scholarly journals Air Pollution and Mortality: Timing Is Everything

Atmosphere ◽  
2020 ◽  
Vol 11 (12) ◽  
pp. 1274
Author(s):  
Frederick W. Lipfert
Keyword(s):  
Air Pollution ◽  
Time Series ◽  
Air Quality ◽  
Indoor Air Pollution ◽  
Health Effect ◽  
Traffic Density ◽  
Long Term Effects ◽  
Short Term ◽  
Long Term Studies

This paper considers timing issues in health-effect exposure and response studies. Short-term studies must consider delayed and cumulative responses; prior exposures, disease latency, and cumulative impacts are required for long-term studies. Lacking individual data, long-term air quality describes locations, as do greenspaces and traffic density, rather than exposures of residents. Indoor air pollution can bias long-term exposures and effect estimates but short-term effects also respond to infiltrated outdoor air. Daily air quality fluctuations may affect the frail elderly and are necessarily included in long-term averages; any true long-term effects must be given by differences between annual and daily effects. I found such differences to be negligible after adjusting for insufficient lag effects in time-series studies and neglect of prior exposures in long-term studies. Aging of subjects under study implies cumulative exposures, but based on age-specific mortality, I found relative risks decreasing with age, precluding cumulative effects. A new type of time-series study found daily mortality of previously frail subjects to be associated with various pollutants without exposure thresholds, but the role of air pollution in the onset of frailty remains an unexplored issue. The importance of short-term fluctuations has been underestimated and putative effects of long-term exposures have been overestimated.

scholarly journals Air pollution, SARS-CoV-2 transmission, and COVID-19 outcomes: A state-of-the-science review of a rapidly evolving research area

2020 ◽  
Author(s):  
Anushka Bhaskar ◽  
Jay Chandra ◽  
Danielle Braun ◽  
Jacqueline Cellini ◽  
Francesca Dominici
Keyword(s):  
Air Pollution ◽  
Time Series ◽  
Health Outcomes ◽  
Study Design ◽  
Air Pollutant ◽  
Short Term ◽  
Cross Sectional ◽  
Short And Long Term ◽  
Long Term Studies

Background: As the coronavirus pandemic rages on, 692,000 (August 7, 2020) human lives and counting have been lost worldwide to COVID-19. Understanding the relationship between short- and long-term exposure to air pollution and adverse COVID-19 health outcomes is crucial for developing solutions to this global crisis. Objectives: To conduct a scoping review of epidemiologic research on the link between short- and long-term exposure to air pollution and COVID-19 health outcomes. Method: We searched PubMed, Web of Science, Embase, Cochrane, MedRxiv, and BioRxiv for preliminary epidemiological studies of the association between air pollution and COVID-19 health outcomes. 28 papers were finally selected after applying our inclusion/exclusion criteria; we categorized these studies as long-term studies, short-term time-series studies, or short-term cross-sectional studies. One study included both short-term time-series and a cross-sectional study design. Results: 27 studies of the 28 reported evidence of statistically significant positive associations between air pollutant exposure and adverse COVID-19 health outcomes; 11 of 12 long-term studies and all 16 short-term studies reported statistically significant positive associations. The 28 identified studies included various confounders, spatial and temporal resolutions of pollution concentrations, and COVID-19 health outcomes. Discussion: We discuss methodological challenges and highlight additional research areas based on our findings. Challenges include data quality issues, ecological study design limitations, improved adjustment for confounders, exposure errors related to spatial resolution, geographic variability in testing, mitigation measures and pandemic stage, clustering of health outcomes, and a lack of publicly available data and code.

scholarly journals Temporal Difference-based Graph Transformer Networks for Air Quality PM2.5 Prediction: A Case Study in China

2022 ◽  
Author(s):  
Zhen Zhang ◽  
Shiqing Zhang ◽  
Xiaoming Zhao ◽  
Linjian Chen ◽  
Jun Yao
Keyword(s):  
Air Pollution ◽  
Time Series ◽  
Air Quality ◽  
Attention Mechanism ◽  
Quality Prediction ◽  
Temporal Difference ◽  
Short Term ◽  
Air Quality Prediction ◽  
Complex Relationships

