Explore the vast range of titles available.

The ocean is a reservoir for CFC-11, a major ozone-depleting chemical. Anthropogenic production of CFC-11 dramatically decreased in the 1990s under the Montreal Protocol, which stipulated a global phase out of production by 2010. However, studies raise questions about current overall emission levels and indicate unexpected increases of CFC-11 emissions of about 10 Gg · yr−1 after 2013 (based upon measured atmospheric concentrations and an assumed atmospheric lifetime). These findings heighten the need to understand processes that could affect the CFC-11 lifetime, including ocean fluxes. We evaluate how ocean uptake and release through 2300 affects CFC-11 lifetimes, emission estimates, and the long-term return of CFC-11 from the ocean reservoir. We show that ocean uptake yields a shorter total lifetime and larger inferred emission of atmospheric CFC-11 from 1930 to 2075 compared to estimates using only atmospheric processes. Ocean flux changes over time result in small but not completely negligible effects on the calculated unexpected emissions change (decreasing it by 0.4 ± 0.3 Gg · yr−1). Moreover, it is expected that the ocean will eventually become a source of CFC-11, increasing its total lifetime thereafter. Ocean outgassing should produce detectable increases in global atmospheric CFC-11 abundances by the mid-2100s, with emission of around 0.5 Gg · yr−1; this should not be confused with illicit production at that time. An illustrative model projection suggests that climate change is expected to make the ocean a weaker reservoir for CFC-11, advancing the detectable change in the global atmospheric mixing ratio by about 5 yr.

Multivariate regression models for real-time coastal air quality forecasting were suggested from 18 to 27 March 2015, with a total of 15 kinds of hourly input data (three-hours-earlier data of PM and gas with meteorological parameters from Kangnung (Korea), associated with two-days-earlier data of PM and gas from Beijing (China)). Multiple correlation coefficients between the predicted and measured PM10, PM2.5, NO2, SO2, CO and O3 concentrations were 0.957, 0.906, 0.886, 0.795, 0.864 and 0.932 before the yellow sand event at Kangnung, 0.936, 0.982, 0.866, 0.917, 0.887 and 0.916 during the event and 0.919, 0.945, 0.902, 0.857, 0.887 and 0.892 after the event. As the significance levels (p) from multi-regression analyses were less than 0.001, all correlation coefficients were very significant. Partial correlation coefficients presenting the contribution of 15 input variables to 6 output variables using the models were presented for the three periods in detail. Scatter plots and their hourly distributions between the predicted and measured values showed the quite good accuracy of the modeling performance for the current time forecasting of six output values and their high applicability.

Ecologic models of influenza burden may be confounded by other exposures that share winter seasonality. We evaluated the effects of air pollution and other environmental exposures in ecologic models estimating influenza-associated hospitalizations. We linked hospitalization data, viral surveillance, and environmental data, including temperature, relative humidity, dew point, and fine particulate matter for 3 counties in Washington, USA, for 2001-2012. We used negative binomial regression models to estimate the incidence of influenza-associated respiratory and circulatory (RC) hospitalizations and to assess the effect of adjusting for environmental exposures on RC hospitalization estimates. The modeled overall incidence rate of influenza-associated RC hospitalizations was 31/100,000 person-years. The environmental parameters were statistically associated with RC hospitalizations but did not appreciably affect the event rate estimates. Modeled influenza-associated RC hospitalization rates were similar to published estimates, and inclusion of environmental covariates in the model did not have a clinically important effect on severe influenza estimates.

