id	sid	tid	token	lemma	pos
ajst-8009	1	1	academic	academic	ADJ
ajst-8009	1	2	journal	journal	NOUN
ajst-8009	1	3	of	of	ADP
ajst-8009	1	4	science	science	NOUN
ajst-8009	1	5	and	and	CCONJ
ajst-8009	1	6	technology	technology	NOUN
ajst-8009	1	7	issn	issn	NOUN
ajst-8009	1	8	:	:	PUNCT
ajst-8009	1	9	2771	2771	NUM
ajst-8009	1	10	-	-	SYM
ajst-8009	1	11	3032	3032	NUM
ajst-8009	1	12	|	|	NOUN
ajst-8009	1	13	vol	vol	NOUN
ajst-8009	1	14	.	.	PROPN
ajst-8009	2	1	5	5	NUM
ajst-8009	2	2	,	,	PUNCT
ajst-8009	2	3	no	no	INTJ
ajst-8009	2	4	.	.	NOUN
ajst-8009	2	5	3	3	NUM
ajst-8009	2	6	,	,	PUNCT
ajst-8009	3	1	2023	2023	NUM
ajst-8009	3	2	172	172	NUM
ajst-8009	3	3	pm2.5	pm2.5	PRON
ajst-8009	3	4	concentration	concentration	NOUN
ajst-8009	3	5	prediction	prediction	NOUN
ajst-8009	3	6	method	method	NOUN
ajst-8009	3	7	based	base	VERB
ajst-8009	3	8	on	on	ADP
ajst-8009	3	9	temporal	temporal	ADJ
ajst-8009	3	10	attention	attention	NOUN
ajst-8009	3	11	mechanism	mechanism	NOUN
ajst-8009	3	12	and	and	CCONJ
ajst-8009	3	13	cnn‐lstm	cnn‐lstm	PROPN
ajst-8009	3	14	zhuohui	zhuohui	PROPN
ajst-8009	3	15	zhou1	zhou1	PROPN
ajst-8009	3	16	,	,	PUNCT
ajst-8009	3	17	xiaofang	xiaofang	PROPN
ajst-8009	3	18	liu1	liu1	PROPN
ajst-8009	3	19	,	,	PUNCT
ajst-8009	3	20	*	*	PROPN
ajst-8009	3	21	,	,	PUNCT
ajst-8009	3	22	huan	huan	PROPN
ajst-8009	3	23	yang1	yang1	PROPN
ajst-8009	4	1	1school	1school	NUM
ajst-8009	4	2	of	of	ADP
ajst-8009	4	3	computer	computer	NOUN
ajst-8009	4	4	science	science	NOUN
ajst-8009	4	5	and	and	CCONJ
ajst-8009	4	6	engineering	engineering	NOUN
ajst-8009	4	7	,	,	PUNCT
ajst-8009	4	8	sichuan	sichuan	PROPN
ajst-8009	4	9	university	university	PROPN
ajst-8009	4	10	of	of	ADP
ajst-8009	4	11	science	science	PROPN
ajst-8009	4	12	&	&	CCONJ
ajst-8009	4	13	engineering	engineering	PROPN
ajst-8009	4	14	,	,	PUNCT
ajst-8009	4	15	sichuan	sichuan	PROPN
ajst-8009	4	16	643000	643000	NUM
ajst-8009	4	17	,	,	PUNCT
ajst-8009	4	18	china	china	PROPN
ajst-8009	4	19	*	*	PUNCT
ajst-8009	4	20	corresponding	correspond	VERB
ajst-8009	4	21	author	author	NOUN
ajst-8009	4	22	:	:	PUNCT
ajst-8009	4	23	liu	liu	PROPN
ajst-8009	4	24	xiaofang	xiaofang	PROPN
ajst-8009	4	25	(	(	PUNCT
ajst-8009	4	26	email	email	NOUN
ajst-8009	4	27	:	:	PUNCT
ajst-8009	4	28	lxf1969@163.com	lxf1969@163.com	X
ajst-8009	4	29	)	)	PUNCT
ajst-8009	4	30	abstract	abstract	NOUN
ajst-8009	4	31	:	:	PUNCT
ajst-8009	4	32	accurately	accurately	ADV
ajst-8009	4	33	predicting	predict	VERB
ajst-8009	4	34	pm2.5	pm2.5	DET
ajst-8009	4	35	concentration	concentration	NOUN
ajst-8009	4	36	can	can	AUX
ajst-8009	4	37	effectively	effectively	ADV
ajst-8009	4	38	avoid	avoid	VERB
ajst-8009	4	39	the	the	DET
ajst-8009	4	40	harm	harm	NOUN
ajst-8009	4	41	caused	cause	VERB
ajst-8009	4	42	by	by	ADP
ajst-8009	4	43	heavy	heavy	ADJ
ajst-8009	4	44	pollution	pollution	NOUN
ajst-8009	4	45	weather	weather	NOUN
ajst-8009	4	46	to	to	ADP
ajst-8009	4	47	human	human	ADJ
ajst-8009	4	48	health	health	NOUN
ajst-8009	4	49	.	.	PUNCT
ajst-8009	5	1	in	in	ADP
ajst-8009	5	2	view	view	NOUN
ajst-8009	5	3	of	of	ADP
ajst-8009	5	4	the	the	DET
ajst-8009	5	5	non	non	NOUN
ajst-8009	5	6	-	-	NOUN
ajst-8009	5	7	linearity	linearity	ADJ
ajst-8009	5	8	,	,	PUNCT
ajst-8009	5	9	time	time	NOUN
ajst-8009	5	10	series	series	PROPN
ajst-8009	5	11	characteristics	characteristic	NOUN
ajst-8009	5	12	,	,	PUNCT
ajst-8009	5	13	and	and	CCONJ
ajst-8009	5	14	the	the	DET
ajst-8009	5	15	problem	problem	NOUN
ajst-8009	5	16	of	of	ADP
ajst-8009	5	17	large	large	ADJ
ajst-8009	5	18	multi	multi	ADJ
ajst-8009	5	19	-	-	ADJ
ajst-8009	5	20	step	step	ADJ
ajst-8009	5	21	prediction	prediction	NOUN
ajst-8009	5	22	errors	error	NOUN
ajst-8009	5	23	in	in	ADP
ajst-8009	5	24	pm2.5	pm2.5	DET
ajst-8009	5	25	concentration	concentration	NOUN
ajst-8009	5	26	data	datum	NOUN
ajst-8009	5	27	,	,	PUNCT
ajst-8009	5	28	a	a	DET
ajst-8009	5	29	method	method	NOUN
ajst-8009	5	30	combining	combine	VERB
ajst-8009	5	31	long	long	ADJ
ajst-8009	5	32	short	short	ADJ
ajst-8009	5	33	-	-	PUNCT
ajst-8009	5	34	term	term	NOUN
ajst-8009	5	35	memory	memory	NOUN
ajst-8009	5	36	network	network	NOUN
ajst-8009	5	37	and	and	CCONJ
ajst-8009	5	38	convolutional	convolutional	ADJ
ajst-8009	5	39	neural	neural	ADJ
ajst-8009	5	40	network	network	NOUN
ajst-8009	5	41	with	with	ADP
ajst-8009	5	42	time	time	NOUN
ajst-8009	5	43	pattern	pattern	NOUN
ajst-8009	5	44	attention	attention	NOUN
ajst-8009	5	45	mechanism	mechanism	NOUN
ajst-8009	5	46	(	(	PUNCT
ajst-8009	5	47	tpa	tpa	NOUN
ajst-8009	5	48	-	-	PUNCT
ajst-8009	5	49	cnn	cnn	NOUN
ajst-8009	5	50	-	-	PUNCT
ajst-8009	5	51	lstm	lstm	NOUN
ajst-8009	5	52	)	)	PUNCT
ajst-8009	5	53	is	be	AUX
ajst-8009	5	54	proposed	propose	VERB
ajst-8009	5	55	.	.	PUNCT
ajst-8009	6	1	the	the	DET
ajst-8009	6	2	method	method	NOUN
