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)

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

Influences of commuting mode, air conditioning mode and meteorological parameters on fine particle (PM2.5) exposure levels in traffic microenvironments

With the aim of determining the impacts of various factors on commuter exposure to fine particulate matter (PM2.5), a series of field studies were carried out to measure commuter exposure to PM2.5 on six major commuting modes (in-cabin mode: bus, taxi and metro; on-roadway mode: walking, bicycle and motorcycle) in a highly industrialized city in the Pearl River Delta, China. The results showed that the exposure level was greatly influenced by the commuter mode, with the on-roadway mode showing a higher PM2.5 concentration (76 μg/m3). An experiment with the taxi mode suggested that the use of air-conditioning can effectively reduce exposure levels in most cases (by at least 83%). Apart from traffic-related emissions, ambient PM2.5 concentration also had important impacts on exposure levels in most commuting modes, which was further ascertained by the seasonal variations in exposure levels and their significant correlations (p < 0.05) with meteorological parameters (temperature, relative humidity, wind speed and direction). The results of a General Linear Model analysis show that temperature, traffic mode and wind speed were significant factors that explained 27.3% of variability for the in-cabin mode, while relative humidity and wind speed were the significant determinants for the on-roadway mode, which contributed 14.1% of variability. In addition, wind direction was also an important determinant for both in-cabin and on-roadway modes. This study has some valuable implications that can help commuters to adopt appropriate travel behavior to reduce their personal exposure to such pollutants

Effects of Ambient Air Pollution on Asthma-Related Emergency Department Visits within the Las Vegas Metropolitan Area

The objective of this research was to evaluate the risk for asthma-related Emergency Department visits and their association with ambient air pollution within the Las Vegas metropolitan area. All data were aggregated by date and ZIP Code. The association was analyzed by applying the distributed lag non-linear model in an attempt to identify elevated concentrations of specific air pollutants as triggers and their delayed effects (lag days). Relative Risk (RR) and 95% confidence intervals were produced, while adjusting for socioeconomic status. This ecological population-based study analyzed daily asthma counts of Emergency Department visits from January 1st, 2009 to December 31st, 2014 (N= 109,550). The exposure-outcome analysis found that when PM10 reaches 265 μg/m3, RR is greater than 1, between 0-2 days lag, dissipates, and peaks between 5-7 days lag. At initial exposure, PM10 had a RR of 2.83 (95% CI = 1.11, 7.20). At 7 days lag, PM10 reached a RR of 2.91 (95% CI= 1.21, 7.02), supporting that these associations present a non-linear lag effect. Understanding the adverse effects caused by elevated concentrations of criteria air pollutants, particularly when they exceed federal standards, and recognizing that a lag time exists, is a call to action for healthcare providers to educate their patients as to proper exposure prevention strategies and the development of tailored asthma management plans