426) and 70.8% (95% CI 39.7-85.2), respectively. In general, respiratory diseases showed a higher number of excess cases and attributable proportions than cardiovascular diseases. Relative to other findings on the global stage, the results emanating from Kuwait are emerging on the higher side. The study outcomes suggest that control strategies are in dire need to bend the pollution levels in Kuwait.

A model for estimating the lifelong exposure to PM2.5 and NO2 and the application to population studies

Numerous epidemiological studies have confirmed the negative influences of air pollutants on human health, where fine particles (PM2.5) and nitrogen dioxide (NO₂) cause the highest health risks. However, the traditional studies have only involved the ambient concentration for a short to medium time period, which ignores the influence of indoor sources, the individual time-activity pattern, and the fact that the health status is impacted by the long-term accumulated exposure. The aim of this paper is to develop a methodology to simulate the lifelong exposure (rather than outdoor concentration) to PM2.5 and NO₂ for individuals in Europe. This method is realized by developing a probabilistic model that integrates an outdoor air quality model, a model estimating indoor air pollution, an exposure model, and a life course trajectory model for predicting retrospectively the employment status. This approach has been applied to samples of two population studies in the frame of the European Commission FP7-ENVIRONMENT research project HEALS (Health and Environment-wide Associations based on Large Population Surveys), where socioeconomic data of the participants have been collected. Results show that the simulated exposures to both pollutants for the samples are influenced by socio-demographic characteristics, including age, gender, residential location, employment status and smoking habits. Both outdoor concentrations and indoor sources play an important role in the total exposure. Moreover, large variances have been observed among countries and cities. The application of this methodology provides valuable insights for the exposure modelling, as well as important input data for exploring the correlation between exposure and health impacts.

Calibration of Portable Particulate Matter-Monitoring Device using Web Query and Machine Learning

Background

Monitoring and control of PM_2.5 are being recognized as key to address health issues attributed to PM_2.5. Availability of low-cost PM_2.5 sensors made it possible to introduce a number of portable PM_2.5 monitors based on light scattering to the consumer market at an affordable price. Accuracy of light scattering–based PM_2.5 monitors significantly depends on the method of calibration. Static calibration curve is used as the most popular calibration method for low-cost PM_2.5 sensors particularly because of ease of application. Drawback in this approach is, however, the lack of accuracy.

Methods

This study discussed the calibration of a low-cost PM_2.5-monitoring device (PMD) to improve the accuracy and reliability for practical use. The proposed method is based on construction of the PM_2.5 sensor network using Message Queuing Telemetry Transport (MQTT) protocol and web query of reference measurement data available at government-authorized PM monitoring station (GAMS) in the republic of Korea. Four machine learning (ML) algorithms such as support vector machine, k-nearest neighbors, random forest, and extreme gradient boosting were used as regression models to calibrate the PMD measurements of PM_2.5. Performance of each ML algorithm was evaluated using stratified K-fold cross-validation, and a linear regression model was used as a reference.

Results

Based on the performance of ML algorithms used, regression of the output of the PMD to PM_2.5 concentrations data available from the GAMS through web query was effective. The extreme gradient boosting algorithm showed the best performance with a mean coefficient of determination (R²) of 0.78 and standard error of 5.0 μg/m³, corresponding to 8% increase in R² and 12% decrease in root mean square error in comparison with the linear regression model. Minimum 100 hours of calibration period was found required to calibrate the PMD to its full capacity. Calibration method proposed poses a limitation on the location of the PMD being in the vicinity of the GAMS. As the number of the PMD participating in the sensor network increases, however, calibrated PMDs can be used as reference devices to nearby PMDs that require calibration, forming a calibration chain through MQTT protocol.

Conclusions

Calibration of a low-cost PMD, which is based on construction of PM_2.5 sensor network using MQTT protocol and web query of reference measurement data available at a GAMS, significantly improves the accuracy and reliability of a PMD, thereby making practical use of the low-cost PMD possible.

Design and Analysis of Particulate Matter Air-Microfluidic Grading Chip Based on MEMS

Atmospheric particulate matter (PM) air-microfluidic grading chip is the premise for realizing high-precision PM online monitoring. It can be used as an indispensable basis for identifying pollution sources and controlling inhalable harmful substances. In this paper, based on aerodynamic theory and COMSOL numerical analysis, a two-stage PM air-microfluidic grading chip with cut-off diameters of 10 μm and 2.5 μm was designed. The effects of chip inlet width (W), main flow width (L), second channel width (S), and split ratio (Q₁/Q) on PM classification efficiency were analyzed, and optimized design parameters were achieved. The collection efficiency curves were plotted according to PM separation effects of the chip on various particle sizes (0.5–15 μm). The results indicate that the chip has good separation effect, which provides an efficient structural model for the PM micro-fluidization chip design.

