Characteristics and Source-specific Health Risks of Ambient PM2.5-bound PAHs in an Urban City of Northern Taiwan

Polycyclic aromatic hydrocarbons (PAHs) with highly toxic compounds mainly exist in small-sized particles and can induce considerable human health risks. Studies on PM2.5-bound PAHs and their source-specific human health risks still remain scarce. Daily PM2.5 samples (n = 119) were collected every three days from 2016 to 2017 in Taipei city, Taiwan. Fifteen PAHs in PM2.5 were analyzed via gas chromatography tandem mass spectrometry (GC/MS-MS). We utilized a positive matrix factorization (PMF) model, diagnostic ratios, and potential source contribution function (PSCF) to identify the origins of PM2.5-bound PAHs. The annual concentration of total PAHs (TPAH) was 0.79 ± 0.67 ng m–3 (range = 0.11–3.27 ng m–3). The highest and lowest values of TPAH appeared in winter and autumn with a mean of 1.36 ng m–3 and 0.43 ng m–3, respectively. The contributions of high-molecular-weight PAHs (HMW PAHs) to TPAH were notably higher than those of low-molecularweight PAHs (LMW PAHs) during the sampling period. Benzo[ghi]perylene (BghiP) accounted for the highest percentage (23.9%) of TPAH among selected congeners. Traffic emissions (31.3%) were identified as the predominant contributor to ambient PM2.5-bound PAHs, followed by industrial emissions (29.2%), evaporated/unburned oil (22.3%), and biomass/coal combustion (17.1%). Apart from the local sources, PSCF-derived results showed that emissions from industrial activities in northeast China and shipping around the Yellow Sea and East China Sea could affect the PAHs in the study area. Traffic emissions were the strongest contributor to human health risk, thus pointing to the significance of control over vehicle exhaust. This study suggests that it is necessary to distinguish the sources of the PM2.5-bound PAHs in order to underpin preventive and mitigative strategies for protecting environmental and public health

Advanced Air Quality Management with Machine Learning

Air pollution has been a significant health risk factor at a regional and global scale. Although the present method can provide assessment indices like exposure risks or air pollutant concentrations for air quality management, the modeling estimations still remain non-negligible bias which could deviate from reality and limit the effectiveness of emission control strategies to reduce air pollution and derive health benefits. The current development in air quality management is still impeded by two major obstacles: (1) biased air quality concentrations from air quality models and (2) inaccurate exposure risk estimations Inspired by more available and overwhelming data, machine learning techniques provide promising opportunities to solve the above-mentioned obstacles and bridge the gap between model results and reality. This dissertation illustrates three machine learning applications to strengthen air quality management: (1) identifying heterogeneous exposure risk to air pollutants among diverse urbanization levels, (2) correcting modeled air pollutant concentrations and quantifying the bias of sources from model inputs, and (3) examine nonlinear air pollutant responses to local emissions. This dissertation uses Taiwan as a case study, due to its well-established hospital data, emission inventory, and air quality monitoring network. In conclusion, although ML models have become common in atmospheric and environmental health science in recent years, the modeling processes and output interpretation should rely on interdisciplinary professions and judgment. Except for meeting the basic modeling performance, future ML applications in atmospheric and environmental health science should provide interpretability and explainability in terms of human-environment interactions and interpretable physical/chemical mechanisms. Such applications are expected to feedback to traditional methods and deepen our understanding of environmental science

Global Health Impacts of Dust Storms: A Systematic Review.

BACKGROUND: Dust storms and their impacts on health are becoming a major public health issue. The current study examines the health impacts of dust storms around the world to provide an overview of this issue. METHOD: In this systematic review, 140 relevant and authoritative English articles on the impacts of dust storms on health (up to September 2019) were identified and extracted from 28 968 articles using valid keywords from various databases (PubMed, WOS, EMBASE, and Scopus) and multiple screening steps. Selected papers were then qualitatively examined and evaluated. Evaluation results were summarized using an Extraction Table. RESULTS: The results of the study are divided into two parts: short and long-term impacts of dust storms. Short-term impacts include mortality, visitation, emergency medical dispatch, hospitalization, increased symptoms, and decreased pulmonary function. Long-term impacts include pregnancy, cognitive difficulties, and birth problems. Additionally, this study shows that dust storms have devastating impacts on health, affecting cardiovascular and respiratory health in particular. CONCLUSION: The findings of this study show that dust storms have significant public health impacts. More attention should be paid to these natural hazards to prepare for, respond to, and mitigate these hazardous events to reduce their negative health impacts.Registration: PROSPERO registration number CRD42018093325

