Synergistic effects of synoptic weather patterns and topography on air quality: a case of the Sichuan Basin of China

Ning, Guicai; Yim, Steve Hung Lam; Wang, Shigong; Duan, Bolong; Nie, Canqi; Yang, Xu; Wang, Jinyan; Shang, Kezheng

doi:10.1007/s00382-019-04954-3

Cited by 99 publications

(66 citation statements)

References 102 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, some specific synoptic patterns might also cause noticeable changes in both UHII and PM 2.5 . Synoptic patterns can affect air quality episodes which are already expressed through the meteorological variables (e.g., temperature, wind speed, rainfall, and cloud cover), and thus, these variables can represent a certain synoptic situation over a given region (Kassomenos et al, ; Luo et al, ; Miao et al, , ; Ning et al, , ; Shahgedanova et al, ; Wang, Liu, et al, ; Yang Yim et al, ; Zheng et al, ). The possibly different associations between the UHII and PM 2.5 under different synoptic patterns will be the focus of a future study.…”

Section: Discussionmentioning

confidence: 99%

PM_2.5 Pollution Modulates Wintertime Urban Heat Island Intensity in the Beijing‐Tianjin‐Hebei Megalopolis, China

Yang

Zheng

Yim

et al. 2020

Geophysical Research Letters

Self Cite

109

View full text Add to dashboard Cite

Heavy PM2.5 (particulate matter with aerodynamic diameter equal to or less than 2.5 μm) pollution and urban heat island (UHI) pose increasing threats to human health and living environment in populated cities. However, how PM2.5 pollution affects the UHI intensity (UHII) has not been fully understood. The impacts of PM2.5 on the wintertime UHII in the Beijing‐Tianjin‐Hebei megalopolis of China are explored during 2013–2017. The results show that the UHII at the time of daily maximum/minimum temperature (UHIImax/UHIImin) exhibits a decreasing/increasing tendency as PM2.5 concentration increases, causing a continuous decrease in the diurnal temperature range. These effects are mediated via aerosol‐radiation interaction (aerosol‐cloud interaction) under clear‐sky (cloudy) condition. The changes in PM2.5 concentration further cause different relative trends of UHIImax/UHIImin/diurnal temperature range across different cities in the Beijing‐Tianjin‐Hebei region, which are likely related to the differences in both the PM2.5 composition and city size. This study provides insights on how air pollution affects urban climate and would help to design effective mitigation strategies.

show abstract

Section: Discussionmentioning

confidence: 99%

PM_2.5 Pollution Modulates Wintertime Urban Heat Island Intensity in the Beijing‐Tianjin‐Hebei Megalopolis, China

Yang

Zheng

Yim

et al. 2020

Geophysical Research Letters

Self Cite

109

View full text Add to dashboard Cite

show abstract

“…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: 88%

“…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 PM<sub>2.5</sub> and AOD in China: Influencing factors and Implications for satellite PM<sub>2.5</sub> estimations by MAIAC AOD

Wang

Yim

2021

Preprint

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…Based on the T-PCA and the explained cluster variance (Hoffmann & HeinkeSchlüNzen, 2013;Ning et al, 2019;Philipp et al, 2014) methods, four different types of meteorological situations were selected. For each of the four types of weather situations, the SLP and wind components over the study area were calculated with the COST 733 toolbox.…”

Section: Synoptic Classification and Corresponding Pm 25 Distributionmentioning

confidence: 99%

Establishment of Conceptual Schemas of Surface Synoptic Meteorological Situations Affecting Fine Particulate Pollution Across Eastern China in the Winter

et al. 2020

View full text Add to dashboard Cite

In the present study, the characteristics of weather conditions and local meteorological variables over the Beijing‐Tianjin‐Hebei (BTH) and the Yangtze River Delta (YRD) regions in the winter are analyzed using the principal component analysis (PCA) method and daily PM2.5 accumulation rate. Typical synoptic weather patterns over China in the winter can be classified into four types. During the Type 1 synoptic weather pattern, China is under the influence of the Siberian High, and northerly winds prevail; this situation is beneficial to the transport of pollutants from north to south. However, when the Siberian High is weak, southerly winds prevail which may result in the transport of pollutants from south to north. The Type 2 weather pattern refers to a weak high pressure located in the BTH resulting in the accumulation of pollutants. During the Type 3 weather pattern, an intense cold Siberian High moves to the south and affects the northern areas of China. The associated front brings heavy precipitation in the YRD resulting in the wet deposition of pollution. During the Type 4 weather condition, the weak Siberian High is blocked by the northeast cold vortex and moves towards the south, causing the accumulation of pollution in the YRD. The PCA model shows that there are two transport pathways for pollutants to the BTH (YRD) area: one from the YRD (BTH) and Shandong during Types 1 and 2 (Types 1 and 3) situations and the other one from the central provinces during Type 4 (Types 1 and 4).

show abstract

Synergistic effects of synoptic weather patterns and topography on air quality: a case of the Sichuan Basin of China

Cited by 99 publications

References 102 publications

PM_2.5 Pollution Modulates Wintertime Urban Heat Island Intensity in the Beijing‐Tianjin‐Hebei Megalopolis, China

PM_2.5 Pollution Modulates Wintertime Urban Heat Island Intensity in the Beijing‐Tianjin‐Hebei Megalopolis, China

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

Establishment of Conceptual Schemas of Surface Synoptic Meteorological Situations Affecting Fine Particulate Pollution Across Eastern China in the Winter

Contact Info

Product

Resources

About

Synergistic effects of synoptic weather patterns and topography on air quality: a case of the Sichuan Basin of China

Cited by 99 publications

References 102 publications

PM2.5 Pollution Modulates Wintertime Urban Heat Island Intensity in the Beijing‐Tianjin‐Hebei Megalopolis, China

PM2.5 Pollution Modulates Wintertime Urban Heat Island Intensity in the Beijing‐Tianjin‐Hebei Megalopolis, China

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

Establishment of Conceptual Schemas of Surface Synoptic Meteorological Situations Affecting Fine Particulate Pollution Across Eastern China in the Winter

Contact Info

Product

Resources

About

PM_2.5 Pollution Modulates Wintertime Urban Heat Island Intensity in the Beijing‐Tianjin‐Hebei Megalopolis, China

PM_2.5 Pollution Modulates Wintertime Urban Heat Island Intensity in the Beijing‐Tianjin‐Hebei Megalopolis, China

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