Half the world’s population are exposed to increasing air pollution

Shaddick, Gavin; Thomas, Matthew L.; Mudu, Pierpaolo; Ruggeri, Giulia; Gumy, Sophie

doi:10.1038/s41612-020-0124-2

Cited by 295 publications

(159 citation statements)

References 23 publications

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“…More than 90% of the global population live in areas exceeding the World Health Organization global air quality guidelines (WHO AQGs) for particulate matter with diameter of less than 2.5 micrometres (PM 2.5 ), i.e. 10 μg/m 3 annual average (Shaddick et al 2020 ). In 2004, WHO and the World Bank initiated the first comprehensive evaluation of ambient air pollution in the global burden of disease (GBD) study (Ezzati et al 2004 ).…”

Section: Introductionmentioning

confidence: 99%

The role of burden of disease assessment in tracking progress towards achieving WHO global air quality guidelines

Evangelopoulos

Perez-Velasco

Walton

et al. 2020

Int J Public Health

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Objectives More than 90% of the global population live in areas exceeding the PM2.5 air quality guidelines (AQGs). We provide an overview of the ambient PM2.5-related burden of disease (BoD) studies along with scenario analysis in the framework of the WHO AQG update on the estimated reduction in the BoD if AQGs were achieved globally. Methods We reviewed the literature for large-scale studies for the BoD attributed to ambient PM2.5. Moreover, we used the latest WHO statistics to calculate the BoD at current levels and the scenarios of aligning with interim targets and AQG levels. Results The most recent BoD studies (2010 onwards) share a similar methodology, but there are differences in the input data which affect the estimates for attributable deaths (2.9–8.9 million deaths annually). Moreover, we found that if AQGs were achieved, the estimated BoD would be reduced by up to 50% in total deaths worldwide. Conclusions Understanding the BoD across countries, especially in those that do not align with the AQGs, is essential in order to inform actions to reduce air pollution globally.

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Section: Introductionmentioning

confidence: 99%

The role of burden of disease assessment in tracking progress towards achieving WHO global air quality guidelines

Evangelopoulos

Perez-Velasco

Walton

et al. 2020

Int J Public Health

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“…As indicated in the fifth assessment report by the Intergovernmental Panel on Climate Change (IPCC), climate change manifests in a global increase of air and sea temperature and results in the loss of polar snow and ice, sea level rise, as well as the increased occurrence of extreme weather events [1]. This is accompanied by a directly anthropogenically induced environmental change expressed, for example, by extensive deforestation [2,3], the land take of cities and settlements [4,5], and the increasing pollution of the environment [6][7][8]. The ramifications of these changes affect the livelihoods of humanity directly: Decreasing snow cover in the mountains threatens the tourism-based economies of regions and entire countries [9,10].…”

Section: The Need For Forecastsmentioning

confidence: 99%

“…Again, one reviewed study may employ more than one sensor, and since some sensors can produce multiple data types (e.g., MODIS optical and thermal), the total counts between sensors and sensor types differ slightly. Optical EO data was used 160 times, followed by SAR (17), thermal (10), passive microwave (9), and altimetry data (8). Gravimetry and scatterometer data have been used in one case each.…”

Section: Technical Aspectsmentioning

confidence: 99%

Forecasting Spatio-Temporal Dynamics on the Land Surface Using Earth Observation Data—A Review

Koehler

Kuenzer

2020

Remote Sensing

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Reliable forecasts on the impacts of global change on the land surface are vital to inform the actions of policy and decision makers to mitigate consequences and secure livelihoods. Geospatial Earth Observation (EO) data from remote sensing satellites has been collected continuously for 40 years and has the potential to facilitate the spatio-temporal forecasting of land surface dynamics. In this review we compiled 143 papers on EO-based forecasting of all aspects of the land surface published in 16 high-ranking remote sensing journals within the past decade. We analyzed the literature regarding research focus, the spatial scope of the study, the forecasting method applied, as well as the temporal and technical properties of the input data. We categorized the identified forecasting methods according to their temporal forecasting mechanism and the type of input data. Time-lagged regressions which are predominantly used for crop yield forecasting and approaches based on Markov Chains for future land use and land cover simulation are the most established methods. The use of external climate projections allows the forecasting of numerical land surface parameters up to one hundred years into the future, while auto-regressive time series modeling can account for intra-annual variances. Machine learning methods have been increasingly used in all categories and multivariate modeling that integrates multiple data sources appears to be more popular than univariate auto-regressive modeling despite the availability of continuously expanding time series data. Regardless of the method, reliable EO-based forecasting requires high-level remote sensing data products and the resulting computational demand appears to be the main reason that most forecasts are conducted only on a local scale. In the upcoming years, however, we expect this to change with further advances in the field of machine learning, the publication of new global datasets, and the further establishment of cloud computing for data processing.

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“…Air quality monitoring in many countries has shown that the levels of common pollutants have increased from 1980 until now. During 2010–2016, an estimated 55.3% of the global population were exposed to dangerous levels of air pollution ( Shaddick et al, 2020 ). Therefore, a major global health risk to the world’s population is air pollution, with more than 90% of people living in areas that do not meet the 2017 World Health Organization’s (WHO) recommended threshold of air quality levels ( Health Effects Institute, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

The performance of a modified EWMA control chart for monitoring autocorrelated PM2.5 and carbon monoxide air pollution data

Supharakonsakun

Areepong

Sukparungsee

2020

PeerJ

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PM2.5 (particulate matter less than or equal to 2.5 micron) is found in the air and comprises dust, dirt, soot, smoke, and liquid droplets. PM2.5 and carbon monoxide emissions can have a negative impact on humans and animals throughout the world. In this paper, we present the performance of a modified exponentially weighted moving average (modified EWMA) control chart to detect small changes when the observations are autocorrelated with exponential white noise through the average run length evaluated (ARLs) by explicit formulas. The accuracy of the solution was verified with a numerical integral equation method. The efficacy of the modified EWMA control chart to monitor PM2.5 and carbon monoxide air pollution data and compare its performance with the standard EWMA control chart. The results suggest that the modified EWMA control chart is far superior to the standard one.

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Half the world’s population are exposed to increasing air pollution

Cited by 295 publications

References 23 publications

The role of burden of disease assessment in tracking progress towards achieving WHO global air quality guidelines

The role of burden of disease assessment in tracking progress towards achieving WHO global air quality guidelines

Forecasting Spatio-Temporal Dynamics on the Land Surface Using Earth Observation Data—A Review

The performance of a modified EWMA control chart for monitoring autocorrelated PM2.5 and carbon monoxide air pollution data

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