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"Pollution studies"
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Temporal relationship between air pollutants and hospital admissions for chronic obstructive pulmonary disease in Hong Kong
Aims: To assess any relationship between the levels of ambient air pollutants and hospital admissions for chronic obstructive pulmonary disease (COPD) in Hong Kong. Methods: A retrospective ecological study was undertaken. Data of daily emergency hospital admissions to 15 major hospitals in Hong Kong for COPD and indices of air pollutants (sulphur dioxide (SO2), nitrogen dioxide (NO2), ozone (O3), particulates with an aerodynamic diameter of <10 μm (PM10) and 2.5 μm (PM2.5)) and meteorological variables from January 2000 to December 2004 were obtained from several government departments. Analysis was performed using generalised additive models with Poisson distribution, adjusted for the effects of time trend, season, other cyclical factors, temperature and humidity. Autocorrelation and overdispersion were corrected. Results: Significant associations were found between hospital admissions for COPD with all five air pollutants. Relative risks for admission for every 10 μg/m3 increase in SO2, NO2, O3, PM10 and PM2.5 were 1.007, 1.026, 1.034, 1.024 and 1.031, respectively, at a lag day ranging from lag 0 to cumulative lag 0–5. In a multipollutant model, O3, SO2 and PM2.5 were significantly associated with increased admissions for COPD. SO2, NO2 and O3 had a greater effect on COPD admissions in the cold season (December to March) than during the warm season. Conclusion: Ambient concentrations of air pollutants have an adverse effect on hospital admissions for COPD in Hong Kong, especially during the winter season. This might be due to indoor exposure to outdoor pollution through open windows as central heating is not required in the mild winter. Measures to improve air quality are urgently needed.
Journal Article
Improved 1 km resolution PM2.5 estimates across China using enhanced space–time extremely randomized trees
Fine particulate matter with aerodynamic diameters ≤2.5 µm (PM2.5) has adverse effects on human health and the atmospheric environment. The estimation of surface PM2.5 concentrations has made intensive use of satellite-derived aerosol products. However, it has been a great challenge to obtain high-quality and high-resolution PM2.5 data from both ground and satellite observations, which is essential to monitor air pollution over small-scale areas such as metropolitan regions. Here, the space–time extremely randomized trees (STET) model was enhanced by integrating updated spatiotemporal information and additional auxiliary data to improve the spatial resolution and overall accuracy of PM2.5 estimates across China. To this end, the newly released Moderate Resolution Imaging Spectroradiometer Multi-Angle Implementation of Atmospheric Correction AOD product, along with meteorological, topographical and land-use data and pollution emissions, was input to the STET model, and daily 1 km PM2.5 maps for 2018 covering mainland China were produced. The STET model performed well, with a high out-of-sample (out-of-station) cross-validation coefficient of determination (R2) of 0.89 (0.88), a low root-mean-square error of 10.33 (10.93) µg m-3, a small mean absolute error of 6.69 (7.15) µg m-3 and a small mean relative error of 21.28 % (23.69 %). In particular, the model captured well the PM2.5 concentrations at both regional and individual site scales. The North China Plain, the Sichuan Basin and Xinjiang Province always featured high PM2.5 pollution levels, especially in winter. The STET model outperformed most models presented in previous related studies, with a strong predictive power (e.g., monthly R2=0.80), which can be used to estimate historical PM2.5 records. More importantly, this study provides a new approach for obtaining high-resolution and high-quality PM2.5 dataset across mainland China (i.e., ChinaHighPM2.5), important for air pollution studies focused on urban areas.
Journal Article
How does Foreign Direct Investment Affect Pollution? Toward a Better Understanding of the Direct and Conditional Effects
This paper seeks to investigate the impact of foreign direct investments (FDIs) on industrial pollution (
CO
2
,
SO
2
,
NO
x
and BOD emissions) on a large sample of highly heterogeneous countries. By using panel data on manufacturing FDIs from France, Germany, Sweden, and the United Kingdom between 1995 and 2008, and by developing an empirical model with “first” and “second order” interaction terms, we investigate the existence and the conditionality of the most controversial FDI-induced effects on industrial emissions, i.e.,
Pollution Haven
,
Factor Endowments
and
Pollution Halo hypotheses
. The paper has three main findings: (1) the central hypotheses linking pollution to FDI are found to act simultaneously, with opposing effects; (2) FDIs are associated with pollution reduction, i.e.,
predominating pollution halo induced effect
, in countries with low to average capital-to-labour ratio but not too lax environmental regulation; (3) FDIs are found to increase pollution, i.e.,
prevailing pollution haven and/or factor endowments induced effects
, in countries with average capital endowments and lax environmental regulations, as well as in all the capital abundant countries, though with a smaller magnitude in countries having strict environmental regulations and/or a high-skilled labour force. Some specific and interesting findings are discussed regarding different FDI-origin countries and FDI-host country groups.