Air Pollution Predicts Harsh Moral Judgment

The present research recruited participants from China, which is suffering from serious air pollution, and examined whether air pollution would be associated with moral judgment and immoral behavioral intention. Study 1 (n = 145) used the objective Air Quality Index to indicate the level of air pollution and found that it predicted harsh judgment on others’ moral violations but did not predict judgment on others’ non-moral negative behaviors or their own immoral behavioral intentions. Study 2 (n = 90) asked participants either to recall a past experience of being exposed to air pollution or to recall a neutral experience and consistently found that air pollution only influenced judgment on moral violations. The findings also ruled out the feeling of threat or the trust of government as possible mediators in the relationship between air pollution and harsh moral judgment.

Systematic Literature Review of Health Impact Assessments in Low and Middle-Income Countries

Health Impact Assessments (HIAs) motivate effective measures for safeguarding public health. There is consensus that HIAs in low and middle-income countries (LMICs) are lacking, but no study systematically focuses on those that have been successfully conducted across all regions of the world, nor do they highlight factors that may enable or hinder their implementation. Our objectives are to (1) systematically review, geographically map, and characterize HIA activity in LMICs; and (2) apply a process evaluation method to identify factors which are important to improve HIA implementation in LMICs. A systematic review of peer-reviewed HIAs in 156 LMICs was performed in Scopus, Medline, Web of Science, Sociological abstracts, and LILACs (Latin American and Caribbean Health Sciences) databases. The search used PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and covered HIAs across all type of interventions, topics, and health outcomes. HIAs were included if they reported a clear intervention and health outcome to be assessed. No time restriction was applied, and grey literature was not included. The eligible studies were subjected to six process evaluation criteria. The search yielded 3178 hits and 57 studies were retained. HIAs were conducted in 26 out of 156 countries. There was an unequal distribution of HIAs across regions and within LMICs countries. The leading topics of HIA in LMICs were air pollution, development projects, and urban transport planning. Most of the HIAs reported quantitative approaches (72%), focused on air pollution (46%), appraised policies (60%), and were conducted at the city level (36%). The process evaluation showed important variations in the way HIAs have been conducted and low uniformity in the reporting of six criteria. No study reported the time, money, and staff used to perform HIAs. Only 12% of HIAs were based on participatory approaches; 92% of HIAs considered multiple outcomes; and 61% of HIAs provided recommendations and fostered cross-national collaboration. The limited transparency in process, weak participation, and inconsistent delivery of recommendations were potential limitations to HIA implementation in low and middle-income countries. Scaling and improving HIA implementation in low and middle-income countries in the upcoming years will depend on expanding geographically by increasing HIA governance, adapting models and tools in quantitative methods, and adopting better reporting practices.

Estimated Long-Term (1981-2016) Concentrations of Ambient Fine Particulate Matter across North America from Chemical Transport Modeling, Satellite Remote Sensing, and Ground-Based Measurements

Accurate data concerning historical fine particulate matter (PM_2.5) concentrations are needed to assess long-term changes in exposure and associated health risks. We estimated historical PM_2.5 concentrations over North America from 1981 to 2016 for the first time by combining chemical transport modeling, satellite remote sensing, and ground-based measurements. We constrained and evaluated our estimates with direct ground-based PM_2.5 measurements when available and otherwise with historical estimates of PM_2.5 from PM₁₀ measurements or total suspended particle (TSP) measurements. The estimated PM_2.5 concentrations were generally consistent with direct ground-based PM_2.5 measurements over their duration from 1988 onward ( R² = 0.6 to 0.85) and to a lesser extent with PM_2.5 inferred from PM₁₀ measurements from 1985 to 1998 ( R² = 0.5 to 0.6). The collocated comparison of the trends of population-weighted annual average PM_2.5 from our estimates and ground-based measurements was highly consistent (RMSD = 0.66 μg m^-3). The population-weighted annual average PM_2.5 over North America decreased from 22 ± 6.4 μg m^-3 in 1981, to 12 ± 3.2 μg m^-3 in 1998, and to 7.9 ± 2.1 μg m^-3 in 2016, with an overall trend of -0.33 μg m^-3 yr^-1 (95% CI: -0.35, -0.31).

