Ground PM2.5 prediction using imputed MAIAC AOD with uncertainty quantification

Pu, Qiang; Yoo, Eun-Hye

doi:10.1016/j.envpol.2021.116574

Cited by 37 publications

(17 citation statements)

References 48 publications

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“…Another unique point of our study is that we have used complete and relatively accurate mobility data collected from thousands individuals during their daily life. As demonstrated in our comparison study, however, the effect of mobility on the measurement error of long-term air pollution exposure estimates is only pronounced when they are matched with spatially and temporally resolved PM 2.5 estimates [57]. The difference between residence-based and mobility-based exposure estimates have not been detected unless the spatial dynamics of ambient PM 2.5 were captured by the multi-sourced model.…”

Section: Discussionmentioning

confidence: 59%

“…During the study period, however, there were many days without sufficient MAIAC AOD retrievals due to cloud/snow presence. To address this missing AOD problem, we imputed the missing values by incorporating multiple proxy data available at coarse spatial resolutions, including Modern-Era Retrospective Analysis for Research and Applications version 2 and the CMAQ model outputs [57].…”

Section: Air Pollution Datamentioning

confidence: 99%

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The Impact of Individual Mobility on Long-Term Exposure to Ambient PM2.5: Assessing Effect Modification by Travel Patterns and Spatial Variability of PM2.5

Yoo

Eum

et al. 2021

IJERPH

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The impact of individuals’ mobility on the degree of error in estimates of exposure to ambient PM2.5 concentrations is increasingly reported in the literature. However, the degree to which accounting for mobility reduces error likely varies as a function of two related factors—individuals’ routine travel patterns and the local variations of air pollution fields. We investigated whether individuals’ routine travel patterns moderate the impact of mobility on individual long-term exposure assessment. Here, we have used real-world time–activity data collected from 2,013 participants in Erie/Niagara counties, New York, USA, matched with daily PM2.5 predictions obtained from two spatial exposure models. We further examined the role of the spatiotemporal representation of ambient PM2.5 as a second moderator in the relationship between an individual’s mobility and the exposure measurement error using a random effect model. We found that the effect of mobility on the long-term exposure estimates was significant, but that this effect was modified by individuals’ routine travel patterns. Further, this effect modification was pronounced when the local variations of ambient PM2.5 concentrations were captured from multiple sources of air pollution data (`a multi-sourced exposure model’). In contrast, the mobility effect and its modification were not detected when ambient PM2.5 concentration was estimated solely from sparse monitoring data (`a single-sourced exposure model’). This study showed that there was a significant association between individuals’ mobility and the long-term exposure measurement error. However, the effect could be modified by individuals’ routine travel patterns and the error-prone representation of spatiotemporal variability of PM2.5.

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Section: Discussionmentioning

confidence: 59%

Section: Air Pollution Datamentioning

confidence: 99%

The Impact of Individual Mobility on Long-Term Exposure to Ambient PM2.5: Assessing Effect Modification by Travel Patterns and Spatial Variability of PM2.5

Yoo

Eum

et al. 2021

IJERPH

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“…Specifically, the station density is high in economically developed regions, but the density is low in less developed areas; in other words, monitoring stations are sparse and are focused mostly in discrete cities. Nevertheless, with the rapid advancement of remote sensing technologies, the gaps in the monitoring station distribution can be filled ( Pu & Yoo, 2021 ; Sun, Gong & Zhou, 2021 ). Satellite remote sensing provides an efficient method to rapidly and economically predict PM 2.5 concentrations by using aerosol optical depth (AOD) estimates in the areas devoid of monitoring stations, enabling the acquisition of surface PM 2.5 data on a large scale.…”

Section: Introductionmentioning

confidence: 99%

“…Given the release of the MCD19A2 product in 2018, it presently possible to refine the estimates of PM 2.5 . This product utilizes image-based processing in conjunction with time series analysis ( Lyapustin et al, 2011 ; Pu & Yoo, 2021 ). As a result, aerosol inversion and atmospheric correction can be performed on sparsely vegetated land and relatively bright surfaces.…”

Section: Introductionmentioning

confidence: 99%

“…This product has a high spatial resolution of 1 km, and high-quality AOD data at this resolution with great performance can better explain the aerosol distribution than similar data at a coarse spatial resolution. Therefore, the MCD19A2 product has been favored by many researchers ( Goldberg et al, 2019 ; Jia et al, 2020 ; Nabavi, Haimberger & Abbasi, 2019 ; Pu & Yoo, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

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Machine learning driven by environmental covariates to estimate high-resolution PM2.5 in data-poor regions

Jin

Ding

et al. 2022

PeerJ

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PM2.5, which refers to fine particles with an equivalent aerodynamic diameter of less than or equal to 2.5 µm, can not only affect air quality but also endanger public health. Nevertheless, the spatial distribution of PM2.5 is not well understood in data-poor regions where monitoring stations are scarce. Therefore, we constructed a random forest (RF) model and a bagging algorithm model based on ground-monitored PM2.5 data, aerosol optical depth (AOD) and meteorological data, and auxiliary geographical variables to accurately estimate the spatial distribution of PM2.5 concentrations in Xinjiang during 2015–2020 at a resolution of 1 km. Through 10-fold cross-validation (CV), the RF model and bagging algorithm model were verified and compared. The results showed the following: (1) The RF model achieved better model performance and thus can be used to estimate the PM2.5 concentration at a relatively high resolution. (2) The PM2.5 concentrations were high in southern Xinjiang and low in northern Xinjiang. The high values were concentrated mainly in the Tarim Basin, while most areas of northern Xinjiang maintained low PM2.5 levels year-round. (3) The PM2.5 values in Xinjiang showed significant seasonality, with the seasonally averaged concentrations decreasing as follows: winter (71.95 µg m−3) > spring (64.76 µg m−3) > autumn (46.01 µg m−3) > summer (43.40 µg m−3). Our model provides a way to monitor air quality in data-scarce places, thereby advancing efforts to achieve sustainable development in the future.

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Exploiting PSO-SVM and sample entropy in BEMD for the prediction of interval-valued time series and its application to daily PM2.5 concentration forecasting

et al. 2022

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Ground PM2.5 prediction using imputed MAIAC AOD with uncertainty quantification

Cited by 37 publications

References 48 publications

The Impact of Individual Mobility on Long-Term Exposure to Ambient PM2.5: Assessing Effect Modification by Travel Patterns and Spatial Variability of PM2.5

The Impact of Individual Mobility on Long-Term Exposure to Ambient PM2.5: Assessing Effect Modification by Travel Patterns and Spatial Variability of PM2.5

Machine learning driven by environmental covariates to estimate high-resolution PM2.5 in data-poor regions

Exploiting PSO-SVM and sample entropy in BEMD for the prediction of interval-valued time series and its application to daily PM2.5 concentration forecasting

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