Low-Concentration PM2.5 and Mortality: Estimating Acute and Chronic Effects in a Population-Based Study

Background: Both short- and long-term exposures to fine particulate matter (≤ 2.5 μm; PM2.5) are associated with mortality. However, whether the associations exist at levels below the new U.S. Environmental Protection Agency (EPA) standards (12 μg/m3 of annual average PM2.5, 35 μg/m3 daily) is unclear. In addition, it is not clear whether results from previous time series studies (fit in larger cities) and cohort studies (fit in convenience samples) are generalizable. Objectives: We estimated the effects of low-concentration PM2.5 on mortality. Methods: High resolution (1 km × 1 km) daily PM2.5 predictions, derived from satellite aerosol optical depth retrievals, were used. Poisson regressions were applied to a Medicare population (≥ 65 years of age) in New England to simultaneously estimate the acute and chronic effects of exposure to PM2.5, with mutual adjustment for short- and long-term exposure, as well as for area-based confounders. Models were also restricted to annual concentrations < 10 μg/m3 or daily concentrations < 30 μg/m3. Results: PM2.5 was associated with increased mortality. In the study cohort, 2.14% (95% CI: 1.38, 2.89%) and 7.52% (95% CI: 1.95, 13.40%) increases were estimated for each 10-μg/m3 increase in short- (2 day) and long-term (1 year) exposure, respectively. The associations held for analyses restricted to low-concentration PM2.5 exposure, and the corresponding estimates were 2.14% (95% CI: 1.34, 2.95%) and 9.28% (95% CI: 0.76, 18.52%). Penalized spline models of long-term exposure indicated a larger effect for mortality in association with exposures ≥ 6 μg/m3 versus those < 6 μg/m3. In contrast, the association between short-term exposure and mortality appeared to be linear across the entire exposure distribution. Conclusions: Using a mutually adjusted model, we estimated significant acute and chronic effects of PM2.5 exposure below the current U.S. EPA standards. These findings suggest that improving air quality with even lower PM2.5 than currently allowed by U.S. EPA standards may benefit public health. Citation Shi L, Zanobetti A, Kloog I, Coull BA, Koutrakis P, Melly SJ, Schwartz JD. 2016. Low-concentration PM2.5 and mortality: estimating acute and chronic effects in a population-based study. Environ Health Perspect 124:46–52; http://dx.doi.org/10.1289/ehp.140911

Rapid growth and high cloud-forming potential of anthropogenic sulfate aerosol in a thermal power plant plume during COVID lockdown in India

The COVID lockdown presented an interesting opportunity to study the anthropogenic emissions from different sectors under relatively cleaner conditions in India. The complex interplays of power production, industry, and transport could be dissected due to the significantly reduced influence of the latter two emission sources. Here, based on measurements of cloud condensation nuclei (CCN) activity and chemical composition of atmospheric aerosols during the lockdown, we report an episodic event resulting from distinct meteorological conditions. This event was marked by rapid growth and high hygroscopicity of new aerosol particles formed in the SO2 plume from a large coal-fired power plant in Southern India. These sulfate-rich particles had high CCN activity and number concentration, indicating high cloud-forming potential. Examining the sensitivity of CCN properties under relatively clean conditions provides important new clues to delineate the contributions of different anthropogenic emission sectors and further to understand their perturbations of past and future climate forcing

Tracking the impacts of climate change on human health via indicators: lessons from the Lancet Countdown

Background: In the past decades, climate change has been impacting human lives and health via extreme weather and climate events and alterations in labour capacity, food security, and the prevalence and geographical distribution of infectious diseases across the globe. Climate change and health indicators (CCHIs) are workable tools designed to capture the complex set of interdependent interactions through which climate change is affecting human health. Since 2015, a novel sub-set of CCHIs, focusing on climate change impacts, exposures, and vulnerability indicators (CCIEVIs) has been developed, refined, and integrated by Working Group 1 of the “Lancet Countdown: Tracking Progress on Health and Climate Change”, an international collaboration across disciplines that include climate, geography, epidemiology, occupation health, and economics. / Discussion: This research in practice article is a reflective narrative documenting how we have developed CCIEVIs as a discrete set of quantifiable indicators that are updated annually to provide the most recent picture of climate change’s impacts on human health. In our experience, the main challenge was to define globally relevant indicators that also have local relevance and as such can support decision making across multiple spatial scales. We found a hazard, exposure, and vulnerability framework to be effective in this regard. We here describe how we used such a framework to define CCIEVIs based on both data availability and the indicators’ relevance to climate change and human health. We also report on how CCIEVIs have been improved and added to, detailing the underlying data and methods, and in doing so provide the defining quality criteria for Lancet Countdown CCIEVIs. / Conclusions: Our experience shows that CCIEVIs can effectively contribute to a world-wide monitoring system that aims to track, communicate, and harness evidence on climate-induced health impacts towards effective intervention strategies. An ongoing challenge is how to improve CCIEVIs so that the description of the linkages between climate change and human health can become more and more comprehensive

