Modulations of synoptic and climatic changes on ozone pollution and its health risks in mountain-basin areas

Ning, Guicai; Yim, Steve Hung Lam; Yang, Yibo; Gu, Yefu; Dong, Guang‐Hui

doi:10.1016/j.atmosenv.2020.117808

Cited by 27 publications

(25 citation statements)

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“…The largest correlation in Sichuan Basin was most likely due to its bowl topography. The surrounding terrain is not favorable for pollutant dispersion and thus causes pollutant acumination in the local environment (Ning et al, 2018;Ning et al, 2019;Ning et al, 2020). This explains a good agreement between groundlevel PM2.5 and total column AOD.…”

Section: Discussionmentioning

confidence: 87%

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The spatiotemporal relationship between PM2.5 and AOD in China: Influencing factors and Implications for satellite PM2.5 estimations by MAIAC AOD

Wang

Yim

2021

Preprint

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Abstract. Satellite aerosol retrievals have been a popular alternative to monitoring surface PM2.5 concentration due to its extensive spatial and temporal coverage. Satellite-derived PM2.5 estimation strongly relies on an accurate representation of the relationship between ground PM2.5 and satellite aerosol optical depth (AOD). Due to the limitation of satellite AOD data, most studies examined the relationship at a coarse-resolution (i.e., ≥ 10 km) scale; more effort is still needed to better understand the relationship between in-situ PM2.5 and AOD at finer spatial scales. While PM2.5 and AOD could have obvious temporal variations, few studies have examined the diurnal variation in their relationship. Considerable uncertainty therefore still exists in satellite-derived PM2.5 estimation due to these research gaps. Taking advantage of the newly released fine-spatial-resolution satellite AOD data derived by the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm and real-time ground aerosol and PM2.5 measurements, this study explicitly explored the relationship between PM2.5 and AOD and its plausible impact factors including meteorological parameters and topography in mainland China during 2019, at various spatial and temporal scales. Coefficient of variation, Pearson correlation coefficient and slope of linear regression model were used. Spatially, stronger correlations mainly occurred in northern and eastern China and linear slope in northern inland regions was on average larger than those in other areas. Temporally, the PM2.5-AOD correlation peaked in the noon and afternoon and reached the maximum in winter. Simultaneously considering relative humidity (RH) and planetary boundary layer height (PBLH) in the relationship can improve the correlation but the effect of RH and PBLH on the correlation varied spatially and temporally, both in strength and direction. In addition, the largest correlation occurred at 400–600 m primarily in basin terrain such as Sichuan Basin, Shanxi-Shaanxi Basins and Junggar Basin. MAIAC 1-km AOD can better represent the ground-level fine particulate matter in most domains with exceptions such as in very high terrain i.e. Tibetan Plateau and north-central China i.e. Qinghai and Gansu. Findings in this study have useful implications for satellite-based PM2.5 monitoring and will further inform the understanding of the aerosol variation and PM2.5 pollution status in mainland China.

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Section: Discussionmentioning

confidence: 87%

“…Moreover, the complex topography protects the basins from transboundary transport of air pollution (Ning et al, 2019;Ning et al, 2020). These two reasons contributed to the consistency between near-surface PM2.5 and column AOD.…”

Section: Discussionmentioning

confidence: 99%

The spatiotemporal relationship between PM2.5 and AOD in China: Influencing factors and Implications for satellite PM2.5 estimations by MAIAC AOD

Wang

Yim

2021

Preprint

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Section: Introductionmentioning

confidence: 99%

“…The application of established LUR models to areas outside the study area can reduce extra efforts to develop new models (Poplawski et al, 2009). To date, a few studies have evaluated the transferability of air pollution LUR models within a city and between cities or countries (Allen et al, 2011;Patton et al, 2015;Vienneau et al, 2010;Yang et al, 2020). Direct transferability refers to predictor variables and coefficients of LUR models both being transferred (Allen et al, 2011), whereas transferability with calibration means that model coefficients are calibrated using air pollutant measurements from the target areas (Yang et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

“…Direct transferability is more meaningful because it can be applied in areas without air quality measurements (Allen et al, 2011;Yang et al, 2020). Previous studies on the transferability of LUR models concluded that the predictive performances of LUR models from one area to another were not consistent, ranging from poor (Marcon et al, 2015) to relatively acceptable predictive accuracy (Poplawski et al, 2009;Wang et al, 2014). Therefore, more studies should be conducted to assess the transferability of air pollution LUR models.…”

Section: Introductionmentioning

confidence: 99%

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Development and intercity transferability of land-use regression models for predicting ambient PM10, PM2.5, NO2 and O3 concentrations in northern Taiwan

Chuang

et al. 2021

Atmos. Chem. Phys.

