The health effects of air pollution have been subject to intense study in recent decades. Exposure to pollutants such as airborne particulate matter and ozone has been associated with increases in morbidity and mortality, especially with regards to respiratory and cardiovascular diseases. Unfortunately, individuals do not have readily accessible methods by which to track their exposure to pollution. This paper proposes how pollution parameters like CO, NO2, O3, PM2.5, PM10 and SO2 can be monitored for respiratory and cardiovascular personalized health during outdoor exercise events. Using location tracked activities, we synchronize them to public data sets of pollution sensors. For improved accuracy in estimation, we use heart rate data to understand breathing volume mapped with the local air quality sensors via constant GPS tracking.
The cigarette equivalent is derived from both the pollution intake and the tidal volume, where depending on the percentage of each of the pollutant intake, the total summation gives the result. Here the cigarette equivalent is used to eliminate the confusion, by converting real-time air quality data from pollution levels into the equivalent number of cigarettes smoked over time. We use this for semantic understanding by the user. Since inhaling cigarette smoke has been shown to produce acute changes in the lung including alterations in resistance to airflow, cough, and irritation of the airway, the early stage of smoking might affect the respiratory function, converting the air quality to this standard gives a better insight of how the pollution affects any individual @cite_11 @cite_8 .
@cite_8 @cite_11
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Abstract Background Although the health effects of long term exposure to air pollution are well established, it is difficult to effectively communicate the health risks of this (largely invisible) risk factor to the public and policy makers. The purpose of this study is to develop a method that expresses the health effects of air pollution in an equivalent number of daily passively smoked cigarettes. Methods Defined changes in PM2.5, nitrogen dioxide (NO 2 ) and Black Carbon (BC) concentration were expressed into number of passively smoked cigarettes, based on equivalent health risks for four outcome measures: Low Birth Weight ( 1 ), cardiovascular mortality and lung cancer. To describe the strength of the relationship with ETS and air pollutants, we summarized the epidemiological literature using published or new meta-analyses. Results Realistic increments of 10 µg m 3 in PM2.5 and NO 2 concentration and a 1 µg m 3 increment in BC concentration correspond to on average (standard error in parentheses) 5.5 (1.6), 2.5 (0.6) and 4.0 (1.2) passively smoked cigarettes per day across the four health endpoints, respectively. The uncertainty reflects differences in equivalence between the health endpoints and uncertainty in the concentration response functions. The health risk of living along a major freeway in Amsterdam is, compared to a counterfactual situation with ‘clean’ air, equivalent to 10 daily passively smoked cigarettes.. Conclusions We developed a method that expresses the health risks of air pollution and the health benefits of better air quality in a simple, appealing manner. The method can be used both at the national regional and the local level. Evaluation of the usefulness of the method as a communication tool is needed. Smoking is known to be one of the main causes for premature deaths. A reliable smoking detection method can enable applications for an insight into a user's smoking behaviour and for use in smoking cessation programs. However, it is difficult to accurately detect smoking because it can be performed in various postures or in combination with other activities, it is less-repetitive, and it may be confused with other similar activities, such as drinking and eating. In this paper, we propose to use a two-layer hierarchical smoking detection algorithm (HLSDA) that uses a classifier at the first layer, followed by a lazy context-rule-based correction method that utilizes neighbouring segments to improve the detection. We evaluated our algorithm on a dataset of 45 hours collected over a three month period where 11 participants performed 17 hours (230 cigarettes) of smoking while sitting, standing, walking, and in a group conversation. The rest of 28 hours consists of other similar activities, such as eating, and drinking. We show that our algorithm improves recall as well as precision for smoking compared to a single layer classification approach. For smoking activity, we achieve an F-measure of 90-97 in person-dependent evaluations and 83-94 in person-independent evaluations. In most cases, our algorithm corrects up to 50 of the misclassified smoking segments. Our algorithm also improves the detection of eating and drinking in a similar way. We make our dataset and data logger publicly available for the reproducibility of our work. Combustion emissions adversely impact air quality and human health. A multiscale air quality model is applied to assess the health impacts of major emissions sectors in United States. Emissions are classified according to six different sources: electric power generation, industry, commercial and residential sources, road transportation, marine transportation and rail transportation. Epidemiological evidence is used to relate long-term population exposure to sector-induced changes in the concentrations of PM2.5 and ozone to incidences of premature death. Total combustion emissions in the U.S. account for about 200,000 (90 CI: 90,000e362,000) premature deaths per year in the U.S. due to changes in PM2.5 concentrations, and about 10,000 (90 CI: " 1000 to 21,000) deaths due to changes in ozone concentrations. The largest contributors for both pollutant-related mortalities are road transportation, causing w53,000 (90 CI: 24,000e95,000) PM2.5-related deaths and w5000 (90 CI: " 900 to 11,000) ozonerelated early deaths per year, and power generation, causing w52,000 (90 CI: 23,000e94,000) PM2.5related and w2000 (90 CI: " 300 to 4000) ozone-related premature mortalities per year. Industrial emissions contribute to w41,000 (90 CI: 18,000e74,000) early deaths from PM2.5 and w2000 (90 CI: 0 e4000) early deaths from ozone. The results are indicative of the extent to which policy measures could be undertaken in order to mitigate the impact of specific emissions from different sectors d in particular black carbon emissions from road transportation and sulfur dioxide emissions from power generation. Abstract Traditional approaches to mechanical ventilation use tidal volumes of 10 to 15 ml per kilogram of body weight and may cause stretch-induced lung injury in patients with acute lung injury and the acute respiratory distress syndrome. We therefore conducted a trial to determine whether ventilation with lower tidal volumes would improve the clinical outcomes in these patients. Patients with acute lung injury and the acute respiratory distress syndrome were enrolled in a multicenter, randomized trial. The trial compared traditional ventilation treatment, which involved an initial tidal volume of 12 ml per kilogram of predicted body weight and an airway pressure measured after a 0.5-second pause at the end of inspiration (plateau pressure) of 50 cm of water or less, with ventilation with a lower tidal volume, which involved an initial tidal volume of 6 ml per kilogram of predicted body weight and a plateau pressure of 30 cm of water or less. The primary outcomes were death before a patient was discharged home and was breathing without assistance and the number of days without ventilator use from day 1 to day 28. The trial was stopped after the enrollment of 861 patients because mortality was lower in the group treated with lower tidal volumes than in the group treated with traditional tidal volumes (31.0 percent vs. 39.8 percent, P=0.007), and the number of days without ventilator use during the first 28 days after randomization was greater in this group (mean [+ -SD], 12+ -11 vs. 10+ -11; P=0.007). The mean tidal volumes on days 1 to 3 were 6.2+ -0.8 and 11.8+ -0.8 ml per kilogram of predicted body weight (P