select ad.sno,ad.journal,ad.title,ad.author_names,ad.abstract,ad.abstractlink,j.j_name,vi.* from articles_data ad left join journals j on j.journal=ad.journal left join vol_issues vi on vi.issue_id_en=ad.issue_id where ad.sno_en='40883' and ad.lang_id='9' and j.lang_id='9' and vi.lang_id='9' Functional Clustering of Particulate Matter less than 10 Mic | 40883
జర్నల్ ఆఫ్ పొల్యూషన్ ఎఫెక్ట్స్ & కంట్రోల్

జర్నల్ ఆఫ్ పొల్యూషన్ ఎఫెక్ట్స్ & కంట్రోల్
అందరికి ప్రవేశం

ISSN: 2375-4397

నైరూప్య

Functional Clustering of Particulate Matter less than 10 Microns: A Case of Africa

Musopole A

Particulate matter (PM) air pollution is a challenge that is endangering the environment and human health in Africa. Most countries in Africa are poor, hence monitoring of PM-related aspects is a challenge. Clustering of aspects of PM can ease the burden. In this paper, time series (TS) of PM of less than 10 microns (PM10) for 51 African countries are clustered. The data are represented in functional form and complete-linkage hierarchical clustering algorithm is used to cluster the coefficients of the functional data, thereby clustering the original TS. The functional form has the advantage of looking at the trajectories as a whole. 2 clusters are extracted from the data. 2014 Cross-sectional data of PM of less than 2.5 microns (PM2.5) for African countries are also clustered and 3 clusters are obtained. Adjusted Rand Index (ARI) computed for clusters resulting from the two data sets is 0.588, indicating some agreement on the clusters resulting from the two data sets.

నిరాకరణ: ఈ సారాంశం కృత్రిమ మేధస్సు సాధనాలను ఉపయోగించి అనువదించబడింది మరియు ఇంకా సమీక్షించబడలేదు లేదా ధృవీకరించబడలేదు.
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