Choices Behind Numbers: a Review of the Major Air Pollution Health Impact Assessments in Europe

Purpose of Review

The aim of this review is to identify the key contextual and methodological differences in health impact assessments (HIA) of ambient air pollution performed for Europe. We limited our review to multi-country reviews. An additional aim is to quantify some of these differences by applying them in a HIA template in three European cities.

Recent Findings

Several HIAs of ambient air pollution have been performed for Europe, and their key results have been largely disseminated. Different studies have, however, come up with substantial differences in attributed health effects. It is of importance to review the background contributing to these differences and to quantify their importance for decision makers who will use them.

Summary

We identified several methodological differences that could explain the discrepancy behind the number of attributable deaths or years of life lost. The main differences are due to the exposure-response functions chosen, the ways of assessing air pollution levels, the air pollution scenarios and the study population. In the quantification part, we found that using risk estimates from the European Study of Cohorts for Air Pollution Effects (ESCAPE) instead of the American Cancer Society (ACS) study could nearly double the attributable burden of ambient air pollution.

This study provides some insights into the differential results in previously published HIAs on air pollution in Europe. These results are important for stakeholders in order to make informed decisions.

Electronic supplementary material

The online version of this article (10.1007/s40572-018-0175-2) contains supplementary material, which is available to authorized users.

Occupational Fine/Ultrafine Particles and Noise Exposure in Aircraft Personnel Operating in Airport Taxiway

The occupational exposure to airborne fine and ultrafine particles (UFPs) and noise in aircraft personnel employed in airport taxiway was investigated. Stationary samplings and multiple personal sampling sites and job tasks were considered. Size distribution, particle number concentrations, lung dose surface area were measured by personal particle counters and by means of an electric low pressure impactor (ELPI+TM). Morphological and chemical characterization of UFPs were performed by transmission and scanning electron microscopy, the latter together with energy dispersive X-Ray spectroscopy based spatially resolved compositional mapping. A-weighted noise exposure level A-weighted noise exposure level normalized to an 8 h working day and Peak Sound C-weighted Pressure Level was calculated for single worker and for homogeneous exposure groups. Our study provides evidence on the impact of aviation-related emissions on occupational exposure to ultrafine particles and noise exposure of workers operating in an airport taxiway. Main exposure peaks are related to pre-flight operations of engine aircrafts. Although exposure to ultrafine particles and noise appears to not be critical if compared with other occupational scenarios, the coincidence in time of high peaks of exposure to ultrafine particles and noise suggest that further investigations are warranted in order to assess possible subclinical and clinical adverse health effects in exposed workers, especially for cardiovascular apparatus.

Estimation of personal exposure to fine particles (PM2.5) of ambient origin for healthy adults in Hong Kong

Personal exposure and ambient fine particles (PM_2.5) measurements for 13 adult subjects (ages 19-57) were conducted in Hong Kong between April 2014 and June 2015. Six to 21 personal samples (mean = 19) per subject were obtained throughout the study period. Samples were analyzed for mass by gravimetric analysis, and 19 elements (from Na to Pb) were analyzed using X-Ray Fluorescence. Higher subject-specific correlations between personal and ambient sulfur (r_s = 0.92; p < 0.001) were found as compared to PM_2.5 mass (r_s = 0.79; p < 0.001) and other elements (0.06 < r_s < 0.86). Personal vs. ambient sulfur regression yielded an average exposure factor (F_pex) of 0.73 ± 0.02, supporting the use of sulfur as a surrogate to estimate personal exposure to PM_2.5 of ambient origin (E_a). E_a accounted for 41-82% and 57-73% of total personal PM_2.5 exposures (P) by season and by subject, respectively. The importance of both E_a and non-ambient exposures (E_na, 11.2 ± 5.6 μg/m³; 32.5 ± 10.9%) are noted. Mixed-effects models were applied to estimate the relationships between ambient PM_2.5 concentrations and their corresponding exposure variables (E_a, P). Higher correlations for E_a (0.90; p < 0.001) than for P (0.58; p < 0.01) were found. A calibration coefficient < 1 suggests an attenuation of 22% (ranging 16-28%) of the true effect estimates when using average ambient concentrations at central monitoring stations as surrogates for E_a. Stationary ambient data can be used to assess population exposure only if PM exposure is dominated by E_a.

