A novel air pollution index based on the relative risk of daily mortality associated with short-term exposure to common air pollutants

Cairncross, Eugene; John, Juanette; Zunckel, Mark

doi:10.1016/j.atmosenv.2007.07.003

Cited by 179 publications

(91 citation statements)

References 29 publications

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“…Sensitivity analyses showed that the multi-city formulation was in good agreement with single-city epidemiologic effect estimates (Stieb et al, 2008). Other researchers have proposed similar multipollutant indexes using effect estimates from locally-conducted epidemiologic analyses to make the indexes more applicable to a specific area (Cairncross et al, 2007;Sicard et al, 2011Sicard et al, , 2012Wong et al, 2013).…”

Section: Metrics Based On Health Effectsmentioning

confidence: 84%

Evaluating the application of multipollutant exposure metrics in air pollution health studies

Oakes¹,

Baxter

Long

2014

Environment International

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a b s t r a c t a r t i c l e i n f oBackground: Health effects associated with air pollution are typically evaluated using a single pollutant approach, yet people are exposed to mixtures consisting of multiple pollutants that may have independent or combined effects on human health. Development of exposure metrics that represent the multipollutant environment is important to understand the impact of ambient air pollution on human health. Objectives: We reviewed existing multipollutant exposure metrics to evaluate how they can be applied to understand associations between air pollution and health effects. Methods: We conducted a literature search using both targeted search terms and a relational search in Web of Science and PubMed in April and December 2013. We focused on exposure metrics that are constructed from ambient pollutant concentrations and can be broadly applied to evaluate air pollution health effects. Results: Multipollutant exposure metrics were identified in 57 eligible studies. Metrics reviewed can be categorized into broad pollutant grouping paradigms based on: 1) source emissions and atmospheric processes or 2) common health outcomes. Discussion: When comparing metrics, it is apparent that no universal exposure metric exists; each type of metric addresses different research questions and provides unique information on human health effects. Key limitations of these metrics include the balance between complexity and simplicity as well as the lack of an existing "gold standard" for multipollutant health effects and exposure. Conclusions: Future work on characterizing multipollutant exposure error and joint effects will inform development of improved multipollutant metrics to advance air pollution health effects research and human health risk assessment.

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Section: Metrics Based On Health Effectsmentioning

confidence: 84%

Evaluating the application of multipollutant exposure metrics in air pollution health studies

Oakes¹,

Baxter

Long

2014

Environment International

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“…This study uses excessive risk of the pollutants (ER i ) given by Cairncross et al (2007), as shown in Eq. (2):…”

Section: Health-risk Associated With a Pollutantmentioning

confidence: 99%

Significance of PM2.5 Air Quality at the Indian Capital

Sahu

Kota

2017

Aerosol Air Qual. Res.

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In New Delhi, the capital city of India, concentrations of regulated air pollutants often exceed the Indian national ambient air quality standards (INAAQS). As the sources of these pollutants differ, it is of utmost priority to understand the most dangerous air pollutant to formulate better control strategies in the city. In this study, regulated air pollutant concentrations in New Delhi during 2011 to 2014 were collected. Compared to other pollutants, PM 2.5 concentrations exceeded the INAAQS quite often. While PM 2.5 exceeded INAAQS during 85% of the days, NO 2 , O 3 , CO and SO 2 exceeded only on 37, 14, 11 and 0% of the days, respectively. Using air quality index approach, the most dominant pollutant was identified as PM 2.5 , for 75 to 90% of the days. However, a seasonal variation in the percentage dominance of PM 2.5 was observed. For example, PM 2.5 was dominant during 95% of the winter and 68% of monsoon days. In addition to absolute concentrations, pollutants can also be ranked by studying their associated short term mortality impacts. However, such studies are rare in India. For the first time, the short term impact of PM 2.5 concentrations on non-disease specific mortality in New Delhi was assessed using Poisson regression models. Results indicated that the excessive risk associated with PM 2.5 estimated was 0.57, which was higher than the other regulated pollutants. This indicates a projected 6.2 and 6.5% decrease in mortality by meeting the PM 2.5 Indian standards and WHO set limits, respectively.

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“…So, the IAQI formula is purely based on piecewise linear function using pollutant concentration breakpoints. In this way, the total daily health impact is expected to be the sum of the values associated with each AP [20]. In addition, the measurement intervals of these six pollutants were not identical to each other in China.…”

Section: Air Quality Forecastingmentioning

confidence: 99%

Air quality forecasting based on cloud model granulation

Lin

Zhao

et al. 2018

J Wireless Com Network

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This paper proposes a novel algorithm based on cloud model granulation (CMG) for air quality forecasting. Through data exploration of three different types of monitoring localities in Wuhan City, the determinative pollutants were reduced to NO 2 , PM 10 , O 3 , and PM 25 for modeling. After iterative granulation of original time series, the concepts of cloud model were extracted for each granule from original data space to feature space. Then, the cloud model features of future granules were predicted in the new feature space. Finally, the value in the feature space is transformed into the solution in the concept space. In addition, this paper uses the grid search to optimize the parameters in all experiments. Compare with several machine learning approaches, considering the mean squared error, the results on composition model and direct model shows that the proposed algorithm has better in predicting both individual air quality index and air quality index. At ZKX locality, the CMG algorithm can achieve high accuracy 71.43% for prediction of air quality index class. The results show that this algorithm not only can simplify the modeling process of uncertain time series in the form of knowledge abstraction, but also has good prediction performance in IAQI and AQI.

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A novel air pollution index based on the relative risk of daily mortality associated with short-term exposure to common air pollutants

Cited by 179 publications

References 29 publications

Evaluating the application of multipollutant exposure metrics in air pollution health studies

Evaluating the application of multipollutant exposure metrics in air pollution health studies

Significance of PM2.5 Air Quality at the Indian Capital

Air quality forecasting based on cloud model granulation

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