ajst-8009	6	3	uses	use	VERB
ajst-8009	6	4	historical	historical	ADJ
ajst-8009	6	5	pm2.5	pm2.5	PROPN
ajst-8009	6	6	concentration	concentration	NOUN
ajst-8009	6	7	data	datum	NOUN
ajst-8009	6	8	,	,	PUNCT
ajst-8009	6	9	historical	historical	ADJ
ajst-8009	6	10	meteorological	meteorological	ADJ
ajst-8009	6	11	data	datum	NOUN
ajst-8009	6	12	,	,	PUNCT
ajst-8009	6	13	and	and	CCONJ
ajst-8009	6	14	surrounding	surround	VERB
ajst-8009	6	15	station	station	NOUN
ajst-8009	6	16	data	datum	NOUN
ajst-8009	6	17	to	to	PART
ajst-8009	6	18	predict	predict	VERB
ajst-8009	6	19	the	the	DET
ajst-8009	6	20	future	future	ADJ
ajst-8009	6	21	6	6	NUM
ajst-8009	6	22	-	-	PUNCT
ajst-8009	6	23	hour	hour	NOUN
ajst-8009	6	24	pm2.5	pm2.5	PROPN
ajst-8009	6	25	concentration	concentration	NOUN
ajst-8009	6	26	of	of	ADP
ajst-8009	6	27	air	air	NOUN
ajst-8009	6	28	quality	quality	NOUN
ajst-8009	6	29	monitoring	monitoring	NOUN
ajst-8009	6	30	stations	station	NOUN
ajst-8009	6	31	.	.	PUNCT
ajst-8009	7	1	firstly	firstly	ADV
ajst-8009	7	2	,	,	PUNCT
ajst-8009	7	3	cnn	cnn	PROPN
ajst-8009	7	4	is	be	AUX
ajst-8009	7	5	used	use	VERB
ajst-8009	7	6	to	to	PART
ajst-8009	7	7	obtain	obtain	VERB
ajst-8009	7	8	the	the	DET
ajst-8009	7	9	spatial	spatial	ADJ
ajst-8009	7	10	characteristics	characteristic	NOUN
ajst-8009	7	11	between	between	ADP
ajst-8009	7	12	multiple	multiple	ADJ
ajst-8009	7	13	stations	station	NOUN
ajst-8009	7	14	,	,	PUNCT
ajst-8009	7	15	secondly	secondly	ADV
ajst-8009	7	16	,	,	PUNCT
ajst-8009	7	17	lstm	lstm	NOUN
ajst-8009	7	18	is	be	AUX
ajst-8009	7	19	added	add	VERB
ajst-8009	7	20	after	after	SCONJ
ajst-8009	7	21	cnn	cnn	PROPN
ajst-8009	7	22	to	to	PART
ajst-8009	7	23	extract	extract	VERB
ajst-8009	7	24	the	the	DET
ajst-8009	7	25	temporal	temporal	ADJ
ajst-8009	7	26	changes	change	NOUN
ajst-8009	7	27	of	of	ADP
ajst-8009	7	28	non	non	ADJ
ajst-8009	7	29	-	-	ADJ
ajst-8009	7	30	linear	linear	ADJ
ajst-8009	7	31	data	datum	NOUN
ajst-8009	7	32	,	,	PUNCT
ajst-8009	7	33	and	and	CCONJ
ajst-8009	7	34	finally	finally	ADV
ajst-8009	7	35	,	,	PUNCT
ajst-8009	7	36	to	to	PART
ajst-8009	7	37	capture	capture	VERB
ajst-8009	7	38	the	the	DET
ajst-8009	7	39	key	key	ADJ
ajst-8009	7	40	features	feature	NOUN
ajst-8009	7	41	of	of	ADP
ajst-8009	7	42	temporal	temporal	ADJ
ajst-8009	7	43	information	information	NOUN
ajst-8009	7	44	,	,	PUNCT
ajst-8009	7	45	temporal	temporal	ADJ
ajst-8009	7	46	pattern	pattern	NOUN
ajst-8009	7	47	attention	attention	NOUN
ajst-8009	7	48	mechanism	mechanism	NOUN
ajst-8009	7	49	(	(	PUNCT
ajst-8009	7	50	tpa	tpa	NOUN
ajst-8009	7	51	)	)	PUNCT
ajst-8009	7	52	is	be	AUX
ajst-8009	7	53	added	add	VERB
ajst-8009	7	54	.	.	PUNCT
ajst-8009	8	1	tpa	tpa	PROPN
ajst-8009	8	2	can	can	AUX
ajst-8009	8	3	automatically	automatically	ADV
ajst-8009	8	4	adjust	adjust	VERB
ajst-8009	8	5	weights	weight	NOUN
ajst-8009	8	6	based	base	VERB
ajst-8009	8	7	on	on	ADP
ajst-8009	8	8	the	the	DET
ajst-8009	8	9	input	input	NOUN
ajst-8009	8	10	of	of	ADP
ajst-8009	8	11	each	each	DET
ajst-8009	8	12	time	time	NOUN
ajst-8009	8	13	step	step	NOUN
ajst-8009	8	14	,	,	PUNCT
ajst-8009	8	15	and	and	CCONJ
ajst-8009	8	16	select	select	VERB
ajst-8009	8	17	the	the	DET
ajst-8009	8	18	most	most	ADV
ajst-8009	8	19	relevant	relevant	ADJ
ajst-8009	8	20	time	time	NOUN
ajst-8009	8	21	step	step	NOUN
ajst-8009	8	22	for	for	ADP
ajst-8009	8	23	prediction	prediction	NOUN
ajst-8009	8	24	,	,	PUNCT
ajst-8009	8	25	thereby	thereby	ADV
ajst-8009	8	26	improving	improve	VERB
ajst-8009	8	27	the	the	DET
ajst-8009	8	28	accuracy	accuracy	NOUN
ajst-8009	8	29	of	of	ADP
ajst-8009	8	30	the	the	DET
ajst-8009	8	31	model	model	NOUN
ajst-8009	8	32	.	.	PUNCT
ajst-8009	9	1	an	an	DET
ajst-8009	9	2	example	example	NOUN
ajst-8009	9	3	analysis	analysis	NOUN
ajst-8009	9	4	is	be	AUX
ajst-8009	9	5	conducted	conduct	VERB
ajst-8009	9	6	on	on	ADP
ajst-8009	9	7	the	the	DET
ajst-8009	9	8	measured	measured	ADJ
ajst-8009	9	9	data	datum	NOUN
ajst-8009	9	10	of	of	ADP
ajst-8009	9	11	beijing	beijing	PROPN
ajst-8009	9	12	's	's	PART
ajst-8009	9	13	air	air	NOUN
ajst-8009	9	14	quality	quality	NOUN
ajst-8009	9	15	stations	station	NOUN
ajst-8009	9	16	in	in	ADP
ajst-8009	9	17	2018	2018	NUM
ajst-8009	9	18	,	,	PUNCT
ajst-8009	9	19	and	and	CCONJ
ajst-8009	9	20	compared	compare	VERB
ajst-8009	9	21	with	with	ADP
ajst-8009	9	22	other	other	ADJ
ajst-8009	9	23	mainstream	mainstream	ADJ
ajst-8009	9	24	algorithms	algorithm	NOUN
ajst-8009	9	25	.	.	PUNCT
ajst-8009	10	1	the	the	DET
ajst-8009	10	2	results	result	NOUN
ajst-8009	10	3	show	show	VERB
ajst-8009	10	4	that	that	SCONJ
ajst-8009	10	5	the	the	DET
ajst-8009	10	6	proposed	propose	VERB
ajst-8009	10	7	model	model	NOUN
ajst-8009	10	8	has	have	VERB
ajst-8009	10	9	higher	high	ADJ
