Downward trend of NO2 in the urban areas of Beijing-Tianjin-Hebei region from 2014 to 2020: Comparison of satellite retrievals, ground observations, and emission inventories

Xu, Jing; Zhang, Ziyin; Zhao, Xiujuan; Cheng, Siyu

doi:10.1016/j.atmosenv.2022.119531

Cited by 8 publications

(2 citation statements)

References 47 publications

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“…Regarding the annual predictions, a noticeable decline was found for PM 2.5 year by year. It is expected that the air quality level in Beijing has become better in recent years [ 37 ]. Meanwhile, regarding NO 2 , the decline in annual mean concentrations was almost flat from 2015 to 2017, especially for PLS-OK and RF-OK predictions.…”

Section: Resultsmentioning

confidence: 99%

Comparison of Population-Weighted Exposure Estimates of Air Pollutants Based on Multiple Geostatistical Models in Beijing, China

Wu,

Xu,

Liu

et al. 2024

Toxics

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Various geostatistical models have been used in epidemiological research to evaluate ambient air pollutant exposures at a fine spatial scale. Few studies have investigated the performance of different exposure models on population-weighted exposure estimates and the resulting potential misclassification across various modeling approaches. This study developed spatial models for NO2 and PM2.5 and conducted exposure assessment in Beijing, China. It explored three spatial modeling approaches: variable dimension reduction, machine learning, and conventional linear regression. It compared their model performance by cross-validation (CV) and population-weighted exposure estimates. Specifically, partial least square (PLS) regression, random forests (RF), and supervised linear regression (SLR) models were developed based on an ordinary kriging (OK) framework for NO2 and PM2.5 in Beijing, China. The mean squared error-based R2 (R2mse) and root mean squared error (RMSE) in leave-one site-out cross-validation (LOOCV) were used to evaluate model performance. These models were used to predict the ambient exposure levels in the urban area and to estimate the misclassification of population-weighted exposure estimates in quartiles between them. The results showed that the PLS-OK models for NO2 and PM2.5, with the LOOCV R2mse of 0.82 and 0.81, respectively, outperformed the other models. The population-weighted exposure to NO2 estimated by the PLS-OK and RF-OK models exhibited the lowest misclassification in quartiles. For PM2.5, the estimates of potential misclassification were comparable across the three models. It indicated that the exposure misclassification made by choosing different modeling approaches should be carefully considered, and the resulting bias needs to be evaluated in epidemiological studies.

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Section: Resultsmentioning

confidence: 99%

Comparison of Population-Weighted Exposure Estimates of Air Pollutants Based on Multiple Geostatistical Models in Beijing, China

Wu,

Xu,

Liu

et al. 2024

Toxics

View full text Add to dashboard Cite

show abstract

“…This emission reduction occurred more sharply during P2 (− 6.1 %/year) than P1 (− 1.7 %/year). Xu et al (2023) and Zheng et al (2018) have reported a downward trend of NO x emissions in China. South Korea's a posteriori NO x emissions decreased by an average of 3.9 %/year over the study period, which is 18 % lower in 2021 compared to 2016.…”

Section: Annual Emissions and Trends After The Application Of Teamentioning

confidence: 99%

Development of Surface Observation-Based Two-Step Emissions Adjustment and its Application on Co, Nox, and So2 Emissions in China and South Korea

Kim,

Kim

et al. 2023

Preprint

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Fine particulate matter and ozone variability with regional and local meteorology in Beijing, China

Guha,

Zhang,

Kinney

et al. 2024

Atmospheric Environment

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Downward trend of NO2 in the urban areas of Beijing-Tianjin-Hebei region from 2014 to 2020: Comparison of satellite retrievals, ground observations, and emission inventories

Cited by 8 publications

References 47 publications

Comparison of Population-Weighted Exposure Estimates of Air Pollutants Based on Multiple Geostatistical Models in Beijing, China

Comparison of Population-Weighted Exposure Estimates of Air Pollutants Based on Multiple Geostatistical Models in Beijing, China

Development of Surface Observation-Based Two-Step Emissions Adjustment and its Application on Co, Nox, and So2 Emissions in China and South Korea

Fine particulate matter and ozone variability with regional and local meteorology in Beijing, China

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