Journal Article
The drug company next door : pollution, jobs, and community health in Puerto Rico
\"This fascinating and most timely critical medical anthropology study successfully binds two still emergent areas of contemporary anthropological research in the global world: the nature and significant impact of multinational pharmaceutical manufacturers on human social life everywhere, and the contribution of corporations to the fast-paced degradation of our life support system, planet Earth. Focusing on a pharmaceutically-impacted town on the colonized island of Puerto Rico, Dietrich ably demonstrates the value of ethnography carried out in small places in framing the large issues facing humanity.\" --Merrill Singer, University of Connecticut The production of pharmaceuticals is among the most profitable industries on the planet. Drug companies produce chemical substances that can save, extend, or substantially improve the quality of human life.However, even as the companies present themselves publicly as health and environmental stewards, their factories are a significant source of air and water pollution--toxic to people and the environment. In Puerto Rico, the pharmaceutical industry is the backbone of the island's economy: in one small town alone, there are over a dozen drug factories representing five multinationals, the highest concentration per capita of such factories in the world. It is a place where the enforcement of environmental regulations and the public trust they ensure are often violated in the name of economic development. The Drug Company Next Door unites the concerns of critical medical anthropology with those of political ecology, investigating the multi-faceted role of pharmaceutical corporations as polluters, economic providers, and social actors.Rather than simply demonizing the drug companies, the volume explores the dynamics involved in their interactions with the local community and discusses the strategies used by both individuals and community groups to deal with the consequences of pollution. The Drug Company Next Door puts a human face on a growing set of problems for communities around the world.Accessible and engaging, the book encourages readers to think critically about the role of corporations in everyday life, health, and culture. Alexa S. Dietrich is Assistant Professor of Anthropology at Wagner College. \"-- Provided by publisher.
Book
Confounding by Socioeconomic Status in Epidemiological Studies of Air Pollution and Health: Challenges and Opportunities
Despite a vast air pollution epidemiology literature to date and the recognition that lower-socioeconomic status (SES) populations are often disproportionately exposed to pollution, there is little research identifying optimal means of adjusting for confounding by SES in air pollution epidemiology, nor is there a strong understanding of biases that may result from improper adjustment.
We aim to provide a conceptualization of SES and a review of approaches to its measurement in the U.S. context and discuss pathways by which SES may influence health and confound effects of air pollution. We explore bias related to measurement and operationalization and identify statistical approaches to reduce bias and confounding.
Drawing on the social epidemiology, health geography, and economic literatures, we describe how SES, a multifaceted construct operating through myriad pathways, may be conceptualized and operationalized in air pollution epidemiology studies. SES varies across individuals within the contexts of place, time, and culture. Although no single variable or index can fully capture SES, many studies rely on only a single measure. We recommend examining multiple facets of SES appropriate to the study design. Furthermore, investigators should carefully consider the multiple mechanisms by which SES might be operating to identify those SES indicators that may be most appropriate for a given context or study design and assess the impact of improper adjustment on air pollution effect estimates. Last, exploring model contraction and expansion methods may enrich adjustment, whereas statistical approaches, such as quantitative bias analysis, may be used to evaluate residual confounding. https://doi.org/10.1289/EHP7980.
Journal Article
Introduction to the special issue “In-depth study of air pollution sources and processes within Beijing and its surrounding region (APHH-Beijing)”
The Atmospheric Pollution and Human Health in a Chinese Megacity (APHH-Beijing) programme is an international collaborative project focusing on understanding the sources, processes and health effects of air pollution in the Beijing megacity. APHH-Beijing brings together leading China and UK research groups, state-of-the-art infrastructure and air quality models to work on four research themes: (1) sources and emissions of air pollutants; (2) atmospheric processes affecting urban air pollution; (3) air pollution exposure and health impacts; and (4) interventions and solutions. Themes 1 and 2 are closely integrated and support Theme 3, while Themes 1–3 provide scientific data for Theme 4 to develop cost-effective air pollution mitigation solutions. This paper provides an introduction to (i) the rationale of the APHH-Beijing programme and (ii) the measurement and modelling activities performed as part of it. In addition, this paper introduces the meteorology and air quality conditions during two joint intensive field campaigns – a core integration activity in APHH-Beijing. The coordinated campaigns provided observations of the atmospheric chemistry and physics at two sites: (i) the Institute of Atmospheric Physics in central Beijing and (ii) Pinggu in rural Beijing during 10 November–10 December 2016 (winter) and 21 May–22 June 2017 (summer). The campaigns were complemented by numerical modelling and automatic air quality and low-cost sensor observations in the Beijing megacity. In summary, the paper provides background information on the APHH-Beijing programme and sets the scene for more focused papers addressing specific aspects, processes and effects of air pollution in Beijing.
Journal Article
Methodological Considerations for Epidemiological Studies of Air Pollution and the SARS and COVID-19 Coronavirus Outbreaks
Studies have reported that ambient air pollution is associated with an increased risk of developing or dying from coronavirus-2 (COVID-19). Methodological approaches to investigate the health impacts of air pollution on epidemics should differ from those used for chronic diseases, but the methods used in these studies have not been appraised critically.
Our study aimed to identify and critique the methodological approaches of studies of air pollution on infections and mortality due to COVID-19 and to identify and critique the methodological approaches of similar studies concerning severe acute respiratory syndrome (SARS).
Published and unpublished papers of associations between air pollution and developing or dying from COVID-19 or SARS that were reported as of 10 May 2020 were identified through electronic databases, internet searches, and other sources.
All six COVID-19 studies and two of three SARS studies reported positive associations. Two were time series studies that estimated associations between daily changes in air pollution, one was a cohort that assessed associations between air pollution and the secondary spread of SARS, and six were ecological studies that used area-wide exposures and outcomes. Common shortcomings included possible cross-level bias in ecological studies, underreporting of health outcomes, using grouped data, the lack of highly spatially resolved air pollution measures, inadequate control for confounding and evaluation of effect modification, not accounting for regional variations in the timing of outbreaks' temporal changes in at-risk populations, and not accounting for nonindependence of outcomes.
Studies of air pollution and novel coronaviruses have relied mainly on ecological measures of exposures and outcomes and are susceptible to important sources of bias. Although longitudinal studies with individual-level data may be imperfect, they are needed to adequately address this topic. The complexities involved in these types of studies underscore the need for careful design and for peer review. https://doi.org/10.1289/EHP7411.
Journal Article