ajst-8009	10	10	prediction	prediction	NOUN
ajst-8009	10	11	accuracy	accuracy	NOUN
ajst-8009	10	12	and	and	CCONJ
ajst-8009	10	13	performance	performance	NOUN
ajst-8009	10	14	.	.	PUNCT
ajst-8009	11	1	keywords	keyword	NOUN
ajst-8009	11	2	:	:	PUNCT
ajst-8009	11	3	pm2.5	pm2.5	PRON
ajst-8009	11	4	concentration	concentration	NOUN
ajst-8009	11	5	prediction	prediction	NOUN
ajst-8009	11	6	,	,	PUNCT
ajst-8009	11	7	separated	separate	VERB
ajst-8009	11	8	by	by	ADP
ajst-8009	11	9	commas	comma	NOUN
ajst-8009	11	10	,	,	PUNCT
ajst-8009	11	11	temporal	temporal	ADJ
ajst-8009	11	12	pattern	pattern	NOUN
ajst-8009	11	13	attention	attention	NOUN
ajst-8009	11	14	mechanism	mechanism	NOUN
ajst-8009	11	15	,	,	PUNCT
ajst-8009	11	16	cnn	cnn	PROPN
ajst-8009	11	17	-	-	PUNCT
ajst-8009	11	18	lstm	lstm	PROPN
ajst-8009	11	19	.	.	PUNCT
ajst-8009	12	1	1	1	X
ajst-8009	12	2	.	.	X
ajst-8009	12	3	introduction	introduction	NOUN
ajst-8009	12	4	the	the	DET
ajst-8009	12	5	introduction	introduction	NOUN
ajst-8009	12	6	should	should	AUX
ajst-8009	12	7	provide	provide	VERB
ajst-8009	12	8	background	background	NOUN
ajst-8009	12	9	information	information	NOUN
ajst-8009	12	10	(	(	PUNCT
ajst-8009	12	11	including	include	VERB
ajst-8009	12	12	relevant	relevant	ADJ
ajst-8009	12	13	references	reference	NOUN
ajst-8009	12	14	)	)	PUNCT
ajst-8009	12	15	and	and	CCONJ
ajst-8009	12	16	should	should	AUX
ajst-8009	12	17	indicate	indicate	VERB
ajst-8009	12	18	the	the	DET
ajst-8009	12	19	purpose	purpose	NOUN
ajst-8009	12	20	of	of	ADP
ajst-8009	12	21	the	the	DET
ajst-8009	12	22	manuscript	manuscript	NOUN
ajst-8009	12	23	.	.	PUNCT
ajst-8009	13	1	cite	cite	VERB
ajst-8009	13	2	relevant	relevant	ADJ
ajst-8009	13	3	work	work	NOUN
ajst-8009	13	4	by	by	ADP
ajst-8009	13	5	others	other	NOUN
ajst-8009	13	6	,	,	PUNCT
ajst-8009	13	7	including	include	VERB
ajst-8009	13	8	research	research	NOUN
ajst-8009	13	9	outside	outside	ADP
ajst-8009	13	10	your	your	PRON
ajst-8009	13	11	company	company	NOUN
ajst-8009	13	12	.	.	PUNCT
ajst-8009	14	1	place	place	VERB
ajst-8009	14	2	your	your	PRON
ajst-8009	14	3	work	work	NOUN
ajst-8009	14	4	in	in	ADP
ajst-8009	14	5	perspective	perspective	NOUN
ajst-8009	14	6	by	by	ADP
ajst-8009	14	7	referring	refer	VERB
ajst-8009	14	8	to	to	ADP
ajst-8009	14	9	other	other	ADJ
ajst-8009	14	10	research	research	NOUN
ajst-8009	14	11	papers	paper	NOUN
ajst-8009	14	12	.	.	PUNCT
ajst-8009	15	1	inclusion	inclusion	NOUN
ajst-8009	15	2	of	of	ADP
ajst-8009	15	3	statements	statement	NOUN
ajst-8009	15	4	at	at	ADP
ajst-8009	15	5	the	the	DET
ajst-8009	15	6	end	end	NOUN
ajst-8009	15	7	of	of	ADP
ajst-8009	15	8	the	the	DET
ajst-8009	15	9	introduction	introduction	NOUN
ajst-8009	15	10	regarding	regard	VERB
ajst-8009	15	11	the	the	DET
ajst-8009	15	12	organization	organization	NOUN
ajst-8009	15	13	of	of	ADP
ajst-8009	15	14	the	the	DET
ajst-8009	15	15	manuscript	manuscript	NOUN
ajst-8009	15	16	can	can	AUX
ajst-8009	15	17	be	be	AUX
ajst-8009	15	18	helpful	helpful	ADJ
ajst-8009	15	19	to	to	ADP
ajst-8009	15	20	the	the	DET
ajst-8009	15	21	reader	reader	NOUN
ajst-8009	15	22	.	.	PUNCT
ajst-8009	16	1	pm2.5	pm2.5	PROPN
ajst-8009	16	2	refers	refer	VERB
ajst-8009	16	3	to	to	ADP
ajst-8009	16	4	particulate	particulate	NOUN
ajst-8009	16	5	matter	matter	NOUN
ajst-8009	16	6	with	with	ADP
ajst-8009	16	7	a	a	DET
ajst-8009	16	8	diameter	diameter	NOUN
ajst-8009	16	9	of	of	ADP
ajst-8009	16	10	equal	equal	ADJ
ajst-8009	16	11	to	to	ADP
ajst-8009	16	12	or	or	CCONJ
ajst-8009	16	13	less	less	ADJ
ajst-8009	16	14	than	than	ADP
ajst-8009	16	15	2.5	2.5	NUM
ajst-8009	16	16	micrometers	micrometer	NOUN
ajst-8009	16	17	in	in	ADP
ajst-8009	16	18	ambient	ambient	ADJ
ajst-8009	16	19	air	air	NOUN
ajst-8009	16	20	.	.	PUNCT
ajst-8009	17	1	due	due	ADP
ajst-8009	17	2	to	to	ADP
ajst-8009	17	3	their	their	PRON
ajst-8009	17	4	small	small	ADJ
ajst-8009	17	5	size	size	NOUN
ajst-8009	17	6	,	,	PUNCT
ajst-8009	17	7	these	these	DET
ajst-8009	17	8	particles	particle	NOUN
ajst-8009	17	9	can	can	AUX
ajst-8009	17	10	penetrate	penetrate	VERB
ajst-8009	17	11	deep	deep	ADV
ajst-8009	17	12	into	into	ADP
ajst-8009	17	13	the	the	DET
ajst-8009	17	14	respiratory	respiratory	ADJ
ajst-8009	17	15	system	system	NOUN
ajst-8009	17	16	and	and	CCONJ
ajst-8009	17	17	pose	pose	VERB
ajst-8009	17	18	a	a	DET
ajst-8009	17	19	significant	significant	ADJ
ajst-8009	17	20	threat	threat	NOUN
ajst-8009	17	21	to	to	ADP
ajst-8009	17	22	human	human	ADJ
ajst-8009	17	23	health	health	NOUN
ajst-8009	17	24	.	.	PUNCT
ajst-8009	18	1	pm2.5	pm2.5	PRON
ajst-8009	18	2	particles	particle	NOUN
ajst-8009	18	3	often	often	ADV
ajst-8009	18	4	adsorb	adsorb	VERB
ajst-8009	18	5	carcinogenic	carcinogenic	ADJ
ajst-8009	18	6	substances	substance	NOUN
ajst-8009	18	7	,	,	PUNCT