In the present work, we analyze the spatiotemporal dynamics of the kinetic wind energy with and without allowance for the kinetic energy of outliers. We first separated the contributions of the mean kinetic energy and the kinetic energy of the outliers and estimated the latter using robust parametric, semiparametric, and semi-nonparametric algorithms developed by the authors. The kinetic wind energy was estimated by the postprocessing of minisodar measurements of three wind velocity components and their variances in the lower 200 m layer of the atmosphere. By the outliers, we mean wind velocities, including wind gusts, the distribution of which deviates from the prior distribution of the majority of observations. A nonmonotonic increase in the kinetic energy of the outliers with sounding altitude was established. Physically, this can be explained by a nonmonotonic increase in the turbulent kinetic energy of local air vortices in the atmospheric boundary layer (ABL). The vertical extension of the outlier layers was of the order of 10–20 m.

Solutions of the pollution transport problem and its adjoint are used to monitor mean pollution concentration in an ecologically important zone. Four strategies of control over pollutants released into the atmosphere by industrial plants are suggested. They differ by the restrictions imposed on the emission rate of each plant. All the strategies use solutions of the adjoint transport problem and assure the fulfillment of the sanitary norm in the zone. A linear interpolation of these strategies also brings pollution level in the zone down to the sanitary norm. A method of detecting the plants violating the prescribed emission rates is also given. A simple example is given to illustrate the strategies suggested. [PUBLICATION ABSTRACT]

Health Impact Assessments (HIA) approach can be executed by calculating the attributable burden of disease. The most common indicators used in the HIA methodology are: premature mortality, morbidity, life-expectancy, and Disability Adjusted Life-Year (DALY). The term Disability Adjusted Life-Years (DALYs) indicates months/years lost due to a premature death or disability. The aim of the study was to present health benefits, expressed in terms of lower total mortality and cardio-respiratory hospitalization rates, due to a decreased particulate matter (PM) concentration in ambient air, in Silesian voivodeship. In this paper, results obtained from the APHEKOM (Improving Knowledge and Communication for Decision Making on Air Pollution and Health in Europe) project, which provided specialized HIA tools, useful for assessing health benefits resulting from reducing air pollution, were used. Both short-term and long-term exposure HIA tools were applied with regard to the appropriate data for Silesian voivodeship. Exposure data were obtained from the Regional Environmental Inspectorate in Katowice, while population and health data were obtained from the Central Statistical Office of Poland or from the Silesian Voivodeship Office, respectively. Health benefits that are related to an improvement of ambient air quality in Silesia region are similar to previous estimates obtained for Kraków city. The reduction of short-term exposure to PM10 by 5 μg/m3 results in a lower number of yearly non-external deaths (2.6-2.75 per 100 000 inhabitants). This effect was also shown to be similar in the city of Zabrze, as well as in the whole Silesia region. The Health Impact Assessments tools developed by the World Health Organization (WHO) can help public health experts make decisions in order to improve the health of populations living in particular regions of Europe.

Gaseous pollutants at the ground level seriously threaten the urban air quality environment and public health. There are few estimates of gaseous pollutants that are spatially and temporally resolved and continuous across China. This study takes advantage of big data and artificial-intelligence technologies to generate seamless daily maps of three major ambient pollutant gases, i.e., NO2, SO2, and CO, across China from 2013 to 2020 at a uniform spatial resolution of 10 km. Cross-validation between our estimates and ground observations illustrated a high data quality on a daily basis for surface NO2, SO2, and CO concentrations, with mean coefficients of determination (root-mean-square errors) of 0.84 (7.99 µg m−3), 0.84 (10.7 µg m−3), and 0.80 (0.29 mg m−3), respectively. We found that the COVID-19 lockdown had sustained impacts on gaseous pollutants, where surface CO recovered to its normal level in China on around the 34th day after the Lunar New Year, while surface SO2 and NO2 rebounded more than 2 times slower due to more CO emissions from residents' increased indoor cooking and atmospheric oxidation capacity. Surface NO2, SO2, and CO reached their peak annual concentrations of 21.3 ± 8.8 µg m−3, 23.1 ± 13.3 µg m−3, and 1.01 ± 0.29 mg m−3 in 2013, then continuously declined over time by 12 %, 55 %, and 17 %, respectively, until 2020. The declining rates were more prominent from 2013 to 2017 due to the sharper reductions in anthropogenic emissions but have slowed down in recent years. Nevertheless, people still suffer from high-frequency risk exposure to surface NO2 in eastern China, while surface SO2 and CO have almost reached the World Health Organization (WHO) recommended short-term air quality guidelines (AQG) level since 2018, benefiting from the implemented stricter “ultra-low” emission standards. This reconstructed dataset of surface gaseous pollutants will benefit future (especially short-term) air pollution and environmental health-related studies.