Health benefits and control costs of tightening particulate matter emissions standards for coal power plants - The case of Northeast Brazil

Exposure to ambient particulate matter (PM) caused an estimated 4.2 million deaths worldwide in 2015. However, PM emission standards for power plants vary widely. To explore if the current levels of these standards are sufficiently stringent in a simple cost-benefit framework, we compared the health benefits (avoided monetized health costs) with the control costs of tightening PM emission standards for coal-fired power plants in Northeast (NE) Brazil, where ambient PM concentrations are below World Health Organization (WHO) guidelines. We considered three Brazilian PM₁₀ (PM_x refers to PM with a diameter under x micrometers) emission standards and a stricter U.S. EPA standard for recent power plants. Our integrated methodology simulates hourly electricity grid dispatch from utility-scale power plants, disperses the resulting PM_2.5, and estimates selected human health impacts from PM_2.5 exposure using the latest integrated exposure-response model. Since the emissions inventories required to model secondary PM are not available in our study area, we modeled only primary PM so our benefit estimates are conservative. We found that tightening existing PM₁₀ emission standards yields health benefits that are over 60 times greater than emissions control costs in all the scenarios we considered. The monetary value of avoided hospital admissions alone is at least four times as large as the corresponding control costs. These results provide strong arguments for considering tightening PM emission standards for coal-fired power plants worldwide, including in regions that meet WHO guidelines and in developing countries.

Human inflammatory response of endotoxin affected by particulate matter-bound transition metals

Bacterial endotoxins are a component of particulate matter (PM) with anticipated health implications, yet we know little about how host reception of endotoxin through toll-like receptor 4 (TLR4) is affected by its association with other PM components. Subsequently, we investigated the relationship between endotoxin concentration (recombinant Factor C (rFC) assay) and host recognition (HEK Blue-TLR4 NF-kB reporter cell line based assay) in various compositions of urban PM, including road traffic, industrial and urban green land use classes. While the assays did not correlate strongly between each other, the TLR4 reporter cell line was found to be better correlated to the IL-8 response of PM. Furthermore, the ability of the quantified endotoxin (rFC assay) to stimulate the TLR4/MD-2 complex was significantly affected by the urban land use class, where traffic locations were found to be significantly higher in bioactive endotoxin than the industrial and green locations. We subsequently turned our attention to PM composition and characterized the samples based on transition metal content (through ICP-MS). The effect of nickel and cobalt - previously reported to activate the hTLR4/MD-2 complex - was found to be negligible in comparison to that of iron. Here, the addition of iron as a factor significantly improved the regression model between the two endotoxin assays, explaining 77% of the variation of the TLR4 stimulation and excluding the significant effect of land use class. Moreover, the effect of iron proved to be more than a correlation, since dosing LPS with Fe²⁺ led to an increase up to 64% in TLR4 stimulation, while Fe²⁺ without LPS was unable to stimulate a response. This study shows that endotoxin quantification assays (such as the rFC assay) may not always correspond to human biological recognition of endotoxin in urban PM, while its toxicity can be synergistically influenced by the associated PM composition.

Mortality and morbidity due to exposure to ambient particulate matter

The aim of this study was to investigate spatial variation and health risk of the exposure to PM_2.5 (particulate matter with a diameter of 2.5 µm or less) and PM₁₀ (particulate matter with a diameter of 10 µm or less) in Sabzevar, Iran. PM_2.5 and PM₁₀ were measured during three campaigns from April to November 2017, in 26 sampling points. Spatial analysis was performed using kriging and autocorrelations (Moran's index) model in Arc GIS software. Relationship between exposure to the PM_2.5 and PM₁₀ and their health impacts were investigated by AirQ 2.2.3 software. The mean concentrations (and standard deviation) of PM _2.5 and PM₁₀ over the entire study period were 32.54 (37.28) and 42.61 (47.76) μg/m³, respectively, which were higher than the guideline of World Health Organization. According to the spatial analysis, the maximum concentrations of PM_2.5 and PM₁₀ were around the main highway (beltway) which placed all over the south of Sabzevar. According to the Moran's index, the emission patterns for PM_2.5 (Z-score = 2.53; P-value = 0.011) and PM₁₀ (Z-score = 2.82; P-value = 0.004) were clustered. The attributable percentage (AP) of total mortality related to PM_2.5 and PM₁₀ were 3.544% (95% confidence interval (CI): 2.623-4.447%) and 2.055% (95% CI: 1.379-2.721%) per increasing each 10 μg/m³ of these pollutants, respectively. According to observed results, it is suggested that the beltway and other pollution sources, such as industries, should be placed at a greater distance from the city, to reduce PM amounts in residential areas.