ajst-8009	18	8	toxic	toxic	ADJ
ajst-8009	18	9	metals	metal	NOUN
ajst-8009	18	10	,	,	PUNCT
ajst-8009	18	11	persistent	persistent	ADJ
ajst-8009	18	12	organic	organic	ADJ
ajst-8009	18	13	pollutants	pollutant	NOUN
ajst-8009	18	14	,	,	PUNCT
ajst-8009	18	15	and	and	CCONJ
ajst-8009	18	16	other	other	ADJ
ajst-8009	18	17	harmful	harmful	ADJ
ajst-8009	18	18	materials	material	NOUN
ajst-8009	18	19	,	,	PUNCT
ajst-8009	18	20	which	which	PRON
ajst-8009	18	21	can	can	AUX
ajst-8009	18	22	directly	directly	ADV
ajst-8009	18	23	affect	affect	VERB
ajst-8009	18	24	the	the	DET
ajst-8009	18	25	lungs	lung	NOUN
ajst-8009	18	26	when	when	SCONJ
ajst-8009	18	27	they	they	PRON
ajst-8009	18	28	enter	enter	VERB
ajst-8009	18	29	the	the	DET
ajst-8009	18	30	human	human	ADJ
ajst-8009	18	31	body	body	NOUN
ajst-8009	18	32	,	,	PUNCT
ajst-8009	18	33	leading	lead	VERB
ajst-8009	18	34	to	to	ADP
ajst-8009	18	35	heavy	heavy	ADJ
ajst-8009	18	36	metal	metal	NOUN
ajst-8009	18	37	poisoning	poisoning	NOUN
ajst-8009	18	38	,	,	PUNCT
ajst-8009	18	39	increased	increase	VERB
ajst-8009	18	40	cancer	cancer	NOUN
ajst-8009	18	41	risk	risk	NOUN
ajst-8009	18	42	,	,	PUNCT
ajst-8009	18	43	reproductive	reproductive	ADJ
ajst-8009	18	44	harm	harm	NOUN
ajst-8009	18	45	,	,	PUNCT
ajst-8009	18	46	and	and	CCONJ
ajst-8009	18	47	other	other	ADJ
ajst-8009	18	48	problems	problem	NOUN
ajst-8009	18	49	[	[	X
ajst-8009	18	50	1	1	NUM
ajst-8009	18	51	]	]	PUNCT
ajst-8009	18	52	.	.	PUNCT
ajst-8009	19	1	the	the	DET
ajst-8009	19	2	fluctuations	fluctuation	NOUN
ajst-8009	19	3	in	in	ADP
ajst-8009	19	4	pm2.5	pm2.5	DET
ajst-8009	19	5	concentration	concentration	NOUN
ajst-8009	19	6	have	have	VERB
ajst-8009	19	7	varying	vary	VERB
ajst-8009	19	8	degrees	degree	NOUN
ajst-8009	19	9	of	of	ADP
ajst-8009	19	10	impact	impact	NOUN
ajst-8009	19	11	on	on	ADP
ajst-8009	19	12	the	the	DET
ajst-8009	19	13	respiratory	respiratory	ADJ
ajst-8009	19	14	system	system	NOUN
ajst-8009	19	15	,	,	PUNCT
ajst-8009	19	16	crop	crop	NOUN
ajst-8009	19	17	growth	growth	NOUN
ajst-8009	19	18	,	,	PUNCT
ajst-8009	19	19	and	and	CCONJ
ajst-8009	19	20	tourism	tourism	NOUN
ajst-8009	19	21	.	.	PUNCT
ajst-8009	20	1	predicting	predict	VERB
ajst-8009	20	2	future	future	ADJ
ajst-8009	20	3	pm2.5	pm2.5	ADJ
ajst-8009	20	4	concentrations	concentration	NOUN
ajst-8009	20	5	in	in	ADP
ajst-8009	20	6	advance	advance	NOUN
ajst-8009	20	7	can	can	AUX
ajst-8009	20	8	provide	provide	VERB
ajst-8009	20	9	valuable	valuable	ADJ
ajst-8009	20	10	health	health	NOUN
ajst-8009	20	11	information	information	NOUN
ajst-8009	20	12	for	for	ADP
ajst-8009	20	13	travelers	traveler	NOUN
ajst-8009	20	14	and	and	CCONJ
ajst-8009	20	15	serve	serve	VERB
ajst-8009	20	16	as	as	ADP
ajst-8009	20	17	a	a	DET
ajst-8009	20	18	warning	warning	NOUN
ajst-8009	20	19	for	for	ADP
ajst-8009	20	20	relevant	relevant	ADJ
ajst-8009	20	21	departments	department	NOUN
ajst-8009	20	22	to	to	PART
ajst-8009	20	23	take	take	VERB
ajst-8009	20	24	measures	measure	NOUN
ajst-8009	20	25	to	to	PART
ajst-8009	20	26	improve	improve	VERB
ajst-8009	20	27	air	air	NOUN
ajst-8009	20	28	quality	quality	NOUN
ajst-8009	21	1	[	[	X
ajst-8009	21	2	2	2	NUM
ajst-8009	21	3	]	]	PUNCT
ajst-8009	21	4	.	.	PUNCT
ajst-8009	22	1	this	this	DET
ajst-8009	22	2	research	research	NOUN
ajst-8009	22	3	topic	topic	NOUN
ajst-8009	22	4	has	have	VERB
ajst-8009	22	5	positive	positive	ADJ
ajst-8009	22	6	significance	significance	NOUN
ajst-8009	22	7	for	for	ADP
ajst-8009	22	8	the	the	DET
ajst-8009	22	9	long	long	ADJ
ajst-8009	22	10	-	-	PUNCT
ajst-8009	22	11	term	term	NOUN
ajst-8009	22	12	development	development	NOUN
ajst-8009	22	13	of	of	ADP
ajst-8009	22	14	public	public	ADJ
ajst-8009	22	15	health	health	NOUN
ajst-8009	22	16	,	,	PUNCT
ajst-8009	22	17	the	the	DET
ajst-8009	22	18	economy	economy	NOUN
ajst-8009	22	19	,	,	PUNCT
ajst-8009	22	20	and	and	CCONJ
ajst-8009	22	21	the	the	DET
ajst-8009	22	22	ecological	ecological	ADJ
ajst-8009	22	23	environment	environment	NOUN
ajst-8009	22	24	.	.	PUNCT
ajst-8009	23	1	at	at	ADP
ajst-8009	23	2	the	the	DET
ajst-8009	23	3	same	same	ADJ
ajst-8009	23	4	time	time	NOUN
ajst-8009	23	5	,	,	PUNCT
ajst-8009	23	6	it	it	PRON
ajst-8009	23	7	presents	present	VERB
ajst-8009	23	8	significant	significant	ADJ
ajst-8009	23	9	challenges	challenge	NOUN
ajst-8009	23	10	to	to	ADP
ajst-8009	23	11	the	the	DET
ajst-8009	23	12	accuracy	accuracy	NOUN
ajst-8009	23	13	and	and	CCONJ
ajst-8009	23	14	stability	stability	NOUN
ajst-8009	23	15	of	of	ADP
ajst-8009	23	16	the	the	DET
ajst-8009	23	17	models	model	NOUN
ajst-8009	23	18	,	,	PUNCT
ajst-8009	23	19	making	make	VERB
ajst-8009	23	20	it	it	PRON
ajst-8009	23	21	a	a	DET
ajst-8009	23	22	profound	profound	ADJ
ajst-8009	23	23	and	and	CCONJ
ajst-8009	23	24	meaningful	meaningful	ADJ