Tracking the impacts of climate change on human health via indicators: lessons from the Lancet Countdown

Background: In the past decades, climate change has been impacting human lives and health via extreme weather and climate events and alterations in labour capacity, food security, and the prevalence and geographical distribution of infectious diseases across the globe. Climate change and health indicators (CCHIs) are workable tools designed to capture the complex set of interdependent interactions through which climate change is affecting human health. Since 2015, a novel sub-set of CCHIs, focusing on climate change impacts, exposures, and vulnerability indicators (CCIEVIs) has been developed, refined, and integrated by Working Group 1 of the “Lancet Countdown: Tracking Progress on Health and Climate Change”, an international collaboration across disciplines that include climate, geography, epidemiology, occupation health, and economics. Discussion: This research in practice article is a reflective narrative documenting how we have developed CCIEVIs as a discrete set of quantifiable indicators that are updated annually to provide the most recent picture of climate change’s impacts on human health. In our experience, the main challenge was to define globally relevant indicators that also have local relevance and as such can support decision making across multiple spatial scales. We found a hazard, exposure, and vulnerability framework to be effective in this regard. We here describe how we used such a framework to define CCIEVIs based on both data availability and the indicators’ relevance to climate change and human health. We also report on how CCIEVIs have been improved and added to, detailing the underlying data and methods, and in doing so provide the defining quality criteria for Lancet Countdown CCIEVIs. Conclusions: Our experience shows that CCIEVIs can effectively contribute to a world-wide monitoring system that aims to track, communicate, and harness evidence on climate-induced health impacts towards effective intervention strategies. An ongoing challenge is how to improve CCIEVIs so that the description of the linkages between climate change and human health can become more and more comprehensive

The 2020 report of The Lancet Countdown on health and climate change: responding to converging crises

The Lancet Countdown is an international collaboration, established to provide an independent, global monitoring system dedicated to tracking the emerging health profile of the changing climate. The 2020 report presents 43 indicators across five sections: climate change impacts, exposures, and vulnerability; adaptation, planning, and resilience for health; mitigation actions and health co-benefits; economics and finance; and public and political engagement. This report represents the findings and consensus of the 35 leading academic institutions and UN agencies that make up the Lancet Countdown, and draws on the expertise of climate scientists, geographers, and engineers; of energy, food, and transport experts; and of economists, social and political scientists, data scientists, public health professionals, and doctors

Replication Data for "Impacts of temperature and its variability on mortality in New England"

Because of privacy issues, the Center for Medicare and Medicaid Services requires each investigator wishing to analyze their data to individually submit a request for the data. Consequently, the Medicare data are not included here. However, we have included our programs, the temperature data by ZIP code, the data of covariates that we used, and investigators can obtain the Medicare data from the Center

Estimating Health Effects of Temperature and pm2.5 Using Satellite-Retrieved High-Resolution Exposures

Human activities emit greenhouse gases (GHGs) and air pollutants, which would affect the environment and in turn affect human health. Accurate estimate of the health effects requires high resolution exposure data of environmental stressors, such as air temperature (Ta) and fine particulate matter (PM2.5). The availability of those exposure data, however, is usually limited by sparsely distributed ground-based monitoring network. Therefore, the first chapter estimates Ta at a fine scale on a daily basis by incorporating satellite-based remote sensing data. Satellite can provide a global daily estimate of 1 km × 1 km surface temperature (Ts), which is correlated with Ta. Hence, a statistical calibration approach between Ta and Ts was used to retrieve daily mean Ta at1 km resolution for the Southeastern United States for the years 2000 to 2014. The second chapter investigates the chronic effects of temperature and temperature variability on mortality in New England, by using the satellite-retrieved daily mean Ta estimated from previous studies similar to our first chapter. Our findings indicate that the variability of atmospheric temperature emerges as a key factor of the potential health impacts of climate change. The last chapter examines the association between low-concentration PM2.5 and mortality in New England, by using the satellite-retrieved PM2.5 estimates. Our findings suggest that adverse health effects occur at low levels of fine particles, even for levels not exceeding the newly revised EPA standards

Explosive transitions to synchronization in weighted static scale-free networks

The emergence of explosive synchronization transitions in networks of phase oscillators has become recently one of the most interesting topics. We simulate the Kuramoto model on top of a family of weighted static scale-free networks. It is found that when the strength of the network’s edge is linearly correlated with frequency gap of pair of oscillators at its ends, i.e., the microscopic correlation exponent β is equal to 1, the model with the degree distribution exponent γ > 3 undergoes a first-order phase transition, while the transition becomes second order at 2 < γ ≤ 3. We also find that in homogeneous networks (γ → ∞) the explosive synchronization is replaced by a continuous phase transition when the microscopic correlation exponent β is changed from positive to negative. This is a new discovery of explosive synchronization transitions in weighted complex networks, which provides a fresh angle and tool to understand this explosive behavior