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Synoptic Weather Patterns and Associated Air Pollution in Taiwan Aerosol and Air Quality Research, 19: 1139–1151, 2019 Copyright © Taiwan Association for Aerosol Research ISSN: 1680-8584 print / 2071-1409 online doi: 10.4209/aaqr.2018.09.0348 Synoptic Weather Patterns and Associated Air Pollution in Taiwan Chia-Hua Hsu, Fang-Yi Cheng* Department of Atmospheric Sciences, National Central University, Taoyuan 32001, Taiwan ABSTRACT In this study, cluster analysis is applied to the daily averaged wind fields and sea-level pressure observed at surface weather stations in Taiwan from January 2013 to March 2018 to classify the synoptic weather patterns and study the characteristics of corresponding air pollutants, including fine particulate matter (PM2.5), coarse particulate matter (PM10), and ozone (O3). The classification identified six weather types: Clusters 1, 2, and 3 (C1–C3), which are typical winter weather types and associated with high air pollutant concentrations—C3, in particular, influenced by weak synoptic weather, is associated with the lowest wind speeds and the highest PM2.5 and PM10 concentrations and represents the most prevalent weather type that is prone to the occurrence of PM2.5 events; C4, which occurs mostly during seasonal transition months and is associated with the highest O3 concentrations; and C5 and C6, which are summer weather types with low air pollutant concentrations. Further analysis of the local wind flow using the 0.3° ERA5 reanalysis dataset and surface-observed wind data indicates that in western Taiwan, the land-sea breeze is embedded within the synoptic weather type of C3, which is favorable to air pollutant accumulation. However, when the prevailing northeasterly wind is obstructed by the Central Mountain Range, southwestern Taiwan, being situated on the leeside of the mountains, often exhibits the worst air pollution due to stagnant wind conditions. Keywords: Synoptic weather classification; Cluster analysis; PM2.5; Ozone; Stagnant wind. INTRODUCTION The weather classification method can be subjective, such as manual categorizations of weather maps, and it can also Air pollution is an important environmental issue in be objective, such as the cluster analysis method, which is Taiwan and can be either locally produced or transported used to categorize data and has been applied in many long distances from East Asia (Cheng et al., 2012; Chuang studies for classifying weather patterns (Kidson, 2000; et al., 2017). The major domestic anthropogenic emissions Coleman and Roger, 2007; Russo et al., 2015). Ngan and are from urban areas, coal-fired power plants, crude oil Byun (2011) applied the two-stage cluster analysis method refinery plants, industrial parks, and major highways and to classify the synoptic weather patterns over eastern Texas are emitted mostly in western Taiwan (Fang and Chen, using the 850 hPa wind fields for the 5-month ozone (O3) 1996; Hsu and Cheng, 2016). In addition to emissions, season (May–September) in the years 2005 and 2006. The meteorological conditions have been shown to play an results demonstrate the effectiveness of using wind fields important role in affecting air pollution dispersion in Taiwan for classifying the weather pattern and clearly identified the (Tsai et al., 2008, 2011; Lai, 2014). associated O3 characteristics among the different clusters. Identifying the synoptic weather patterns and large-scale Taiwan is an island located on the southeastern fringe of circulation patterns represents a useful method of studying East Asia. The synoptic weather conditions are dominated by air pollution problems (Rainham et al., 2005; Cheng et al., the western North Pacific subtropical high-pressure system 2013; Vanos et al., 2015; Hsu and Cheng, 2016). Calvo et al. during summer and the Asian continental anticyclone during (2010) characterized the chemical composition of rainwater winter and spring and further complicated by the land-ocean based on the origin of air masses and found a strong distribution and high Central Mountain Range (CMR), relationship with the origin of air masses and precipitation. which runs from north to south across the island. This study was conducted to develop synoptic weather classifications in Taiwan using the cluster analysis method and investigate the role of meteorological conditions in air pollutant * Corresponding author. concentrations, including fine particulate matter (PM2.5), Tel.: +886-3-4227151-65508; Fax: +886-3-425-6841 coarse particulate matter (PM10) and O3. The objectives of E-mail address: [email protected] this study are as follows: (1) Study the characteristics of 1140 Hsu and Cheng, Aerosol and Air Quality Research, 19: 1139–1151, 2019 synoptic weather patterns and their associated circulation To avoid discussions on the tropical depression and patterns in Taiwan; and (2) examine the role of synoptic typhoon related weather types, the two-stage cluster weather patterns on PM2.5, PM10, and O3 concentrations in analysis was applied twice. The first application of the different areas