ajst-8009	23	25	research	research	NOUN
ajst-8009	23	26	topic	topic	NOUN
ajst-8009	23	27	.	.	PUNCT
ajst-8009	24	1	against	against	ADP
ajst-8009	24	2	the	the	DET
ajst-8009	24	3	background	background	NOUN
ajst-8009	24	4	of	of	ADP
ajst-8009	24	5	rapid	rapid	ADJ
ajst-8009	24	6	development	development	NOUN
ajst-8009	24	7	in	in	ADP
ajst-8009	24	8	machine	machine	NOUN
ajst-8009	24	9	learning	learning	NOUN
ajst-8009	24	10	,	,	PUNCT
ajst-8009	24	11	researchers	researcher	NOUN
ajst-8009	24	12	have	have	AUX
ajst-8009	24	13	conducted	conduct	VERB
ajst-8009	24	14	many	many	ADJ
ajst-8009	24	15	studies	study	NOUN
ajst-8009	24	16	on	on	ADP
ajst-8009	24	17	air	air	NOUN
ajst-8009	24	18	pollutant	pollutant	ADJ
ajst-8009	24	19	concentration	concentration	NOUN
ajst-8009	24	20	prediction	prediction	NOUN
ajst-8009	24	21	based	base	VERB
ajst-8009	24	22	on	on	ADP
ajst-8009	24	23	different	different	ADJ
ajst-8009	24	24	algorithms	algorithm	NOUN
ajst-8009	24	25	and	and	CCONJ
ajst-8009	24	26	models	model	NOUN
ajst-8009	24	27	.	.	PUNCT
ajst-8009	25	1	for	for	ADP
ajst-8009	25	2	example	example	NOUN
ajst-8009	25	3	,	,	PUNCT
ajst-8009	25	4	zhang	zhang	PROPN
ajst-8009	25	5	et	et	PROPN
ajst-8009	25	6	al	al	PROPN
ajst-8009	25	7	.	.	PUNCT
ajst-8009	26	1	[	[	X
ajst-8009	26	2	3	3	X
ajst-8009	26	3	]	]	PUNCT
ajst-8009	26	4	used	use	VERB
ajst-8009	26	5	the	the	DET
ajst-8009	26	6	pca	pca	NOUN
ajst-8009	26	7	method	method	NOUN
ajst-8009	26	8	for	for	ADP
ajst-8009	26	9	data	datum	NOUN
ajst-8009	26	10	feature	feature	NOUN
ajst-8009	26	11	extraction	extraction	NOUN
ajst-8009	26	12	,	,	PUNCT
ajst-8009	26	13	which	which	PRON
ajst-8009	26	14	improved	improve	VERB
ajst-8009	26	15	the	the	DET
ajst-8009	26	16	prediction	prediction	NOUN
ajst-8009	26	17	accuracy	accuracy	NOUN
ajst-8009	26	18	and	and	CCONJ
ajst-8009	26	19	reduced	reduce	VERB
ajst-8009	26	20	the	the	DET
ajst-8009	26	21	model	model	NOUN
ajst-8009	26	22	's	's	PART
ajst-8009	26	23	time	time	NOUN
ajst-8009	26	24	complexity	complexity	NOUN
ajst-8009	26	25	.	.	PUNCT
ajst-8009	27	1	they	they	PRON
ajst-8009	27	2	input	input	VERB
ajst-8009	27	3	the	the	DET
ajst-8009	27	4	extracted	extract	VERB
ajst-8009	27	5	data	datum	NOUN
ajst-8009	27	6	into	into	ADP
ajst-8009	27	7	a	a	DET
ajst-8009	27	8	bp	bp	PROPN
ajst-8009	27	9	neural	neural	ADJ
ajst-8009	27	10	network	network	NOUN
ajst-8009	27	11	for	for	ADP
ajst-8009	27	12	prediction	prediction	NOUN
ajst-8009	27	13	.	.	PUNCT
ajst-8009	28	1	samal	samal	PROPN
ajst-8009	28	2	et	et	PROPN
ajst-8009	28	3	al	al	PROPN
ajst-8009	28	4	.	.	PUNCT
ajst-8009	29	1	[	[	X
ajst-8009	29	2	4	4	X
ajst-8009	29	3	]	]	PUNCT
ajst-8009	29	4	proposed	propose	VERB
ajst-8009	29	5	the	the	DET
ajst-8009	29	6	multi	multi	ADJ
ajst-8009	29	7	-	-	ADJ
ajst-8009	29	8	time	time	ADJ
ajst-8009	29	9	convolutional	convolutional	ADJ
ajst-8009	29	10	artificial	artificial	ADJ
ajst-8009	29	11	neural	neural	ADJ
ajst-8009	29	12	network	network	NOUN
ajst-8009	29	13	(	(	PUNCT
ajst-8009	29	14	mtcan	mtcan	PROPN
ajst-8009	29	15	)	)	PUNCT
ajst-8009	29	16	model	model	NOUN
ajst-8009	29	17	,	,	PUNCT
ajst-8009	29	18	which	which	PRON
ajst-8009	29	19	can	can	AUX
ajst-8009	29	20	perform	perform	VERB
ajst-8009	29	21	feature	feature	NOUN
ajst-8009	29	22	learning	learning	NOUN
ajst-8009	29	23	and	and	CCONJ
ajst-8009	29	24	sequence	sequence	NOUN
ajst-8009	29	25	modeling	model	VERB
ajst-8009	29	26	simultaneously	simultaneously	ADV
ajst-8009	29	27	,	,	PUNCT
ajst-8009	29	28	and	and	CCONJ
ajst-8009	29	29	use	use	VERB
ajst-8009	29	30	a	a	DET
ajst-8009	29	31	large	large	ADJ
ajst-8009	29	32	amount	amount	NOUN
ajst-8009	29	33	of	of	ADP
ajst-8009	29	34	past	past	ADJ
ajst-8009	29	35	observation	observation	NOUN
ajst-8009	29	36	data	datum	NOUN
ajst-8009	29	37	for	for	ADP
ajst-8009	29	38	long	long	ADJ
ajst-8009	29	39	-	-	PUNCT
ajst-8009	29	40	term	term	NOUN
ajst-8009	29	41	prediction	prediction	NOUN
ajst-8009	29	42	to	to	PART
ajst-8009	29	43	minimize	minimize	VERB
ajst-8009	29	44	memory	memory	NOUN
ajst-8009	29	45	requirements	requirement	NOUN
ajst-8009	29	46	and	and	CCONJ
ajst-8009	29	47	operating	operating	NOUN
ajst-8009	29	48	time	time	NOUN
ajst-8009	29	49	.	.	PUNCT
ajst-8009	30	1	liu	liu	PROPN
ajst-8009	30	2	et	et	PROPN
ajst-8009	30	3	al	al	PROPN
ajst-8009	30	4	.	.	PUNCT
ajst-8009	31	1	[	[	X
ajst-8009	31	2	5	5	NUM
ajst-8009	31	3	]	]	PUNCT
ajst-8009	31	4	optimized	optimize	VERB
ajst-8009	31	5	the	the	DET
ajst-8009	31	6	bp	bp	PROPN
ajst-8009	31	7	neural	neural	ADJ
ajst-8009	31	8	network	network	NOUN
ajst-8009	31	9	using	use	VERB
ajst-8009	31	10	a	a	DET
ajst-8009	31	11	genetic	genetic	ADJ
ajst-8009	31	12	algorithm	algorithm	NOUN
ajst-8009	31	13	and	and	CCONJ
ajst-8009	31	14	constructed	construct	VERB