Abstract The acceleration of industrialization and urbanization has recently brought about serious air pollution problems, which threaten human health and lives, the environmental safety, and sustainable social development. Air quality prediction is an effective approach for providing early warning of air pollution and supporting cleaner industrial production. However, existing approaches have suffered from a weak ability to capture long-term dependencies and complex relationships from time series PM2.5 data. To address this problem, this paper proposes a new deep learning model called temporal difference-based graph transformer networks (TDGTN) to learn long-term temporal dependencies and complex relationships from time series PM2.5 data for air quality PM2.5 prediction. The proposed TDGTN comprises of encoder and decoder layers associated with the developed graph attention mechanism. In particular, considering the similarity of different time moments and the importance of temporal difference between two adjacent moments for air quality prediction, we first construct graph-structured data from original time series PM2.5 data at different moments without explicit graph structure. Then, based on the constructed graph, we improve the self-attention mechanism with the temporal difference information, and develop a new graph attention mechanism. Finally, the developed graph attention mechanism is embedded into the encoder and decoder layers of the proposed TDGTN to learn long-term temporal dependencies and complex relationships from a graph prospective on air quality PM2.5 prediction tasks. To verify the effectiveness of the proposed method, we conduct air quality prediction experiments on two real-world datasets in China, such as Beijing PM2.5 dataset ranging from 01/01/2010 to 12/31/2014 and Taizhou PM2.5 dataset ranging from 01/01/2017 to 12/31/2019. Compared with other air quality forecasting methods, such as autoregressive moving average (ARMA), support vector regression (SVR), convolutional neural network (CNN), long short-term memory (LSTM), the original Transformer, our experiment results indicate that the proposed method achieves more accurate results on both short-term (1 hour) and long-term (6, 12, 24, 48 hours) air quality prediction tasks.

scholarly journals Effects of meteorology and emissions on urban air quality: a quantitative statistical approach to long-term records (1999–2016) in Seoul, South Korea

2018 ◽  
Vol 18 (21) ◽  
pp. 16121-16137 ◽  
Author(s):  
Jihoon Seo ◽  
Doo-Sun R. Park ◽  
Jin Young Kim ◽  
Daeok Youn ◽  
Yong Bin Lim ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
South Korea ◽  
Meteorological Conditions ◽  
Meteorological Variables ◽  
Urban Air ◽  
Short Term ◽  
Long Term Trends ◽  
Local Emissions

Abstract. Together with emissions of air pollutants and precursors, meteorological conditions play important roles in local air quality through accumulation or ventilation, regional transport, and atmospheric chemistry. In this study, we extensively investigated multi-timescale meteorological effects on the urban air pollution using the long-term measurements data of PM10, SO2, NO2, CO, and O3 and meteorological variables over the period of 1999–2016 in Seoul, South Korea. The long-term air quality data were decomposed into trend-free short-term components and long-term trends by the Kolmogorov–Zurbenko filter, and the effects of meteorology and emissions were quantitatively isolated using a multiple linear regression with meteorological variables. In terms of short-term variability, intercorrelations among the pollutants and meteorological variables and composite analysis of synoptic meteorological fields exhibited that the warm and stagnant conditions in the migratory high-pressure system are related to the high PM10 and primary pollutant, while the strong irradiance and low NO2 by high winds at the rear of a cyclone are related to the high O3. In terms of long-term trends, decrease in PM10 (−1.75 µg m−3 yr−1) and increase in O3 (+0.88 ppb yr−1) in Seoul were largely contributed by the meteorology-related trends (−0.94 µg m−3 yr−1 for PM10 and +0.47 ppb yr−1 for O3), which were attributable to the subregional-scale wind speed increase. Comparisons with estimated local emissions and socioeconomic indices like gross domestic product (GDP) growth and fuel consumptions indicate probable influences of the 2008 global economic recession as well as the enforced regulations from the mid-2000s on the emission-related trends of PM10 and other primary pollutants. Change rates of local emissions and the transport term of long-term components calculated by the tracer continuity equation revealed a decrease in contributions of local emissions to the primary pollutants including PM10 and an increase in contributions of local secondary productions to O3. The present results not only reveal an important role of synoptic meteorological conditions on the episodic air pollution events but also give insights into the practical effects of environmental policies and regulations on the long-term air pollution trends. As a complementary approach to the chemical transport modeling, this study will provide a scientific background for developing and improving effective air quality management strategy in Seoul and its metropolitan area.

scholarly journals Estimating Short- and Long-Term Associations Between Air Quality Index and COVID-19 Transmission: Evidence From 257 Chinese Cities

