scholarly journals The Temporal Evolution of PM2.5 Pollution Events in Taiwan: Clustering and the Association with Synoptic Weather

Atmosphere ◽  
2020 ◽  
Vol 11 (11) ◽  
pp. 1265
Author(s):  
Shih-Hao Su ◽  
Chiao-Wei Chang ◽  
Wei-Ting Chen
Keyword(s):  
Clustering Algorithm ◽  
Temporal Evolution ◽  
Weather Forecasting ◽  
Fine Particulate Matter ◽  
Atmospheric Stability ◽  
Distinct Pattern ◽  
Local Circulation ◽  
Weather Patterns ◽  
Synoptic Weather ◽  
Pollution Events

This study conducted a cluster analysis on the fine particulate matter (PM2.5) data over Taiwan from 2006 to 2015 and diagnosed their association with the synoptic weather patterns. Five clusters are identified via a hierarchical clustering algorithm; three of them correspond to severe events, each with a distinct pattern of temporal evolution within the 240-h window. The occurrence of the different clusters exhibits strong seasonal variation. Two of the polluted clusters are more frequently associated with weak synoptic weather, while the other one is related to northeasterly winds and fronts. Detailed case studies show that the weather patterns’ temporal evolutions clearly modulate the transition among various pollution clusters by influencing the changes in local circulation and atmospheric stability. In winter, the clusters characterizing severe PM2.5 pollution events occur when Taiwan is influenced by persistent weak synoptic condition, while in autumn, the long-range transport by strong northerly winds leads to the occurrence of severe PM2.5 pollution. The current results shed light on the potential of combining the data-driven approach and the numerical weather forecasting model to provide extended range forecasts of local air pollution forecasts.

scholarly journals Joint Occurrence of Heatwaves and Ozone Pollution and Increased Health Risks in Beijing, China: Roles of Synoptic Weather Pattern and Urbanization

2022 ◽  
Author(s):  
Lian Zong ◽  
Yuanjian Yang ◽  
Haiyun Xia ◽  
Meng Gao ◽  
Zhaobin Sun ◽  
...  
Keyword(s):  
Health Risks ◽  
Rural Areas ◽  
Urban Areas ◽  
Ozone Pollution ◽  
Synoptic Patterns ◽  
Weather Patterns ◽  
Boundary Layer Height ◽  
Synoptic Weather ◽  
High Concentrations ◽  
Pollution Events

Abstract. Heatwaves (HWs) paired with higher ozone (O3) concentration at surface level pose a serious threat to human health. Their combined modulation of synoptic patterns and urbanization remains unclear. By using five years of summertime temperature and O3 concentrations observation in Beijing, this study explored potential drivers of compound HWs and O3 pollution events. Three unfavourable synoptic weather patterns were identified to dominate the compound HWs and O3 pollution events. The weather patterns contributing to enhance those conditions are characterized by sinking air motion, low boundary layer height, and hot temperatures. Under the synergistic stress of HWs and O3 pollution, the public mortality risk increased by approximately 12.59 % (95 % confidence interval: 4.66 %, 21.42 %). Relative to rural areas, urbanization caused higher risks for HWs, but lower risks for O3 over urban areas. In general, unfavourable synoptic patterns and urbanization can enhance the compound risk of events in Beijing by 45.46 % and 8.08 %, respectively. Our findings provide robust evidence and implications for forecasting compound heatwaves and O3 pollution event and its health risks in Beijing or in other urban areas all over the word having high concentrations of O3 and high-density populations.

scholarly journals Supervised Multilevel Clustering for Rainfall forecasting using Meteorological data

2019 ◽  
Vol 8 (12) ◽  
pp. 1591-1596
Keyword(s):  
Clustering Algorithm ◽  
Weather Forecasting ◽  
Meteorological Data ◽  
Clustering Algorithms ◽  
Model Performance ◽  
Rainfall Forecasting ◽  
Weather Patterns ◽  
Improve Model ◽  
Meteorological Observations ◽  
Em Clustering

Atmospheric science focuses on weather processes and forecasting. Numerical and statistical analysis plays an important role in meteorological research. Meteorological data will be used to predict the changes in climatic patterns by using forecasting models and weather forecasting instruments. Data mining techniques have more scope to discover future weather patterns by analyzing past weather dimensions. In our study two techniques Multiple Linear Regression (MLR) and Expectation Maximization (EM) clustering algorithms are combined for rainfall forecasting. MLR interprets most important parameters of rainfall for clustering algorithm. EM clustering algorithm will find correctly and incorrectly clustered instances when applied on selected partitioned attributes. The model was able to forecast less rainfall, medium rainfall and high rainfall by analyzing past meteorological observations. Standard deviation is used as a measure of error correction to improve the cluster results. Data normalization helps to improve model performance. These findings are useful to determine future climate expectation.