ajst-8009	31	15	a	a	DET
ajst-8009	31	16	feature	feature	NOUN
ajst-8009	31	17	-	-	PUNCT
ajst-8009	31	18	based	base	VERB
ajst-8009	31	19	pm2.5	pm2.5	PROPN
ajst-8009	31	20	concentration	concentration	NOUN
ajst-8009	31	21	prediction	prediction	NOUN
ajst-8009	31	22	model	model	NOUN
ajst-8009	31	23	.	.	PUNCT
ajst-8009	32	1	li	li	PROPN
ajst-8009	33	1	[	[	X
ajst-8009	33	2	6	6	NUM
ajst-8009	33	3	]	]	PUNCT
ajst-8009	33	4	proposed	propose	VERB
ajst-8009	33	5	an	an	DET
ajst-8009	33	6	ac	ac	PROPN
ajst-8009	33	7	-	-	PUNCT
ajst-8009	33	8	lstm	lstm	ADJ
ajst-8009	33	9	model	model	NOUN
ajst-8009	33	10	composed	compose	VERB
ajst-8009	33	11	of	of	ADP
ajst-8009	33	12	a	a	DET
ajst-8009	33	13	onedimensional	onedimensional	ADJ
ajst-8009	33	14	convolutional	convolutional	ADJ
ajst-8009	33	15	neural	neural	ADJ
ajst-8009	33	16	network	network	NOUN
ajst-8009	33	17	,	,	PUNCT
ajst-8009	33	18	long	long	ADJ
ajst-8009	33	19	short	short	ADJ
ajst-8009	33	20	-	-	PUNCT
ajst-8009	33	21	term	term	NOUN
ajst-8009	33	22	memory	memory	NOUN
ajst-8009	33	23	network	network	NOUN
ajst-8009	33	24	,	,	PUNCT
ajst-8009	33	25	and	and	CCONJ
ajst-8009	33	26	attention	attention	NOUN
ajst-8009	33	27	mechanism	mechanism	NOUN
ajst-8009	33	28	.	.	PUNCT
ajst-8009	34	1	this	this	DET
ajst-8009	34	2	model	model	NOUN
ajst-8009	34	3	not	not	PART
ajst-8009	34	4	only	only	ADV
ajst-8009	34	5	uses	use	VERB
ajst-8009	34	6	air	air	NOUN
ajst-8009	34	7	pollutant	pollutant	ADJ
ajst-8009	34	8	concentration	concentration	NOUN
ajst-8009	34	9	but	but	CCONJ
ajst-8009	34	10	also	also	ADV
ajst-8009	34	11	adds	add	VERB
ajst-8009	34	12	pm2.5	pm2.5	DET
ajst-8009	34	13	concentration	concentration	NOUN
ajst-8009	34	14	from	from	ADP
ajst-8009	34	15	neighboring	neighbor	VERB
ajst-8009	34	16	air	air	NOUN
ajst-8009	34	17	quality	quality	NOUN
ajst-8009	34	18	monitoring	monitoring	NOUN
ajst-8009	34	19	stations	station	NOUN
ajst-8009	34	20	as	as	ADP
ajst-8009	34	21	prediction	prediction	NOUN
ajst-8009	34	22	data	datum	NOUN
ajst-8009	34	23	.	.	PUNCT
ajst-8009	35	1	they	they	PRON
ajst-8009	35	2	used	use	VERB
ajst-8009	35	3	cnn	cnn	PROPN
ajst-8009	35	4	and	and	CCONJ
ajst-8009	35	5	lstm	lstm	NOUN
ajst-8009	35	6	to	to	PART
ajst-8009	35	7	extract	extract	VERB
ajst-8009	35	8	the	the	DET
ajst-8009	35	9	spatio	spatio	PROPN
ajst-8009	35	10	-	-	PUNCT
ajst-8009	35	11	temporal	temporal	ADJ
ajst-8009	35	12	correlation	correlation	NOUN
ajst-8009	35	13	and	and	CCONJ
ajst-8009	35	14	interdependence	interdependence	NOUN
ajst-8009	35	15	of	of	ADP
ajst-8009	35	16	multi	multi	ADJ
ajst-8009	35	17	-	-	ADJ
ajst-8009	35	18	variable	variable	ADJ
ajst-8009	35	19	time	time	NOUN
ajst-8009	35	20	series	series	PROPN
ajst-8009	35	21	data	data	PROPN
ajst-8009	35	22	,	,	PUNCT
ajst-8009	35	23	and	and	CCONJ
ajst-8009	35	24	used	use	VERB
ajst-8009	35	25	attention	attention	NOUN
ajst-8009	35	26	mechanism	mechanism	NOUN
ajst-8009	35	27	to	to	PART
ajst-8009	35	28	capture	capture	VERB
ajst-8009	35	29	the	the	DET
ajst-8009	35	30	importance	importance	NOUN
ajst-8009	35	31	of	of	ADP
ajst-8009	35	32	different	different	ADJ
ajst-8009	35	33	feature	feature	NOUN
ajst-8009	35	34	states	state	NOUN
ajst-8009	35	35	at	at	ADP
ajst-8009	35	36	different	different	ADJ
ajst-8009	35	37	time	time	NOUN
ajst-8009	35	38	steps	step	NOUN
ajst-8009	35	39	in	in	ADP
ajst-8009	35	40	affecting	affect	VERB
ajst-8009	35	41	future	future	ADJ
ajst-8009	35	42	pm2.5	pm2.5	ADJ
ajst-8009	35	43	concentrations	concentration	NOUN
ajst-8009	35	44	.	.	PUNCT
ajst-8009	36	1	liu	liu	PROPN
ajst-8009	36	2	et	et	PROPN
ajst-8009	36	3	al	al	PROPN
ajst-8009	36	4	.	.	PUNCT
ajst-8009	37	1	[	[	X
ajst-8009	37	2	7	7	X
ajst-8009	37	3	]	]	PUNCT
ajst-8009	37	4	used	use	VERB
ajst-8009	37	5	historical	historical	ADJ
ajst-8009	37	6	air	air	NOUN
ajst-8009	37	7	pollutant	pollutant	ADJ
ajst-8009	37	8	and	and	CCONJ
ajst-8009	37	9	meteorological	meteorological	ADJ
ajst-8009	37	10	data	datum	NOUN
ajst-8009	37	11	for	for	ADP
ajst-8009	37	12	a	a	DET
ajst-8009	37	13	region	region	NOUN
ajst-8009	37	14	to	to	PART
ajst-8009	37	15	construct	construct	VERB
ajst-8009	37	16	an	an	DET
ajst-8009	37	17	lstm	lstm	ADJ
ajst-8009	37	18	prediction	prediction	NOUN
ajst-8009	37	19	model	model	NOUN
ajst-8009	37	20	and	and	CCONJ
ajst-8009	37	21	accurately	accurately	ADV
ajst-8009	37	22	predict	predict	VERB
ajst-8009	37	23	the	the	DET
ajst-8009	37	24	pm2.5	pm2.5	ADJ
ajst-8009	37	25	concentration	concentration	NOUN
ajst-8009	37	26	for	for	ADP
ajst-8009	37	27	1	1	NUM
ajst-8009	37	28	,	,	PUNCT
ajst-8009	37	29	4	4	NUM
ajst-8009	37	30	,	,	PUNCT
ajst-8009	37	31	8	8	NUM
ajst-8009	37	32	,	,	PUNCT
ajst-8009	37	33	and	and	CCONJ
ajst-8009	37	34	12	12	NUM
ajst-8009	37	35	hours	hour	NOUN
ajst-8009	37	36	in	in	ADP
