Advancing methodologies for applying machine learning and evaluating spatiotemporal models of fine particulate matter (PM2.5) using satellite data over large regions

Just, Allan C.; Arfer, Kodi B.; Rush, Johnathan; Dorman, Michael; Shtein, Alexandra; Lyapustin, Alexei; Kloog, Itai

doi:10.1016/j.atmosenv.2020.117649

Cited by 68 publications

(44 citation statements)

References 19 publications

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“…The accuracy of model performance depends on many factors, such as ML algorithms, spatial characteristics, prediction targets, temporal resolution, etc. Several authors have mentioned the structural limitations of algorithms, such as the tendency to overfit, complexity, difficulty with interpretation, and time-consuming [36][37][38]. Regarding the prediction target, depending on which pollutant is the prediction target the accuracy may vary since the chemical structure of the pollutants is different.…”

Section: Resultsmentioning

confidence: 99%

Features Exploration from Datasets Vision in Air Quality Prediction Domain

2021

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Air pollution and its consequences are negatively impacting on the world population and the environment, which converts the monitoring and forecasting air quality techniques as essential tools to combat this problem. To predict air quality with maximum accuracy, along with the implemented models and the quantity of the data, it is crucial also to consider the dataset types. This study selected a set of research works in the field of air quality prediction and is concentrated on the exploration of the datasets utilised in them. The most significant findings of this research work are: (1) meteorological datasets were used in 94.6% of the papers leaving behind the rest of the datasets with a big difference, which is complemented with others, such as temporal data, spatial data, and so on; (2) the usage of various datasets combinations has been commenced since 2009; and (3) the utilisation of open data have been started since 2012, 32.3% of the studies used open data, and 63.4% of the studies did not provide the data.

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Section: Resultsmentioning

confidence: 99%

Features Exploration from Datasets Vision in Air Quality Prediction Domain

2021

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“…Jensen et al, 2017, Singh et al, 2014, 2020a area. New approaches of artificial neural network models and machine learning have shown a more detailed representation of air quality in complex built-up areas (e.g., Wang et al, 2015b, Zhan et al, 2017, Just et al, 2020, Alimissis et al, 2018. CTMs have also been developed to improve spatial resolution, for example, through downscaling approaches for predicting air quality in urban areas, forecasting air quality and simulation of exposure at the street scale (Berrocal et al, 2020, Elessa Etuman et al, 2020.…”

Section: Air Quality Modellingmentioning

confidence: 99%

Advances in Air Quality Research – Current and Emerging Challenges

Sokhi

Moussiopoulos

Baklanov

et al. 2021

Preprint

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Abstract. This review provides a community’s perspective on air quality research focussing mainly on developments over the past decade. The article provides perspectives on current and future challenges as well as research needs for selected key topics. While this paper is not an exhaustive review of all research areas in the field of air quality, we have selected key topics that we feel are important from air quality research and policy perspectives. After providing a short historical overview, this review focuses on improvements in characterising sources and emissions of air pollution, new air quality observations and instrumentation, advances in air quality prediction and forecasting, understanding interactions of air quality with meteorology and climate, exposure and health assessment, and air quality management and policy. In conducting the review, specific objectives were (i) to address current developments that push the boundaries of air quality research forward, (ii) to highlight the emerging prominent gaps of knowledge in air quality research and (iii) and to make recommendations to guide the direction for future research within the wider community. This review also identifies areas of particular importance for air quality policy. The original concept of this review was borne at the International Conference on Air Quality 2020 (held online due to the COVID 19 restrictions during 18–26 May 2020), but the article incorporates a wider landscape of research literature within the field of air quality science. On air pollution emissions the review highlights, in particular, the need to reduce uncertainties in emissions from diffuse sources, particulate matter chemical components, shipping emissions and the importance of considering both indoor and outdoor sources. There is a growing need to have integrated air pollution and related observations from both ground based and remote sensing instruments, including especially those on satellites. The research should also capitalize on the growing area of lower cost sensors, while ensuring a quality of the measurements which are regulated by guidelines. Connecting various physical scales in air quality modelling is still a continual issue, with cities being affected by air pollution gradients at local scales and by long range transport. At the same time, one should allow for the impacts from climate change on a longer timescale. Earth system modelling offers considerable potential by providing a consistent framework for treating scales and processes, especially where there are significant feedbacks, such as those related to aerosols, chemistry and meteorology. Assessment of exposure to air pollution should consider both the impacts of indoor and outdoor emissions, as well as apply more sophisticated, dynamic modelling approaches. With particulate matter being one of the most important pollutants for health, research is indicating the urgent need to understand, in particular, the role of particle number and chemical components in terms of health impact, which in turn requires improved emission inventories and models for predicting high resolution distributions of these metrics over cities. The review also examines, how air pollution management needs to adapt to the above-mentioned new challenges and briefly considers the implications from the COVID-19 pandemic for air quality. Finally, we provide recommendations for air quality research and support for policy.

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“…Estimates of the “typical” air quality in a region, derived from fine‐resolution pollutant dispersion models, have been updated with low‐cost sensor data for near‐real‐time air quality mapping (Ahangar et al., 2019 ; Schneider et al., 2017 ). Regional‐scale atmospheric chemistry models have also been used together with MODIS AOD, surface‐level EPA monitoring data, and other information such as land usage and meteorology to produce daily‐average surface PM 2.5 estimates at 1‐km spatial resolution over the southeastern and eastern United States (Friberg et al., 2016 ; Goldberg et al., 2019 ; Just et al., 2020 ; Murray et al., 2019 ). These estimates were highly correlated with the EPA measurements during cross‐validation.…”

Section: Introductionmentioning

confidence: 99%

“…Similar approaches have been applied at a global scale for estimating annual‐average PM 2.5 concentration, although the accuracy of the method was regionally dependent (van Donkelaar et al., 2010 ; Shaddick et al., 2018 ). Much recent research has focused on 1‐km daily‐average surface PM 2.5 estimation combining similar data sources (Cleland et al., 2020 ; Danesh Yazdi et al., 2020 ; Just et al., 2020 ; Mhawish et al., 2020 ), with some research into hourly average concentration estimation (Jiang et al., 2021 ) and into forecasting daily averages (Zhang et al., 2020 ). Similar efforts include regional forecasting of coarse particulate matter (Michaelides et al., 2017 ) and global estimation of 8‐h maximum surface ozone concentrations (Chang et al., 2019 ) by combining model, satellite, and/or ground data.…”

Section: Introductionmentioning

confidence: 99%

“…Similar approaches have been applied at a global scale for estimating annual-average PM 2.5 concentration, although the accuracy of the method was regionally dependent (van Donkelaar et al, 2010;Shaddick et al, 2018). Much recent research has focused on 1-km daily-average surface PM 2.5 estimation combining similar data sources (Cleland et al, 2020;Danesh Yazdi et al, 2020;Just et al, 2020;Mhawish et al, 2020), with some research into hourly average concentration estimation (Jiang et al, 2021) and into forecasting daily averages (Zhang et al, 2020). Similar efforts include regional forecasting of coarse particulate matter (Michaelides et al, 2017) and global estimation of 8-h maximum surface ozone concentrations (Chang et al, 2019) Building on this previous work, this study proposes and demonstrates an approach for using globally available atmospheric composition historical estimates and forecasts and satellite information together with localized surface measurements for generating sub-city-scale and hourly resolution estimates and near-term forecasts up to 24 h in advance of surface-level pollutant concentrations relevant for air quality.…”

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confidence: 99%

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