Assessment of impact of traffic-related air pollution on morbidity and mortality in Copenhagen Municipality and the health gain of reduced exposure

BACKGROUND

Health impact assessment (HIA) of exposure to air pollution is commonly based on city level (fine) particle concentration and may underestimate health consequences of changing local traffic. Exposure to traffic-related air pollution can be assessed at a high resolution by modelling levels of nitrogen dioxide (NO₂), which together with ultrafine particles mainly originate from diesel-powered vehicles in urban areas. The purpose of this study was to estimate the health benefits of reduced exposure to vehicle emissions assessed as NO₂ at the residence among the citizens of Copenhagen Municipality, Denmark.

METHODS

We utilized residential NO₂ concentrations modelled by use of chemistry transport models to calculate contributions from emission sources to air pollution. The DYNAMO-HIA model was applied to the population of Copenhagen Municipality by using NO₂ concentration estimates combined with demographic data and data from nationwide registers on incidence and prevalence of selected diseases, cause specific mortality, and total mortality of the population of Copenhagen. We used exposure-response functions linking NO₂ concentration estimates at the residential address with the risk of diabetes, cardiovascular diseases, and respiratory diseases derived from a large Danish cohort study with the majority of subjects residing in Copenhagen between 1971 and 2010. Different scenarios were modelled to estimate the dynamic impact of NO₂ exposure on related diseases and the potential health benefits of lowering the NO₂ level in the Copenhagen Municipality.

RESULTS

The annual mean NO₂ concentration was 19.6 μg/m³ and for 70% of the population the range of exposure was between 15 and 21 μg/m³. If NO₂ exposure was reduced to the annual mean rural level of 6 μg/m³, life expectancy in 2040 would increase by one year. The greatest gain in disease-free life expectancy would be lifetime without ischemic heart disease (1.4 years), chronic obstructive pulmonary disease (1.5 years for men and 1.6 years for women), and asthma (1.3 years for men and 1.5 years for women). Lowering NO₂ exposure by 20% would increase disease-free life expectancy for the different diseases by 0.3-0.5 years. Using gender specific relative risks affected the results.

CONCLUSIONS

Reducing the NO₂ exposure by controlling traffic-related air pollution reduces the occurrence of some of the most prevalent chronic diseases and increases life expectancy. Such health benefits can be quantified by DYNAMO-HIA in a high resolution exposure modelling. This paper demonstrates how traffic planners can assess health benefits from reduced levels of traffic-related air pollution.

Long-Term Exposure to Air Pollutants and Cancer Mortality: A Meta-Analysis of Cohort Studies

The aim of this study was to examine the relationship between main air pollutants and all cancer mortality by performing a meta-analysis. We searched PubMed, EMBASE (a biomedical and pharmacological bibliographic database of published literature produced by Elsevier), and the reference lists of other reviews until April 2018. A random-effects model was employed to analyze the meta-estimates of each pollutant. A total of 30 cohort studies were included in the final analysis. Overall risk estimates of cancer mortality for 10 µg/m³ per increase of particulate matter (PM)2.5, PM10, and NO₂ were 1.17 (95% confidence interval (CI): 1.11⁻1.24), 1.09 (95% CI: 1.04⁻1.14), and 1.06 (95% CI: 1.02⁻1.10), respectively. With respect to the type of cancer, significant hazardous influences of PM2.5 were noticed for lung cancer mortality and non-lung cancer mortality including liver cancer, colorectal cancer, bladder cancer, and kidney cancer, respectively, while PM10 had harmful effects on mortality from lung cancer, pancreas cancer, and larynx cancer. Our meta-analysis of cohort studies indicates that exposure to the main air pollutants is associated with increased mortality from all cancers.

Disability Adjusted Life Years (DALYs) in Terms of Years of Life Lost (YLL) Due to Premature Adult Mortalities and Postneonatal Infant Mortalities Attributed to PM2.5 and PM10 Exposures in Kuwait