The Association between Dust Storms and Daily Non-Accidental Mortality in the United States, 1993–2005

BACKGROUND: The impact of dust storms on human health has been studied in the context of Asian, Saharan, Arabian, and Australian storms, but there has been no recent population-level epidemiological research on the dust storms in North America. The relevance of dust storms to public health is likely to increase as extreme weather events are predicted to become more frequent with anticipated changes in climate through the 21st century. OBJECTIVES: We examined the association between dust storms and county-level non-accidental mortality in the United States from 1993 through 2005. METHODS: Dust storm incidence data, including date and approximate location, are taken from the U.S. National Weather Service storm database. County-level mortality data for the years 1993-2005 were acquired from the National Center for Health Statistics. Distributed lag conditional logistic regression models under a time-stratified case-crossover design were used to study the relationship between dust storms and daily mortality counts over the whole United States and in Arizona and California specifically. End points included total non-accidental mortality and three mortality subgroups (cardiovascular, respiratory, and other non-accidental). RESULTS: We estimated that for the United States as a whole, total non-accidental mortality increased by 7.4% (95% CI: 1.6, 13.5; p = 0.011) and 6.7% (95% CI: 1.1, 12.6; p = 0.018) at 2- and 3-day lags, respectively, and by an average of 2.7% (95% CI: 0.4, 5.1; p = 0.023) over lags 0-5 compared with referent days. Significant associations with non-accidental mortality were estimated for California (lag 2 and 0-5 day) and Arizona (lag 3), for cardiovascular mortality in the United States (lag 2) and Arizona (lag 3), and for other non-accidental mortality in California (lags 1-3 and 0-5). CONCLUSIONS: Dust storms are associated with increases in lagged non-accidental and cardiovascular mortality. Citation: Crooks JL, Cascio WE, Percy MS, Reyes J, Neas LM, Hilborn ED. 2016. The association between dust storms and daily non-accidental mortality in the United States, 1993-2005. Environ Health Perspect 124:1735-1743; http://dx.doi.org/10.1289/EHP216

Potential Role of Ultrafine Particles in Associations between Airborne Particle Mass and Cardiovascular Health

Numerous epidemiologic time-series studies have shown generally consistent associations of cardiovascular hospital admissions and mortality with outdoor air pollution, particularly mass concentrations of particulate matter (PM) ≤2.5 or ≤10 μm in diameter (PM(2.5), PM(10)). Panel studies with repeated measures have supported the time-series results showing associations between PM and risk of cardiac ischemia and arrhythmias, increased blood pressure, decreased heart rate variability, and increased circulating markers of inflammation and thrombosis. The causal components driving the PM associations remain to be identified. Epidemiologic data using pollutant gases and particle characteristics such as particle number concentration and elemental carbon have provided indirect evidence that products of fossil fuel combustion are important. Ultrafine particles < 0.1 μm (UFPs) dominate particle number concentrations and surface area and are therefore capable of carrying large concentrations of adsorbed or condensed toxic air pollutants. It is likely that redox-active components in UFPs from fossil fuel combustion reach cardiovascular target sites. High UFP exposures may lead to systemic inflammation through oxidative stress responses to reactive oxygen species and thereby promote the progression of atherosclerosis and precipitate acute cardiovascular responses ranging from increased blood pressure to myocardial infarction. The next steps in epidemiologic research are to identify more clearly the putative PM casual components and size fractions linked to their sources. To advance this, we discuss in a companion article (Sioutas C, Delfino RJ, Singh M. 2005. Environ Health Perspect 113:947–955) the need for and methods of UFP exposure assessment

Associations of fine particulate matter exposure with sleep disorder indices in adults and mediating effect of body fat