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.

Globally, nearly 3 billion people rely on solid fuels for cooking and heating, the vast majority residing in low- and middle-income countries (LMICs). The resulting household air pollution (HAP) is a leading environmental risk factor, accounting for an estimated 1.6 million premature deaths annually. Previous interventions of cleaner stoves have often failed to reduce exposure to levels that produce meaningful health improvements. There have been no multicountry field trials with liquefied petroleum gas (LPG) stoves, likely the cleanest scalable intervention. This paper describes the design and methods of an ongoing randomized controlled trial (RCT) of LPG stove and fuel distribution in 3,200 households in 4 LMICs (India, Guatemala, Peru, and Rwanda). We are enrolling 800 pregnant women at each of the 4 international research centers from households using biomass fuels. We are randomly assigning households to receive LPG stoves, an 18-month supply of free LPG, and behavioral reinforcements to the control arm. The mother is being followed along with her child until the child is 1 year old. Older adult women (40 to of age) living in the same households are also enrolled and followed during the same period. Primary health outcomes are low birth weight, severe pneumonia incidence, stunting in the child, and high blood pressure (BP) in the older adult woman. Secondary health outcomes are also being assessed. We are assessing stove and fuel use, conducting repeated personal and kitchen exposure assessments of fine particulate matter with aerodynamic diameter ( ), carbon monoxide (CO), and black carbon (BC), and collecting dried blood spots (DBS) and urinary samples for biomarker analysis. Enrollment and data collection began in May 2018 and will continue through August 2021. The trial is registered with ClinicalTrials.gov (NCT02944682). This study will provide evidence to inform national and global policies on scaling up LPG stove use among vulnerable populations. https://doi.org/10.1289/EHP6407.

Poor air quality is thought to be an important mortality risk factor globally 1 – 3 , but there is little direct evidence from the developing world on how mortality risk varies with changing exposure to ambient particulate matter. Current global estimates apply exposure–response relationships that have been derived mostly from wealthy, mid-latitude countries to spatial population data 4 , and these estimates remain unvalidated across large portions of the globe. Here we combine household survey-based information on the location and timing of nearly 1 million births across sub-Saharan Africa with satellite-based estimates 5 of exposure to ambient respirable particulate matter with an aerodynamic diameter less than 2.5 μm (PM 2.5 ) to estimate the impact of air quality on mortality rates among infants in Africa. We find that a 10 μg m −3 increase in PM 2.5 concentration is associated with a 9% (95% confidence interval, 4–14%) rise in infant mortality across the dataset. This effect has not declined over the last 15 years and does not diminish with higher levels of household wealth. Our estimates suggest that PM 2.5 concentrations above minimum exposure levels were responsible for 22% (95% confidence interval, 9–35%) of infant deaths in our 30 study countries and led to 449,000 (95% confidence interval, 194,000–709,000) additional deaths of infants in 2015, an estimate that is more than three times higher than existing estimates that attribute death of infants to poor air quality for these countries 2 , 6 . Upward revision of disease-burden estimates in the studied countries in Africa alone would result in a doubling of current estimates of global deaths of infants that are associated with air pollution, and modest reductions in African PM 2.5 exposures are predicted to have health benefits to infants that are larger than most known health interventions. A 10 μg m −3 increase in the concentration of ambient respirable particulate matter is associated with a 9% rise in infant mortality in Africa and this increase is not affected by household wealth.