Exposure assessment of emissions from mobile food carts on New York City streets.

Food carts are common along streets in cities throughout the world. In North America, food cart vendors generally use propane, charcoal, or both propane and charcoal (P and C) for food preparation. Although cooking emissions are known to be a major source of indoor air pollution, there is limited knowledge on outdoor cooking's impact on the ambient environment and, in particular, the relative contribution of the different cooking fuels. This field study investigated the air pollution the public is exposed to in the micro-environment around 19 food carts classified into 3 groups: propane, charcoal, and P and C carts. Concentrations near the food carts were measured using both real-time and filter-based methods. Mean real-time concentrations of PM2.5, BC2.5, and particle counts were highest near the charcoal food carts: 196 µg/m3, 5.49 µg/m3, and 69,000 particles/cm3, respectively, with peak exposures of 1520 µg/m3, 67.9 µg/m3, and 235,000 particles/cm3, respectively. In order of pollution emission impacts: charcoal > P and C > propane carts. Thus, significant differences in air pollution emissions occurred in the vicinity of mobile food carts, depending on the fuel used in food preparation. Local air pollution polices should consider these emission factors in regulating food cart vendor operations.

Air Pollution Monitoring for Health Research and Patient Care. An Official American Thoracic Society Workshop Report.

Air quality data from satellites and low-cost sensor systems, together with output from air quality models, have the potential to augment high-quality, regulatory-grade data in countries with in situ monitoring networks and provide much-needed air quality information in countries without them. Each of these technologies has strengths and limitations that need to be considered when integrating them to develop a robust and diverse global air quality monitoring network. To address these issues, the American Thoracic Society, the U.S. Environmental Protection Agency, the National Aeronautics and Space Administration, and the National Institute of Environmental Health Sciences convened a workshop in May 2017 to bring together global experts from across multiple disciplines and agencies to discuss current and near-term capabilities to monitor global air pollution. The participants focused on four topics: 1) current and near-term capabilities in air pollution monitoring, 2) data assimilation from multiple technology platforms, 3) critical issues for air pollution monitoring in regions without a regulatory-quality stationary monitoring network, and 4) risk communication and health messaging. Recommendations for research and improved use were identified during the workshop, including a recognition that the integration of data across monitoring technology groups is critical to maximizing the effectiveness (e.g., data accuracy, as well as spatial and temporal coverage) of these monitoring technologies. Taken together, these recommendations will advance the development of a global air quality monitoring network that takes advantage of emerging technologies to ensure the availability of free, accessible, and reliable air pollution data and forecasts to health professionals, as well as to all global citizens.

Mapping urban air quality using mobile sampling with low-cost sensors and machine learning in Seoul, South Korea.

Recent studies have demonstrated that mobile sampling can improve the spatial granularity of land use regression (LUR) models. Mobile sampling campaigns deploying low-cost (<$300) air quality sensors could potentially offer an inexpensive and practical approach to measure and model air pollution concentration levels. In this study, we developed LUR models for street-level fine particulate matter (PM2.5) concentration levels in Seoul, South Korea. 169 h of data were collected from an approximately three week long campaign across five routes by ten volunteers sharing seven AirBeams, a low-cost ($250 per unit), smartphone-based particle counter, while geospatial data were extracted from OpenStreetMap, an open-source and crowd-generated geographical dataset. We applied and compared three statistical approaches in constructing the LUR models - linear regression (LR), random forest (RF), and stacked ensemble (SE) combining multiple machine learning algorithms - which resulted in cross-validation R2 values of 0.63, 0.73, and 0.80, respectively, and identification of several pollution 'hotspots.' The high R2 values suggest that study designs employing mobile sampling in conjunction with multiple low-cost air quality monitors could be applied to characterize urban street-level air quality with high spatial resolution, and that machine learning models could further improve model performance. Given this study design's cost-effectiveness and ease of implementation, similar approaches may be especially suitable for citizen science and community-based endeavors, or in regions bereft of air quality data and preexisting air monitoring networks, such as developing countries.

Global estimates of mortality associated with long-term exposure to outdoor fine particulate matter.