Exposure to particulate matter (PM) may be a risk factor for obstructive sleep apnea (OSA) and obesity. However, whether body fat accumulation exerts mediating effects on the association between air pollutant exposure and OSA aggravation remains unclear. This study retroactively acquired the polysomnographic data (sleep variables) and body composition information from 2893 patients in a northern Taiwan sleep center. The levels of exposure to various air pollutants were estimated using an adjusted method based on data from governmental air quality monitoring stations near registered residential addresses instead of only referencing the nearest station. The sleep disorder indices and body fat metrics, which served as the outcomes of interest, were transformed using the Box-Cox transformation. Multiple linear regression models and causal mediation analysis were employed to investigate the associations between the analyzed parameters and the estimated air pollutant exposure at various time scales (1-, 6-, and 12-month). Significant associations were observed between the increased interquartile range (IQR) of short-term (1-month) exposure to PM ≤ 10 μm (PM10), PM ≤ 2.5 μm (PM2.5), and the apnea–hypopnea index (AHI), oxygen desaturation index (ODI), and arousal index (ArI). Short-term (1-month) exposure to PM10 and PM2.5 was significantly associated with increased trunk fat percentage. Causal mediation analysis revealed that short-term (1-month) exposure to PM10 and PM2.5 affected trunk fat percentage, thereby partially meditating the elevations in AHI, ODI, and ArI. PM exposure may directly increase sleep disorder indices and alter body fat, thereby mediating the worsening of OSA manifestations (i.e., increased AHI, ODI, and ArI)

Review on Sampling Methods and Health Impacts of Fine (PM₂.₅, ≤2.5 µm) and Ultrafine (UFP, PM₀.₁, ≤0.1 µm) Particles

Airborne particulate matter (PM) is of great concern in the modern-day atmosphere owing to its association with a variety of health impacts, such as respiratory and cardiovascular diseases. Of the various size fractions of PM, it is the finer fractions that are most harmful to health, in particular ultrafine particles (PM₀.₁; UFPs), with an aerodynamic diameter ≤ 100 nm. The smaller size fractions, of ≤2.5 µm (PM₂.₅; fine particles) and ≤0.1 µm (PM₀.₁; ultrafine particles), have been shown to have numerous linkages to negative health effects; however, their collection/sampling remains challenging. This review paper employed a comprehensive literature review methodology; 200 studies were evaluated based on the rigor of their methodologies, including the validity of experimental designs, data collection methods, and statistical analyses. Studies with robust methodologies were prioritised for inclusion. This review paper critically assesses the health risks associated with fine and ultrafine particles, highlighting vehicular emissions as the most significant source of particulate-related health effects. While coal combustion, diesel exhaust, household wood combustors’ emissions, and Earth’s crust dust also pose health risks, evidence suggests that exposure to particulates from vehicular emissions has the greatest impact on human health due to their widespread distribution and contribution to air pollution-related diseases. This article comprehensively examines current sampling technologies, specifically focusing on the collection and sampling of ultrafine particles (UFP) from ambient air to facilitate toxicological and physiochemical characterisation efforts. This article discusses diverse approaches to collect fine and ultrafine particulates, along with experimental endeavours to assess ultrafine particle concentrations across various microenvironments. Following meticulous evaluation of sampling techniques, high-volume air samplers such as the Chem Vol Model 2400 High Volume Cascade Impactor and low-volume samplers like the Personal Cascade Impactor Sampler (PCIS) emerge as effective methods. These techniques offer advantages in particle size fractionation, collection efficiency, and adaptability to different sampling environments, positioning them as valuable tools for precise characterisation of particulate matter in air quality research and environmental monitoring

Air Quality Indices to Understand the Ambient Air Quality in Vicinity of Dam Sites of Different Irrigation Projects in Karnataka State, India

Ambient air quality monitoring was carried out in the vicinity of dam and nearby residential sites in four river basins in Karnataka with reference to SPM, RSPM, SO2 and NOx, employing Envirotech APM-460 Respirable Dust Sampler with provision to keep impingers having absorbing reagent. Further, three different methods of Air quality index (AQI) calculation on based on SPM and RSPM values were used to evaluate the prevailed ambient air quality in the near and surroundings areas at the time of dam constructional activities. The concentrations of SPM, RSPM, SO2 and NOx near the dam sites were respectively 540, 170, 5.8 and 17.9 .g/m3 in Varahi river basin; 440, 158, 3.8 and 11.4 .g/m3 in SLIS river basin and, 255.55, 83.3, 2.0 and 1.7.g/m3 in SRLIS river basin. The SPM, RSPM and SO2 concentrations was 340, 70 and 0.3 .g/m3 in the vicinity of dam site of Bellary nala river basin while NOx concentration was below the detectable limit. AQI calculations revealed that the dam sites in all four river basins were high to severely pollute compared to other monitored stations, owing to its construction activitie