of western Taiwan. cluster analysis identified a cluster that is associated with the tropical depression and typhoon system (due to the METHODOLOGY AND DATA inclusion of the SLP) and includes 35 days (refer to Supplementary Information). Cluster analysis is an objective classification method We excluded the 35 days that are affected by typhoons that establishes relatively uniform groups from the input and re-apply the cluster analysis. Moreover, before data, and it separates data into distinctive clusters by applying the cluster analysis, the variables are normalized minimizing variance within each group and maximizing by calculating the standard score of each observed variable variance between groups. In this study, a two-stage clustering based on Formula (1): method was applied to produce the weather classifications in Taiwan. In the first step, the initial number of clusters X and the centroids of the clusters were determined and used Z (1) as seeds in the second stage for the initiation of the k-means cluster analysis (MacQueen, 1967). The hourly wind speed and wind direction and the sea level pressure (SLP) observed where Z is the standard score of meteorological variable; X at Central Weather Bureau (CWB) surface stations in is the observed surface U, V, and SLP; and µ and σ denote Taiwan (refer to Fig. 1 for station location) are used for the the mean and standard deviation of each meteorological cluster analysis. The hourly observed wind data are first field. decomposed into U and V components, and daily averaged The number of classified clusters is determined according values are estimated. The wind field is selected for the to the silhouette value that measures the similarity within the cluster analysis because it provides information on the air cluster and the dissimilarity between clusters (Rousseeuw, pollution dispersion and transport behavior. Moreover, the 1987). In this study, the variation of the silhouette value cluster analysis experiments are applied by including and becomes small when the number of clusters is greater than excluding the SLP variable. The results indicate that the six; therefore, six clusters are set as the final classified inclusion of the SLP in the cluster analysis can enhance the results. For each identified cluster, the mean values of distinction between the summer and winter classified temperature, precipitation, and wind speed, and PM2.5, weather types; therefore, it is included for weather PM10, and O3 concentrations acquired from the Taiwan classification in this study. Environmental Protection Agency (EPA) air quality The study period is from January 2013 to March 2018. monitoring stations (refer to Fig. 1 for station location) are Fig. 1. Black points identify the locations of EPA surface stations. Blue triangles identify the locations of CWB surface stations. Red triangles identify the forty-eight-hour backward trajectory ending points. The seven AQZs (NT, CM, CT, YCN, KP, YI, and HD) are identified by name. The contour line indicates the mountain range (CMR). Hsu and Cheng, Aerosol and Air Quality Research, 19: 1139–1151, 2019 1141 analyzed. The daily mean SLP and horizontal wind field during the winter months. Among the six clusters, the data from the ERA5 reanalysis data (Hersbach and Dee, mean wind speed is the highest (2.35 m s–1) in the C1 2016) were used to produce the composite surface weather cluster due to the prevalence of the NEM flow. Although map for each cluster. the strong wind fields can disperse the air pollutants, the –3 –3 Due to the complicated geographical location and mean PM2.5 (28.35 µg m ) and PM10 (60.19 µg m ) topographical conditions in Taiwan, the meteorological concentrations are the third highest among the six clusters. conditions can change distinctively from the northern to Due to the influence of the NEM flow, air pollution can be southern parts of Taiwan. Based on distinct characteristics contributed by long-range transported (LRT) air pollutants of the meteorological and emission conditions, the Taiwan from East Asia. EPA has divided the nation into seven air quality zones Cluster 2 (C2) occurs following the C1 weather type that (AQZs), namely, northern Taiwan (NT), the Chu-Miao moves to the east coast of China. With the eastward area (CM), central Taiwan (CT), the Yun-Chia-Nan area movement of the anticyclone, the prevailing wind in (YCN), the Kao-Ping area (KP), the Hua-Dong area (HD), Taiwan is affected by weak northeasterly to easterly flows and Yilan (YI). The analysis and discussion consider five due to continental high-pressure peripheral circulation. The AQZs (NT, CM, CT, YCN, and KP) (Fig. 1) to examine the monthly occurrence is similar to that of C1, and it mostly air pollution problem in different areas of western Taiwan. appears in the cold season from November to March, The HD and YI areas located in eastern Taiwan are not although the number of occurrences is 342 days (17.85%), discussed due to the lower impact of local anthropogenic which is higher than the number of C1 occurrences (Fig.
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