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Abstract. To provide long-term air pollutant exposure estimates for epidemiological studies, it is essential to test the feasibility of developing land-use regression (LUR) models using only routine air quality measurement data and to evaluate the transferability of LUR models between nearby cities. In this study, we developed and evaluated the intercity transferability of annual-average LUR models for ambient respirable suspended particulates (PM10), fine suspended particulates (PM2.5), nitrogen dioxide (NO2) and ozone (O3) in the Taipei–Keelung metropolitan area of northern Taiwan in 2019. Ambient PM10, PM2.5, NO2 and O3 measurements at 30 fixed-site stations were used as the dependent variables, and a total of 156 potential predictor variables in six categories (i.e., population density, road network, land-use type, normalized difference vegetation index, meteorology and elevation) were extracted using buffer spatial analysis. The LUR models were developed using the supervised forward linear regression approach. The LUR models for ambient PM10, PM2.5, NO2 and O3 achieved relatively high prediction performance, with R2 values of > 0.72 and leave-one-out cross-validation (LOOCV) R2 values of > 0.53. The intercity transferability of LUR models varied among the air pollutants, with transfer-predictive R2 values of > 0.62 for NO2 and < 0.56 for the other three pollutants. The LUR-model-based 500 m × 500 m spatial-distribution maps of these air pollutants illustrated pollution hot spots and the heterogeneity of population exposure, which provide valuable information for policymakers in designing effective air pollution control strategies. The LUR-model-based air pollution exposure estimates captured the spatial variability in exposure for participants in a cohort study. This study highlights that LUR models can be reasonably established upon a routine monitoring network, but there exist uncertainties when transferring LUR models between nearby cities. To the best of our knowledge, this study is the first to evaluate the intercity transferability of LUR models in Asia.

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The impact of COVID-19 lockdown on ozone budget and radical chemistry in a typical industrial city of China

Cui

Wang

2022

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Ozone (O 3 ) levels in East China suffered from rapid increases during the COVID-19 period. To clarify the reason for the O 3 increase, a continuous campaign was performed in a industrial city in North China Plain (NCP). Meanwhile, the machinelearning technique and the box model were employed to reveal the mechanisms of O 3 increase from the perspective of meteorology and photochemical process, respectively. The result suggested that the ambient O 3 level in Tangshan increased from 18.7 ± 4.63 to 45.6 ± 8.52 μg/m 3 (143%) after COVID-19 lockdown, and the emission reduction and meteorology contributed to 77% and 66% of this increment, respectively. The higher Wind speed (WS) coupled with regional transport played a significant role on O 3 increase (30.8 kg/s). The O 3 sensitivity verified that O 3 production was highly volatile organic compounds (VOC)-sensitive (Relative incremental reactivity (RIR): 0.75), while the NOx showed the negative impact on O 3 production in Tangshan (RIR:-0.59). It suggested that the control of VOCs rather than NOx might be more effective in reducing O 3 level in Tangshan because it was located on the VOC-limited regime. Besides, both of ozone formation potential (OFP) analysis and observation-based model (OBM) demonstrated that the alkenes (36.3 ppb) and anthropogenic oxygenated volatile organic compounds (OVOCs) (15.2 ppb) showed the higher OFP compared with other species, and their reactions released a large number of HO 2 and RO 2 radicals. Moreover, the concentrations of these species did not experience marked decreases after COVID-19 lockdown, which were major contributors to O 3 increase during this period.

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Modulations of synoptic and climatic changes on ozone pollution and its health risks in mountain-basin areas

Cited by 27 publications

References 70 publications

The spatiotemporal relationship between PM<sub>2.5</sub> and AOD in China: Influencing factors and Implications for satellite PM<sub>2.5</sub> estimations by MAIAC AOD

The spatiotemporal relationship between PM<sub>2.5</sub> and AOD in China: Influencing factors and Implications for satellite PM<sub>2.5</sub> estimations by MAIAC AOD

Development and intercity transferability of land-use regression models for predicting ambient PM<sub>10</sub>, PM<sub>2.5</sub>, NO<sub>2</sub> and O<sub>3</sub> concentrations in northern Taiwan

The impact of COVID-19 lockdown on ozone budget and radical chemistry in a typical industrial city of China

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Modulations of synoptic and climatic changes on ozone pollution and its health risks in mountain-basin areas

Cited by 27 publications

References 70 publications

The spatiotemporal relationship between PM&lt;sub&gt;2.5&lt;/sub&gt; and AOD in China: Influencing factors and Implications for satellite PM&lt;sub&gt;2.5&lt;/sub&gt; estimations by MAIAC AOD

The spatiotemporal relationship between PM&lt;sub&gt;2.5&lt;/sub&gt; and AOD in China: Influencing factors and Implications for satellite PM&lt;sub&gt;2.5&lt;/sub&gt; estimations by MAIAC AOD

Development and intercity transferability of land-use regression models for predicting ambient PM&lt;sub&gt;10&lt;/sub&gt;, PM&lt;sub&gt;2.5&lt;/sub&gt;, NO&lt;sub&gt;2&lt;/sub&gt; and O&lt;sub&gt;3&lt;/sub&gt; concentrations in northern Taiwan

The impact of COVID-19 lockdown on ozone budget and radical chemistry in a typical industrial city of China

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The spatiotemporal relationship between PM<sub>2.5</sub> and AOD in China: Influencing factors and Implications for satellite PM<sub>2.5</sub> estimations by MAIAC AOD

The spatiotemporal relationship between PM<sub>2.5</sub> and AOD in China: Influencing factors and Implications for satellite PM<sub>2.5</sub> estimations by MAIAC AOD

Development and intercity transferability of land-use regression models for predicting ambient PM<sub>10</sub>, PM<sub>2.5</sub>, NO<sub>2</sub> and O<sub>3</sub> concentrations in northern Taiwan