Ambient air pollution in terms of fine and coarse particulate matter (PM_2.5 and PM₁₀) has been shown to increase adult and infant mortalities. Most studies have estimated the risk of mortalities through attributable proportions and number of excess cases with no reference to the time lost due to premature mortalities. Disability adjusted life years (DALYs) are necessary to measure the health impact of Ambient particulate matter (PM) over time. In this study, we used life-tables for three years (2014⁻2016) to estimate the years of life lost (YLL), a main component of DALYs, for adult mortalities (age 30+ years) and postneonatal infant mortalities (age 28+ days⁻1 year) associated with PM_2.5 exposure and PM₁₀ exposure, respectively. The annual average of PM_2.5 and PM₁₀ concentrations were recorded as 87.9 μg/m³ and 167.5 μg/m³, which are 8 times greater than the World Health Organization (WHO) air quality guidelines of 10 μg/m³ and 20 μg/m³, respectively. Results indicated a total of 252.18 (95% CI: 170.69⁻322.92) YLL for all ages with an increase of 27,474.61 (95% CI: 18,483.02⁻35,370.58) YLL over 10 years. The expected life remaining (ELR) calculations showed that 30- and 65-year-old persons would gain 2.34 years and 1.93 years, respectively if the current PM_2.5 exposure levels were reduced to the WHO interim targets (IT-1 = 35 μg/m³). Newborns and 1-year old children may live 79.81 and 78.94 years, respectively with an increase in average life expectancy of 2.65 years if the WHO PM₁₀ interim targets were met (IT-1 = 70 μg/m³). Sensitivity analyses for YLL were carried out for the years 2015, 2025, and 2045 and showed that the years of life would increase significantly for age groups between 30 and 85. Life expectancy, especially for the elderly (≥60 years), would increase at higher rates if PM_2.5 levels were reduced further. This study can be helpful for the assessment of poor air quality represented by PM_2.5 and PM₁₀ exposures in causing premature adult mortalities and postneonatal infant mortalities in developing countries with high ambient air pollution. Information in this article adds insights to the sustainable development goals (SDG 3.9.1 and 11.6.2) related to the reduction of mortality rates attributed to ambient air levels of coarse and fine particulate matter.

Estimation of Short-term Mortality and Morbidity Attributed to Fine Particulate Matter in the Ambient Air of Eight Iranian Cities

Amongst the various pollutants in the air, particulate matters (PM) have significant adverse effects on human health. The current research is based on existing epidemiological literature for quantitative estimation of the current health impacts related to particulate matters in some selected principal Iranian megacities. In order to find the influence of air pollution on human health, we used the AirQ software tool presented by the World Health Organization (WHO) European Centre for Environment and Health (ECEH), Bilthoven Division. The adverse health outcomes used in the study consist of mortality (all causes excluding accidental causes), due to cardiovascular (CVD) and respiratory (RES) diseases, and morbidity (hospital admissions for CVD and RES causes). For this purpose, hourly PM₁₀ data were taken from the monitoring stations in eight study cities during 2011 and 2012. Results showed annual average concentrations of PM₁₀ and PM_2.5 in all megacities exceeded national and international air quality standards and even reached levels nearly ten times higher than WHO guidelines in some cities. Considering the short-term effects, PM_2.5 had the maximum effects on the health of the 19,048,000 residents of the eight Iranian cities, causing total mortality of 5,670 out of 87,907 during a one-year time-period. Hence, reducing concentrations and controlling air pollution, particularly the presence of particles, is urgent in these metropolises.

Simultaneous determination of eighteen nitro-polyaromatic hydrocarbons in PM2.5 by atmospheric pressure gas chromatography-tandem mass spectrometry

A new atmospheric pressure gas chromatography-tandem mass spectrometry (APGC-MS/MS) was developed to simultaneously separate, identify and quantify 18 nitro-polyaromatic hydrocarbons (NPAHs) in air fine particulate matter (PM_2.5). Compared with traditional negative chemical ionization (NCI) or electron impact ionization (EI)-MS/MS methods, APGC-MS/MS equipped with an atmospheric pressure chemical ionization (APCI) source provided better sensitivity and selectivity for NPAHs analysis in PM_2.5.18 NPAHs were completely separated, and satisfactory linear response (R² > 0.99), low instrumental detection limits (0.20-2.18 pg mL^-1) and method detection limits (0.001-0.015 pg m^-3) were achieved. Due to the reliable performance of the instrument, only minimal sample pretreatment is needed. It ensured the satisfactory method recovery (70%-120%) and qualified repeatability (RSD: 1.1%-17.2%), which met the requirement of trace analysis of NAPHs in the real environmental PM_2.5. Using the developed method, the actual PM_2.5 samples collected from Taiyuan, China in both summer and winter were analyzed, and 17 NPAHs but 2-nitrofluorene were detected and quantified. According to the obtained NAPH concentration results, the generation mechanism of NPAHs in PM_2.5 and the effects on NPAHs formation caused by some ambient air pollutants were preliminarily discussed: secondary photochemical reaction might be the dominant source of NPAHs in PM_2.5 collected from Taiyuan in both summer and winter; ambient air pollutants (NO₂, SO₂, CO) had more contribution on the NPAHs secondary formation of PM_2.5 in winter.