scholarly journals Evaluating performance of health care facilities at meeting HIV-indicator reporting requirements in Kenya: an application of K-means clustering algorithm

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Milka Bochere Gesicho ◽  
Martin Chieng Were ◽  
Ankica Babic
Keyword(s):  
Clustering Algorithm ◽  
Distinct Pattern ◽  
Health Care Facilities ◽  
Health Facilities ◽  
Hiv Care ◽  
Care Providers ◽  
Homogeneous Groups ◽  
Reporting Requirements ◽  
Low Middle Income Countries ◽  
Over Time

Abstract Background The ability to report complete, accurate and timely data by HIV care providers and other entities is a key aspect in monitoring trends in HIV prevention, treatment and care, hence contributing to its eradication. In many low-middle-income-countries (LMICs), aggregate HIV data reporting is done through the District Health Information Software 2 (DHIS2). Nevertheless, despite a long-standing requirement to report HIV-indicator data to DHIS2 in LMICs, few rigorous evaluations exist to evaluate adequacy of health facility reporting at meeting completeness and timeliness requirements over time. The aim of this study is to conduct a comprehensive assessment of the reporting status for HIV-indicators, from the time of DHIS2 implementation, using Kenya as a case study. Methods A retrospective observational study was conducted to assess reporting performance of health facilities providing any of the HIV services in all 47 counties in Kenya between 2011 and 2018. Using data extracted from DHIS2, K-means clustering algorithm was used to identify homogeneous groups of health facilities based on their performance in meeting timeliness and completeness facility reporting requirements for each of the six programmatic areas. Average silhouette coefficient was used in measuring the quality of the selected clusters. Results Based on percentage average facility reporting completeness and timeliness, four homogeneous groups of facilities were identified namely: best performers, average performers, poor performers and outlier performers. Apart from blood safety reports, a distinct pattern was observed in five of the remaining reports, with the proportion of best performing facilities increasing and the proportion of poor performing facilities decreasing over time. However, between 2016 and 2018, the proportion of best performers declined in some of the programmatic areas. Over the study period, no distinct pattern or trend in proportion changes was observed among facilities in the average and outlier groups. Conclusions The identified clusters revealed general improvements in reporting performance in the various reporting areas over time, but with noticeable decrease in some areas between 2016 and 2018. This signifies the need for continuous performance monitoring with possible integration of machine learning and visualization approaches into national HIV reporting systems.

Diverse synoptic weather patterns of warm-season heavy rainfall events in South Korea

2021 ◽  
Author(s):  
Chanil Park ◽  
Seok-Woo Son ◽  
Joowan Kim ◽  
Eun-Chul Chang ◽  
Jung-Hoon Kim ◽  
...  
Keyword(s):  
North Pacific ◽  
Moisture Transport ◽  
Heavy Rainfall ◽  
Eastern China ◽  
Warm Season ◽  
Sea Level Pressure ◽  
Rainfall Events ◽  
Weather Patterns ◽  
Synoptic Weather ◽  
Heavy Rainfall Events

AbstractThis study identifies diverse synoptic weather patterns of warm-season heavy rainfall events (HREs) in South Korea. The HREs not directly connected to tropical cyclones (TCs) (81.1%) are typically associated with a midlatitude cyclone from eastern China, the expanded North Pacific high and strong southwesterly moisture transport in between. They are frequent both in the first (early summer) and second rainy periods (late summer) with impacts on the south coast and west of the mountainous region. In contrast, the HREs resulting from TCs (18.9%) are caused by the synergetic interaction between the TC and meandering midlatitude flow, especially in the second rainy period. The strong south-southeasterly moisture transport makes the southern and eastern coastal regions prone to the TC-driven HREs. By applying a self-organizing map algorithm to the non-TC HREs, their surface weather patterns are further classified into six clusters. Clusters 1 and 3 exhibit frontal boundary between the low and high with differing relative strengths. Clusters 2 and 5 feature an extratropical cyclone migrating from eastern China under different background sea-level pressure patterns. Cluster 4 is characterized by the expanded North Pacific high with no organized negative sea-level pressure anomaly, and cluster 6 displays a development of a moisture pathway between the continental and oceanic highs. Each cluster exhibits a distinct spatio-temporal occurrence distribution. The result provides useful guidance for predicting the HREs by depicting important factors to be differently considered depending on their synoptic categorization.

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