ajst-8009	37	37	the	the	DET
ajst-8009	37	38	future	future	NOUN
ajst-8009	37	39	.	.	PUNCT
ajst-8009	38	1	the	the	DET
ajst-8009	38	2	above	above	ADJ
ajst-8009	38	3	studies	study	NOUN
ajst-8009	38	4	have	have	AUX
ajst-8009	38	5	made	make	VERB
ajst-8009	38	6	certain	certain	ADJ
ajst-8009	38	7	improvements	improvement	NOUN
ajst-8009	38	8	in	in	ADP
ajst-8009	38	9	predicting	predict	VERB
ajst-8009	38	10	pm2.5	pm2.5	DET
ajst-8009	38	11	concentration	concentration	NOUN
ajst-8009	38	12	,	,	PUNCT
ajst-8009	38	13	but	but	CCONJ
ajst-8009	38	14	these	these	DET
ajst-8009	38	15	studies	study	NOUN
ajst-8009	38	16	often	often	ADV
ajst-8009	38	17	only	only	ADV
ajst-8009	38	18	consider	consider	VERB
ajst-8009	38	19	the	the	DET
ajst-8009	38	20	relevant	relevant	ADJ
ajst-8009	38	21	features	feature	NOUN
ajst-8009	38	22	of	of	ADP
ajst-8009	38	23	a	a	DET
ajst-8009	38	24	single	single	ADJ
ajst-8009	38	25	station	station	NOUN
ajst-8009	38	26	itself	itself	PRON
ajst-8009	38	27	or	or	CCONJ
ajst-8009	38	28	ignore	ignore	VERB
ajst-8009	38	29	the	the	DET
ajst-8009	38	30	correlation	correlation	NOUN
ajst-8009	38	31	between	between	ADP
ajst-8009	38	32	different	different	ADJ
ajst-8009	38	33	time	time	NOUN
ajst-8009	38	34	steps	step	NOUN
ajst-8009	38	35	and	and	CCONJ
ajst-8009	38	36	the	the	DET
ajst-8009	38	37	influence	influence	NOUN
ajst-8009	38	38	of	of	ADP
ajst-8009	38	39	meteorological	meteorological	ADJ
ajst-8009	38	40	factors	factor	NOUN
ajst-8009	38	41	on	on	ADP
ajst-8009	38	42	pm2.5	pm2.5	DET
ajst-8009	38	43	concentration	concentration	NOUN
ajst-8009	38	44	.	.	PUNCT
ajst-8009	39	1	in	in	ADP
ajst-8009	39	2	fact	fact	NOUN
ajst-8009	39	3	,	,	PUNCT
ajst-8009	39	4	pm2.5	pm2.5	DET
ajst-8009	39	5	concentration	concentration	NOUN
ajst-8009	39	6	is	be	AUX
ajst-8009	39	7	not	not	PART
ajst-8009	39	8	only	only	ADV
ajst-8009	39	9	affected	affect	VERB
ajst-8009	39	10	by	by	ADP
ajst-8009	39	11	local	local	ADJ
ajst-8009	39	12	historical	historical	ADJ
ajst-8009	39	13	conditions	condition	NOUN
ajst-8009	39	14	but	but	CCONJ
ajst-8009	39	15	also	also	ADV
ajst-8009	39	16	by	by	ADP
ajst-8009	39	17	the	the	DET
ajst-8009	39	18	transport	transport	NOUN
ajst-8009	39	19	of	of	ADP
ajst-8009	39	20	pollutants	pollutant	NOUN
ajst-8009	39	21	from	from	ADP
ajst-8009	39	22	surrounding	surround	VERB
ajst-8009	39	23	areas	area	NOUN
ajst-8009	39	24	and	and	CCONJ
ajst-8009	39	25	meteorological	meteorological	ADJ
ajst-8009	39	26	factors	factor	NOUN
ajst-8009	39	27	.	.	PUNCT
ajst-8009	40	1	therefore	therefore	ADV
ajst-8009	40	2	,	,	PUNCT
ajst-8009	40	3	this	this	DET
ajst-8009	40	4	paper	paper	NOUN
ajst-8009	40	5	proposes	propose	VERB
ajst-8009	40	6	a	a	DET
ajst-8009	40	7	new	new	ADJ
ajst-8009	40	8	type	type	NOUN
ajst-8009	40	9	of	of	ADP
ajst-8009	40	10	tpa	tpa	NOUN
ajst-8009	40	11	-	-	PUNCT
ajst-8009	40	12	cnn	cnn	PROPN
ajst-8009	40	13	-	-	PUNCT
ajst-8009	40	14	lstm	lstm	ADJ
ajst-8009	40	15	network	network	NOUN
ajst-8009	40	16	,	,	PUNCT
ajst-8009	40	17	which	which	PRON
ajst-8009	40	18	uses	use	VERB
ajst-8009	40	19	historical	historical	ADJ
ajst-8009	40	20	pollutant	pollutant	ADJ
ajst-8009	40	21	concentrations	concentration	NOUN
ajst-8009	40	22	,	,	PUNCT
ajst-8009	40	23	surrounding	surround	VERB
ajst-8009	40	24	pm2.5	pm2.5	DET
ajst-8009	40	25	concentrations	concentration	NOUN
ajst-8009	40	26	,	,	PUNCT
ajst-8009	40	27	and	and	CCONJ
ajst-8009	40	28	meteorological	meteorological	ADJ
ajst-8009	40	29	factors	factor	NOUN
ajst-8009	40	30	as	as	ADP
ajst-8009	40	31	prediction	prediction	NOUN
ajst-8009	40	32	features	feature	NOUN
ajst-8009	40	33	.	.	PUNCT
ajst-8009	41	1	lstm	lstm	NOUN
ajst-8009	41	2	overcomes	overcome	VERB
ajst-8009	41	3	the	the	DET
ajst-8009	41	4	problems	problem	NOUN
ajst-8009	41	5	of	of	ADP
ajst-8009	41	6	gradient	gradient	ADJ
ajst-8009	41	7	explosion	explosion	NOUN
ajst-8009	41	8	and	and	CCONJ
ajst-8009	41	9	vanishing	vanish	VERB
ajst-8009	41	10	in	in	ADP
ajst-8009	41	11	rnn	rnn	NOUN
ajst-8009	41	12	and	and	CCONJ
ajst-8009	41	13	can	can	AUX
ajst-8009	41	14	extract	extract	VERB
ajst-8009	41	15	the	the	DET
ajst-8009	41	16	time	time	NOUN
ajst-8009	41	17	features	feature	NOUN
ajst-8009	41	18	of	of	ADP
ajst-8009	41	19	long	long	ADJ
ajst-8009	41	20	-	-	PUNCT
ajst-8009	41	21	term	term	NOUN
ajst-8009	41	22	prediction	prediction	NOUN
ajst-8009	41	23	.	.	PUNCT
ajst-8009	42	1	cnn	cnn	PROPN
ajst-8009	42	2	can	can	AUX
ajst-8009	42	3	extract	extract	VERB
ajst-8009	42	4	the	the	DET
ajst-8009	42	5	spatiotemporal	spatiotemporal	ADJ
ajst-8009	42	6	features	feature	NOUN
ajst-8009	42	7	in	in	ADP
ajst-8009	42	8	the	the	DET
ajst-8009	42	9	input	input	NOUN
ajst-8009	42	10	data	datum	NOUN
ajst-8009	42	11	and	and	CCONJ
ajst-8009	42	12	the	the	DET
