Impact of low-pressure systems on winter heavy air pollution in the northwest Sichuan Basin, China

Abstract

Abstract. The cities of Chengdu, Deyang, and Mianyang in the northwest Sichuan Basin are part of a rapidly developing urban agglomeration adjoining the eastern slopes of the Tibetan Plateau. Heavy air pollution events have frequently occurred over these cities in recent decades, but the effects of meteorological conditions on these pollution events are unclear. We explored the effects of weather systems on winter heavy air pollution from 1 January 2006 to 31 December 2012 and from 1 January 2014 to 28 February 2017. A total of 10 heavy air pollution events occurred during the research period and 8 of these took place while the region was affected by a dry low-pressure system at 700 hPa. When the urban agglomeration was in front of the low-pressure system and the weather conditions were controlled by a warm southerly air flow, a strong temperature inversion appeared above the atmospheric boundary layer acting as a lid. Forced by this strong inversion layer, the local secondary circulation was confined to the atmospheric boundary layer, and the horizontal wind speed in the lower troposphere was low. As a result, vertical mixing and horizontal dispersion in the atmosphere were poor, favoring the formation of heavy air pollution events. After the low-pressure system had transited over the region, the weather conditions in the urban agglomeration were controlled by a dry and cold air flow from the northwest at 700 hPa. The strong inversion layer gradually dissipated, the secondary circulation enhanced and uplifted, and the horizontal wind speed in the lower troposphere also increased, resulting in a sharp decrease in the concentration of air pollutants. The strong inversion layer above the atmospheric boundary layer induced by the low-pressure system at 700 hPa thus played a key role in the formation of heavy air pollution during the winter months in this urban agglomeration. This study provides scientific insights for forecasting heavy air pollution in this region of China.

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

“…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.…”

Interaction Between Planetary Boundary Layer and PM2.5 Pollution in Megacities in China: a Review

“…Comparing with the clean days, those days with heavy pollution in the SCB are typically characterized by low BLH and strong thermal inversion at 900-hPa level [24•]. The strong thermal inversion is often associated with the low-pressure system at 700-hPa level, induced by the dynamic and thermodynamic effects of the Tibetan Plateau [96]. Comparing with other flat regions, the air quality issue associated with thermal inversion is further aggravated in the SCB since the source area is topographically confined, and the inversion lid usually lies at a lower elevation than the basin sides.…”

Local and regional contributions to fine particulate matter in the 18 cities of Sichuan Basin, southwestern China

“…There are many types of source apportionment methods, such as receptor-based models, air parcel trajectory models, remote-sensing, and chemical transport models (CTMs). Receptor-based models, such as the Positive Matrix Factorization model (PMF) (Paatero and Tapper, 1994;Qiu et al, 2019), the Chemical Mass Balance model (CMB) (Watson et al, 1990), and a local con-tribution model proposed by Zhao et al (2019), are semiquantitative and cannot quantitatively determine the source contributions from an exact emission sector or a specific location. They also require a large number of monitoring data and can only resolve source contributions at the monitoring sites (Hopke, 2016).…”