Metals and mineral phases of dusts collected in different urban parks of Krakow and their impact on the health of city residents

The authors present the results of chemical and mineralogical analyses of urban dusts collected in the spring seasons of 2015 and 2016 in three different parks of the Cracow agglomeration. The parks are located in the city centre, in the Nowa Huta industrial district and in a new housing development situated around 9 km west of the city centre. Mineralogical instrumental analyses included the SEM, FTIR and XRD methods and revealed that the dusts of Cracow are highly amorphous and contain significant amounts of hydrocarbons, whereas quartz, feldspars, kaolinite and gypsum are their crystalline phases. Chemical analyses were carried out using the ICP-MS method on aqua regia extracts of the starting samples. The contents of selected toxic elements are: As 5-123; Cd 1-14; Pb 56-258; Zn 486-1891 mg/kg and Fe 0.74-4.02 wt%. The health risk of these elements imposed on the residents of Cracow frequently visiting the three urban parks was assessed on the basis of the health quotient index HQ. At its values exceeding 1, adverse health effects are probable in humans. The HQ values calculated for As and Tl contained in the Cracow Park dusts in the case of adults are 3.42E-01 and 3.00E-01, respectively. They are significantly higher (one order of magnitude) in the case of children 3.19E+00 and 2.27E+00, respectively.

A county-level estimate of PM2.5 related chronic mortality risk in China based on multi-model exposure data

BACKGROUND

Ambient fine particulate matter (PM_2.5) pollution is currently a serious environmental problem in China, but evidence of health effects with higher resolution and spatial coverage is insufficient.

OBJECTIVE

This study aims to provide a better overall understanding of long-term mortality effects of PM_2.5 pollution in China and a county-level spatial map for estimating PM_2.5 related premature deaths of the entire country.

METHOD

Using four sets of satellite-derived PM_2.5 concentration data and the integrated exposure-response model which has been employed by the Global Burden of Disease (GBD) to estimate global mortality of ambient and household air pollution in 2010, we estimated PM_2.5 related premature mortality for five endpoints across China in 2010.

RESULT

Premature deaths attributed to PM_2.5 nationwide amounted to 1.27million in total, and 119,167, 83,976, 390,266, 670,906 for adult chronic obstructive pulmonary disease, lung cancer, ischemic heart disease, and stroke, respectively; 3995 deaths for acute lower respiratory infections were estimated in children under the age of 5. About half of the premature deaths were from counties with annual average PM_2.5 concentrations above 63.61μg/m³, which cover 16.97% of the Chinese territory. These counties were largely located in the Beijing-Tianjin-Hebei region and the North China Plain. High population density and high pollution areas exhibited the highest health risks attributed to air pollution. On a per capita basis, the highest values were mostly located in heavily polluted industrial regions.

CONCLUSION

PM_2.5-attributable health risk is closely associated with high population density and high levels of pollution in China. Further estimates using long-term historical exposure data and concentration-response (C-R) relationships should be completed in the future to investigate longer-term trends in the effects of PM_2.5.

Forecasting PM2.5 induced male lung cancer morbidity in China using satellite retrieved PM2.5 and spatial analysis

The present study predicts a spatial distribution of lung cancer morbidity in Chinese males due to exposure to PM_2.5 concentration from 2010 to 2015. A spatial autocorrelation method was used to evaluate the spatial relationship between the lung cancer morbidities from 2006 to 2009 and satellite-derived PM_2.5 atmospheric levels. A comprehensive grey correlation degree analysis was carried out to assess the simultaneous and lag associations between the lung cancer morbidity and PM_2.5 concentration. These relationships were subsequently applied to predict male lung cancer morbidity in a specific year. Annual mean PM_2.5 levels in this specific year and previous 8years were used as 9 independent variables to establish four statistical models. These models include ridge regression (RR), partial least squares regression (PLSR), support vector regression (SVR), and the combined forecasting model (CFM) to predict the male lung cancer morbidity in China from 2010 to 2015. The model error evaluations suggested that the partial least squares regression model performed the best in the male lung cancer morbidity forecast. We calculated the male lung cancer morbidity by the optimal method among the established statistical forecasting models at 1948 sites in China. The gridded morbidity distribution from 2010 to 2015 across the country was obtained by Kriging interpolation method. Results showed that the male lung cancer morbidity increased significantly from western to eastern China, except for the far north region. This spatial pattern is in line with the spatial distribution of PM_2.5 concentration, manifesting a significant relationship between PM_2.5 concentration level and lung cancer morbidity in Chinese males.

