Matches in SemOpenAlex for { <https://semopenalex.org/work/W4386003002> ?p ?o ?g. }
- W4386003002 endingPage "662" @default.
- W4386003002 startingPage "652" @default.
- W4386003002 abstract "In the last few years, subways have rapidly spread in many countries and have replaced different modes of commuting in some important areas. Despite the fact that passengers only spend a short time in the subway, pollution in the subway is devastating to human health and can cause various diseases, including respiratory diseases. With the development of artificial intelligence (AI), more and more scholars are keen to use this technology to predict and monitor pollution focuses, which in turn can screen the air quality in subways. This paper reviews the application of AI in prediction and control of indoor air quality (IAQ) in subways during 2010–2022. The results show that most of the prediction studies analyzed were conducted for PM10 and PM2.5, and most of the control studies were conducted to optimize the subway ventilation system. Fewer studies have been conducted on the prediction of other air pollutants and on IAQ control facilities. This study attempts to provide guidelines for future AI to manage IAQ in subways." @default.
- W4386003002 created "2023-08-20" @default.
- W4386003002 creator A5044493285 @default.
- W4386003002 creator A5046324674 @default.
- W4386003002 creator A5076131656 @default.
- W4386003002 date "2023-10-01" @default.
- W4386003002 modified "2023-10-07" @default.
- W4386003002 title "An overview of artificial intelligence in subway indoor air quality prediction and control" @default.
- W4386003002 cites W1967939326 @default.
- W4386003002 cites W1969821818 @default.
- W4386003002 cites W1976289123 @default.
- W4386003002 cites W1977177161 @default.
- W4386003002 cites W2002220459 @default.
- W4386003002 cites W2006067028 @default.
- W4386003002 cites W2013546516 @default.
- W4386003002 cites W2025468124 @default.
- W4386003002 cites W2026579521 @default.
- W4386003002 cites W2030143591 @default.
- W4386003002 cites W2036411747 @default.
- W4386003002 cites W2045356632 @default.
- W4386003002 cites W2053319821 @default.
- W4386003002 cites W2058956762 @default.
- W4386003002 cites W2072333061 @default.
- W4386003002 cites W2074300958 @default.
- W4386003002 cites W2075351671 @default.
- W4386003002 cites W2078650206 @default.
- W4386003002 cites W2084438712 @default.
- W4386003002 cites W2104198157 @default.
- W4386003002 cites W2140804691 @default.
- W4386003002 cites W2164851136 @default.
- W4386003002 cites W2165067379 @default.
- W4386003002 cites W2167317726 @default.
- W4386003002 cites W2184903086 @default.
- W4386003002 cites W2215891346 @default.
- W4386003002 cites W2239373335 @default.
- W4386003002 cites W2469880831 @default.
- W4386003002 cites W2589582138 @default.
- W4386003002 cites W2736440921 @default.
- W4386003002 cites W2737749939 @default.
- W4386003002 cites W2749041713 @default.
- W4386003002 cites W2806040481 @default.
- W4386003002 cites W2883599811 @default.
- W4386003002 cites W2886253200 @default.
- W4386003002 cites W2900579124 @default.
- W4386003002 cites W2901338302 @default.
- W4386003002 cites W2901521127 @default.
- W4386003002 cites W2905241670 @default.
- W4386003002 cites W2915939236 @default.
- W4386003002 cites W2973582255 @default.
- W4386003002 cites W2973961985 @default.
- W4386003002 cites W2995867913 @default.
- W4386003002 cites W3004395473 @default.
- W4386003002 cites W3004417816 @default.
- W4386003002 cites W3009876865 @default.
- W4386003002 cites W3010068470 @default.
- W4386003002 cites W3014411108 @default.
- W4386003002 cites W3032357626 @default.
- W4386003002 cites W3045506926 @default.
- W4386003002 cites W3045641519 @default.
- W4386003002 cites W3088585439 @default.
- W4386003002 cites W3117549668 @default.
- W4386003002 cites W3183199962 @default.
- W4386003002 cites W3194615709 @default.
- W4386003002 cites W3208376516 @default.
- W4386003002 cites W351991690 @default.
- W4386003002 cites W4210255785 @default.
- W4386003002 cites W4280581714 @default.
- W4386003002 cites W4281769461 @default.
- W4386003002 cites W4297497027 @default.
- W4386003002 cites W4298003589 @default.
- W4386003002 cites W4313262613 @default.
- W4386003002 cites W609180008 @default.
- W4386003002 cites W86447045 @default.
- W4386003002 doi "https://doi.org/10.1016/j.psep.2023.08.055" @default.
- W4386003002 hasPublicationYear "2023" @default.
- W4386003002 type Work @default.
- W4386003002 citedByCount "0" @default.
- W4386003002 crossrefType "journal-article" @default.
- W4386003002 hasAuthorship W4386003002A5044493285 @default.
- W4386003002 hasAuthorship W4386003002A5046324674 @default.
- W4386003002 hasAuthorship W4386003002A5076131656 @default.
- W4386003002 hasConcept C126314574 @default.
- W4386003002 hasConcept C127413603 @default.
- W4386003002 hasConcept C153294291 @default.
- W4386003002 hasConcept C154945302 @default.
- W4386003002 hasConcept C178790620 @default.
- W4386003002 hasConcept C185592680 @default.
- W4386003002 hasConcept C200457457 @default.
- W4386003002 hasConcept C205649164 @default.
- W4386003002 hasConcept C22212356 @default.
- W4386003002 hasConcept C2775924081 @default.
- W4386003002 hasConcept C2987853052 @default.
- W4386003002 hasConcept C2993393817 @default.
- W4386003002 hasConcept C41008148 @default.
- W4386003002 hasConcept C559116025 @default.
- W4386003002 hasConcept C65469 @default.
- W4386003002 hasConcept C78519656 @default.
- W4386003002 hasConcept C87717796 @default.