Quantitative evaluation of impacts of the steadiness and duration of urban surface wind patterns on air quality

• Published in: The Science of the total environment 2022-12, Vol.850, p.157957-157957, Article 157957
• Main Authors: Xie, Jielan, Sun, Tianle, Liu, Chanfang, Li, Lei, Xu, Xinqi, Miao, Shengjie, Lin, Liheng, Chen, Yaoyao, Fan, Shaojia
• Format: Article
• Language: English
• Subjects: air; air pollution; Air quality; China; environment; quantitative analysis; Quantitative effect; time series analysis; Urban wind patterns; wind direction; wind speed
• ISSN: 0048-9697; 1879-1026
• DOI: 10.1016/j.scitotenv.2022.157957

The complexity and heterogeneity of urban land surfaces result in inconsistencies in near-surface winds, which in turn influence the diffusion and dispersion of air pollutants. In this study, we classified urban surface wind fields, quantified their steadiness, duration, and influence on air quality using hourly wind observations from 50 meteorological stations, as well as hourly PM2.5 and NO2 concentrations from 18 monitoring stations during 2017–2018 in Shenzhen, a mega city in southern China. We found that the K-means clustering technique was reliable for distinguishing surface wind patterns within the city. Urban surface-wind patterns greatly affected pollutant concentrations. When dominated by calm, northerly wind, high PM2.5/NO2 concentration episodes occurred more frequently than those during other surface wind patterns. The urban surface transport index (USTI) was used to quantify the steadiness of surface wind classes. High pollutant concentrations were present during both high wind speed periods with a large USTI, indicating external pollutant transport, and during low wind speed periods with a small USTI, indicating pollutant accumulation. The threshold durations for surface wind fields (TDSWF) was proposed to quantify the impacts of surface wind persistence on air quality. We found that poor air quality occurred during the first several hours of a dominant wind pattern, indicating that transitions between wind patterns should be a particular focus when assessing air-quality deterioration. USTI and TDSWF are potentially applicable to other urban areas, owing to their clear definitions and simple calculation. In combination with wind speeds, these indices are likely to improve air quality forecasting and strategic decisions on air pollution emergencies, based on long time series of multiple wind and pollutant concentration observations. [Display omitted] •K-means is a reliable algorithm for classifying city-scale surface wind patterns.•USTI is proposed to quantify the impact of surface-wind steadiness on air quality.•TDSWF is proposed to quantify the impact of surface-wind duration on air quality.•USTI can help distinguish between external and local accumulation of air pollutant.•Poor air quality is prevalent during the initial hours of a new wind pattern.