2021 ◽  
Vol 66 ◽  
Author(s):  
Ru Cao ◽  
Yuxin Wang ◽  
Xiaochuan Pan ◽  
Xiaobin Jin ◽  
Jing Huang ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Quality Index ◽  
Air Quality Index ◽  
Pollution Level ◽  
Short Term ◽  
Migration Index ◽  
Long Run ◽  
Long Term Impact

Objectives: To evaluate the long- and short-term effects of air pollution on COVID-19 transmission simultaneously, especially in high air pollution level countries.Methods: Quasi-Poisson regression was applied to estimate the association between exposure to air pollution and daily new confirmed cases of COVID-19, with mutual adjustment for long- and short-term air quality index (AQI). The independent effects were also estimated and compared. We further assessed the modification effect of within-city migration (WM) index to the associations.Results: We found a significant 1.61% (95%CI: 0.51%, 2.72%) and 0.35% (95%CI: 0.24%, 0.46%) increase in daily confirmed cases per 1 unit increase in long- and short-term AQI. Higher estimates were observed for long-term impact. The stratifying result showed that the association was significant when the within-city migration index was low. A 1.25% (95%CI: 0.0.04%, 2.47%) and 0.41% (95%CI: 0.30%, 0.52%) increase for long- and short-term effect respectively in low within-city migration index was observed.Conclusions: There existed positive associations between long- and short-term AQI and COVID-19 transmission, and within-city migration index modified the association. Our findings will be of strategic significance for long-run COVID-19 control.

SHORT-TERM AND LONG-TERM EFFECTS OF AIR POLLUTION ON LUNG FUNCTION

Epidemiology ◽  
2004 ◽  
Vol 15 (4) ◽  
pp. S42 ◽  
Author(s):  
Heike Luttmann-Gibson ◽  
Douglas W. Dockery ◽  
Frank E. Speizer
Keyword(s):  
Air Pollution ◽  
Lung Function ◽  
Long Term Effects ◽  
Short Term

Air pollution, mortality, at-risk population, new entry and life expectancy of the frail elderly in three U.S. cities

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Christian J. Murray ◽  
Frederick W. Lipfert
Keyword(s):  
At Risk ◽  
Air Pollution ◽  
Time Series ◽  
Air Quality ◽  
Life Expectancy ◽  
Short Term ◽  
Pollution Effects ◽  
Risk Population ◽  
Air Pollution Effects ◽  
New Entrants

AbstractWe present the findings of a new time-series model that estimates short-term health effects of particulate matter and ozone, as applied to three U.S. cities. The model is based on observed fluctuations of daily death counts and estimates the corresponding daily subpopulations at-risk of imminent death; it also shows that virtually all elderly deaths are preceded by a brief period of extreme frailty. We augment previous research by allowing new entrants to this at-risk population to be influenced by the environment, rather than be random. The mean frail subpopulations in the three cities, each containing between 3000 and 5000 daily observations on mortality, pollution, and temperature, are estimated to be about 0.1% of those aged 65 or more, and their life expectancies in this frail status are about one week. We find losses in life expectancy due to air pollution and temperature to be at most one day. Air pollution effects on new entrants into the frail population tend to exceed those on mortality. Our results provide context to the many time-series studies that have found significant short-term relationships between air quality and survival, and they suggest that benefits of air quality improvement should be based on increased life expectancy rather than estimated numbers of excess deaths.

scholarly journals How can we improve air pollution? Try increasing trust first

2021 ◽  
pp. 1-21
Author(s):  
Bridget Lynn Hoffmann ◽  
Carlos Scartascini ◽  
Fernando G. Cafferata
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Survey Data ◽  
Mexico City ◽  
Environmental Policies ◽  
Short Term ◽  
The Government ◽  
Polluting Firms

Abstract Environmental policies are characterized by salient short-term costs and long-term benefits that are difficult to observe and to attribute to the government's efforts. These characteristics imply that citizens’ support for environmental policies is highly dependent on their trust in the government's capability to implement solutions and commitment to investments in those policies. Using novel survey data from Mexico City, we show that trust in the government is positively correlated with citizens’ willingness to support an additional tax approximately equal to a day's minimum wage to improve air quality and greater preference for government retention of revenues from fees collected from polluting firms. We find similar correlations using the perceived quality of public goods as a measure of government competence. These results provide evidence that mistrust can be an obstacle to better environmental outcomes.

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