Aerosol Health Effects from Molecular to Global Scales

Poor air quality is globally the largest environmental health risk. Epidemiological studies have uncovered clear relationships of gaseous pollutants and particulate matter (PM) with adverse health outcomes, including mortality by cardiovascular and respiratory diseases. Studies of health impacts by aerosols are highly multidisciplinary with a broad range of scales in space and time. We assess recent advances and future challenges regarding aerosol effects on health from molecular to global scales through epidemiological studies, field measurements, health-related properties of PM, and multiphase interactions of oxidants and PM upon respiratory deposition. Global modeling combined with epidemiological exposure-response functions indicates that ambient air pollution causes more than four million premature deaths per year. Epidemiological studies usually refer to PM mass concentrations, but some health effects may relate to specific constituents such as bioaerosols, polycyclic aromatic compounds, and transition metals. Various analytical techniques and cellular and molecular assays are applied to assess the redox activity of PM and the formation of reactive oxygen species. Multiphase chemical interactions of lung antioxidants with atmospheric pollutants are crucial to the mechanistic and molecular understanding of oxidative stress upon respiratory deposition. The role of distinct PM components in health impacts and mortality needs to be clarified by integrated research on various spatiotemporal scales for better evaluation and mitigation of aerosol effects on public health in the Anthropocene.

Relationship between fine particulate matter, weather condition and daily non-accidental mortality in Shanghai, China: A Bayesian approach

There are concerns that the reported association of ambient fine particulate matter (PM_2.5) with mortality might be a mixture of PM_2.5 and weather conditions. We evaluated the effects of extreme weather conditions and weather types on mortality as well as their interactions with PM_2.5 concentrations in a time series study. Daily non-accidental deaths, individual demographic information, daily average PM_2.5 concentrations and meteorological data between 2012 and 2014 were obtained from Shanghai, China. Days with extreme weather conditions were identified. Six synoptic weather types (SWTs) were generated. The generalized additive model was set up to link the mortality with PM_2.5 and weather conditions. Parameter estimation was based on Bayesian methods using both the Jeffreys’ prior and an informative normal prior in a sensitivity analysis. We estimate the percent increase in non-accidental mortality per 10 μg/m³ increase in PM_2.5 concentration and constructed corresponding 95% credible interval (CrI). In total, 336,379 non-accidental deaths occurred during the study period. Average daily deaths were 307. The results indicated that per 10 μg/m³ increase in daily average PM_2.5 concentration alone corresponded to 0.26–0.35% increase in daily non-accidental mortality in Shanghai. Statistically significant positive associations between PM_2.5 and mortality were found for favorable SWTs when considering the interaction between PM_2.5 and SWTs. The greatest effect was found in hot dry SWT (percent increase = 1.28, 95% CrI: 0.72, 1.83), followed by warm humid SWT (percent increase = 0.64, 95% CrI: 0.15, 1.13). The effect of PM_2.5 on non-accidental mortality differed under specific extreme weather conditions and SWTs. Environmental policies and actions should take into account the interrelationship between the two hazardous exposures.

Scale- and Region-Dependence in Landscape-PM2.5 Correlation: Implications for Urban Planning

Under rapid urbanization, many cities in China suffer from serious fine particulate matter (PM2.5) pollution. As the emission sources or adsorption sinks, land use and the corresponding landscape pattern unavoidably affect the concentration. However, the correlation varies with different regions and scales, leaving a significant gap for urban planning. This study clarifies the correlation with the aid of in situ and satellite-based spatial datasets over six urban agglomerations in China. Two coverage and four landscape indices are adopted to represent land use and landscape pattern. Specifically, the coverage indices include the area ratios of forest (F_PLAND) and built-up areas (C_PLAND). The landscape indices refer to the perimeter-area fractal dimension index (PAFRAC), interspersion and juxtaposition index (IJI), aggregation index (AI), Shannon’s diversity index (SHDI). Then, the correlation between PM2.5 concentration with the selected indices are evaluated from supporting the potential urban planning. Results show that the correlations are weak with the in situ PM2.5 concentration, which are significant with the regional value. It means that land use coverage and landscape pattern affect PM2.5 at a relatively large scale. Furthermore, regional PM2.5 concentration negatively correlate to F_PLAND and positively to C_PLAND (significance at p < 0.05), indicating that forest helps to improve air quality, while built-up areas worsen the pollution. Finally, the heterogeneous landscape presents positive correlation to the regional PM2.5 concentration in most regions, except for the urban agglomeration with highly-developed urban (i.e., the Jing-Jin-Ji and Chengdu-Chongqing urban agglomerations). It suggests that centralized urbanization would be helpful for PM2.5 pollution controlling by reducing the emission sources in most regions. Based on the results, the potential urban planning is proposed for controlling PM2.5 pollution for each urban agglomeration.