Exposure to ambient fine particulate matter (PM2.5) is a major global health concern. Quantitative estimates of attributable mortality are based on disease-specific hazard ratio models that incorporate risk information from multiple PM2.5 sources (outdoor and indoor air pollution from use of solid fuels and secondhand and active smoking), requiring assumptions about equivalent exposure and toxicity. We relax these contentious assumptions by constructing a PM2.5-mortality hazard ratio function based only on cohort studies of outdoor air pollution that covers the global exposure range. We modeled the shape of the association between PM2.5 and nonaccidental mortality using data from 41 cohorts from 16 countries-the Global Exposure Mortality Model (GEMM). We then constructed GEMMs for five specific causes of death examined by the global burden of disease (GBD). The GEMM predicts 8.9 million [95% confidence interval (CI): 7.5-10.3] deaths in 2015, a figure 30% larger than that predicted by the sum of deaths among the five specific causes (6.9; 95% CI: 4.9-8.5) and 120% larger than the risk function used in the GBD (4.0; 95% CI: 3.3-4.8). Differences between the GEMM and GBD risk functions are larger for a 20% reduction in concentrations, with the GEMM predicting 220% higher excess deaths. These results suggest that PM2.5 exposure may be related to additional causes of death than the five considered by the GBD and that incorporation of risk information from other, nonoutdoor, particle sources leads to underestimation of disease burden, especially at higher concentrations.

Climate change. A global threat to cardiopulmonary health.

Recent changes in the global climate system have resulted in excess mortality and morbidity, particularly among susceptible individuals with preexisting cardiopulmonary disease. These weather patterns are projected to continue and intensify as a result of rising CO2 levels, according to the most recent projections by climate scientists. In this Pulmonary Perspective, motivated by the American Thoracic Society Committees on Environmental Health Policy and International Health, we review the global human health consequences of projected changes in climate for which there is a high level of confidence and scientific evidence of health effects, with a focus on cardiopulmonary health. We discuss how many of the climate-related health effects will disproportionally affect people from economically disadvantaged parts of the world, who contribute relatively little to CO2 emissions. Last, we discuss the financial implications of climate change solutions from a public health perspective and argue for a harmonized approach to clean air and climate change policies.

Exposure assessment for estimation of the global burden of disease attributable to outdoor air pollution.

Ambient air pollution is associated with numerous adverse health impacts. Previous assessments of global attributable disease burden have been limited to urban areas or by coarse spatial resolution of concentration estimates. Recent developments in remote sensing, global chemical-transport models, and improvements in coverage of surface measurements facilitate virtually complete spatially resolved global air pollutant concentration estimates. We combined these data to generate global estimates of long-term average ambient concentrations of fine particles (PM(2.5)) and ozone at 0.1° × 0.1° spatial resolution for 1990 and 2005. In 2005, 89% of the world's population lived in areas where the World Health Organization Air Quality Guideline of 10 µg/m(3) PM(2.5) (annual average) was exceeded. Globally, 32% of the population lived in areas exceeding the WHO Level 1 Interim Target of 35 µg/m(3), driven by high proportions in East (76%) and South (26%) Asia. The highest seasonal ozone levels were found in North and Latin America, Europe, South and East Asia, and parts of Africa. Between 1990 and 2005 a 6% increase in global population-weighted PM(2.5) and a 1% decrease in global population-weighted ozone concentrations was apparent, highlighted by increased concentrations in East, South, and Southeast Asia and decreases in North America and Europe. Combined with spatially resolved population distributions, these estimates expand the evaluation of the global health burden associated with outdoor air pollution.

Use of health information in air pollution health research: past successes and emerging needs.

In September 2006, the US Environmental Protection Agency and the US Centers for Disease Control (CDC) co-organized a symposium on "Air Pollution Exposure and Health." The main objective of this symposium was to identify opportunities for improving the use of exposure and health information in future studies of air pollution health effects. This paper deals with the health information needs of such studies. We begin with a selected review of different types of health data and how they were used in previous epidemiologic studies of health effects of ambient particulate matter (PM). We then examine the current and emerging information needs of the environmental health community, dealing with PM and other air pollutants of health concern. We conclude that the past use of routinely collected health data proved to be essential for activities to protect public health, including the identification and evaluation of health hazards by air pollution research, setting standards for criteria pollutants, surveillance of health outcomes to identify incidence trends, and the more recent CDC environmental public health tracking program. Unfortunately, access to vital statistics records that have informed such pivotal research has recently been curtailed sharply, threatening the continuation of the type of research necessary to support future standard setting and research on emerging exposure and health problems (e.g. asthma, multiple sclerosis, diabetes, and others), as well as our ability to evaluate the efficacy of regulatory and other prevention activities. A comprehensive devoted effort, perhaps new legislation, will be needed to address the standardization, centralization, and sharing of data sets, as well as to harmonize the interpretation of confidentiality and privacy protections across jurisdictions. These actions, combined with assuring researchers and public health practitioners appropriate access to data for evaluation of environmental risks, will be essential for the achievement of our environmental health protection goals.