ajst-8009	42	13	relevant	relevant	ADJ
ajst-8009	42	14	173	173	NUM
ajst-8009	42	15	features	feature	NOUN
ajst-8009	42	16	of	of	ADP
ajst-8009	42	17	pm2.5	pm2.5	DET
ajst-8009	42	18	concentration	concentration	NOUN
ajst-8009	42	19	between	between	ADP
ajst-8009	42	20	different	different	ADJ
ajst-8009	42	21	monitoring	monitoring	NOUN
ajst-8009	42	22	stations	station	NOUN
ajst-8009	42	23	.	.	PUNCT
ajst-8009	43	1	the	the	DET
ajst-8009	43	2	time	time	NOUN
ajst-8009	43	3	pattern	pattern	NOUN
ajst-8009	43	4	attention	attention	NOUN
ajst-8009	43	5	mechanism	mechanism	NOUN
ajst-8009	43	6	can	can	AUX
ajst-8009	43	7	capture	capture	VERB
ajst-8009	43	8	periodic	periodic	ADJ
ajst-8009	43	9	features	feature	NOUN
ajst-8009	43	10	in	in	ADP
ajst-8009	43	11	time	time	NOUN
ajst-8009	43	12	series	series	PROPN
ajst-8009	43	13	data	datum	NOUN
ajst-8009	43	14	and	and	CCONJ
ajst-8009	43	15	handle	handle	VERB
ajst-8009	43	16	multiple	multiple	ADJ
ajst-8009	43	17	time	time	NOUN
ajst-8009	43	18	series	series	NOUN
ajst-8009	43	19	inputs	input	NOUN
ajst-8009	43	20	,	,	PUNCT
ajst-8009	43	21	improving	improve	VERB
ajst-8009	43	22	the	the	DET
ajst-8009	43	23	accuracy	accuracy	NOUN
ajst-8009	43	24	,	,	PUNCT
ajst-8009	43	25	adaptability	adaptability	NOUN
ajst-8009	43	26	,	,	PUNCT
ajst-8009	43	27	and	and	CCONJ
ajst-8009	43	28	practicality	practicality	NOUN
ajst-8009	43	29	of	of	ADP
ajst-8009	43	30	the	the	DET
ajst-8009	43	31	model	model	NOUN
ajst-8009	43	32	.	.	PUNCT
ajst-8009	44	1	2	2	X
ajst-8009	44	2	.	.	X
ajst-8009	44	3	data	datum	NOUN
ajst-8009	44	4	and	and	CCONJ
ajst-8009	44	5	method	method	VERB
ajst-8009	44	6	2.1	2.1	NUM
ajst-8009	44	7	.	.	PUNCT
ajst-8009	45	1	data	datum	NOUN
ajst-8009	45	2	description	description	NOUN
ajst-8009	45	3	the	the	DET
ajst-8009	45	4	temporal	temporal	ADJ
ajst-8009	45	5	and	and	CCONJ
ajst-8009	45	6	spatial	spatial	ADJ
ajst-8009	45	7	variations	variation	NOUN
ajst-8009	45	8	of	of	ADP
ajst-8009	45	9	atmospheric	atmospheric	ADJ
ajst-8009	45	10	particulate	particulate	NOUN
ajst-8009	45	11	matter	matter	NOUN
ajst-8009	45	12	(	(	PUNCT
ajst-8009	45	13	pm	pm	NOUN
ajst-8009	45	14	)	)	PUNCT
ajst-8009	45	15	are	be	AUX
ajst-8009	45	16	influenced	influence	VERB
ajst-8009	45	17	by	by	ADP
ajst-8009	45	18	multiple	multiple	ADJ
ajst-8009	45	19	factors	factor	NOUN
ajst-8009	45	20	,	,	PUNCT
ajst-8009	45	21	including	include	VERB
ajst-8009	45	22	pollution	pollution	NOUN
ajst-8009	45	23	sources	source	NOUN
ajst-8009	45	24	,	,	PUNCT
ajst-8009	45	25	meteorological	meteorological	ADJ
ajst-8009	45	26	conditions	condition	NOUN
ajst-8009	45	27	,	,	PUNCT
ajst-8009	45	28	and	and	CCONJ
ajst-8009	45	29	others	other	NOUN
ajst-8009	46	1	[	[	X
ajst-8009	46	2	14,15	14,15	NUM
ajst-8009	46	3	]	]	PUNCT
ajst-8009	46	4	.	.	PUNCT
ajst-8009	47	1	the	the	DET
ajst-8009	47	2	variation	variation	NOUN
ajst-8009	47	3	of	of	ADP
ajst-8009	47	4	pm2.5	pm2.5	DET
ajst-8009	47	5	concentration	concentration	NOUN
ajst-8009	47	6	is	be	AUX
ajst-8009	47	7	not	not	PART
ajst-8009	47	8	only	only	ADV
ajst-8009	47	9	affected	affect	VERB
ajst-8009	47	10	by	by	ADP
ajst-8009	47	11	the	the	DET
ajst-8009	47	12	previous	previous	ADJ
ajst-8009	47	13	atmospheric	atmospheric	ADJ
ajst-8009	47	14	state	state	NOUN
ajst-8009	47	15	and	and	CCONJ
ajst-8009	47	16	pm2.5	pm2.5	PRON
ajst-8009	47	17	concentration	concentration	NOUN
ajst-8009	47	18	but	but	CCONJ
ajst-8009	47	19	also	also	ADV
ajst-8009	47	20	related	relate	VERB
ajst-8009	47	21	to	to	ADP
ajst-8009	47	22	the	the	DET
ajst-8009	47	23	pm2.5	pm2.5	ADJ
ajst-8009	47	24	concentration	concentration	NOUN
ajst-8009	47	25	in	in	ADP
ajst-8009	47	26	adjacent	adjacent	ADJ
ajst-8009	47	27	areas	area	NOUN
ajst-8009	47	28	[	[	X
ajst-8009	47	29	16,17	16,17	NUM
ajst-8009	47	30	]	]	PUNCT
ajst-8009	47	31	.	.	PUNCT
ajst-8009	48	1	therefore	therefore	ADV
ajst-8009	48	2	,	,	PUNCT
ajst-8009	48	3	historical	historical	ADJ
ajst-8009	48	4	air	air	NOUN
ajst-8009	48	5	pollution	pollution	NOUN
ajst-8009	48	6	data	datum	NOUN
ajst-8009	48	7	and	and	CCONJ
ajst-8009	48	8	meteorological	meteorological	ADJ
ajst-8009	48	9	data	datum	NOUN
ajst-8009	48	10	need	need	VERB
ajst-8009	48	11	to	to	PART
ajst-8009	48	12	be	be	AUX
ajst-8009	48	13	considered	consider	VERB
ajst-8009	48	14	in	in	ADP
ajst-8009	48	15	the	the	DET
ajst-8009	48	16	prediction	prediction	NOUN
ajst-8009	48	17	model	model	NOUN
ajst-8009	48	18	.	.	PUNCT
ajst-8009	49	1	in	in	ADP
ajst-8009	49	2	this	this	DET
ajst-8009	49	3	study	study	NOUN
ajst-8009	49	4	,	,	PUNCT
ajst-8009	49	5	we	we	PRON
ajst-8009	49	6	selected	select	VERB
ajst-8009	49	7	35	35	NUM
ajst-8009	49	8	environmental	environmental	ADJ
ajst-8009	49	9	monitoring	monitoring	NOUN
ajst-8009	49	10	stations	station	NOUN
ajst-8009	49	11	in	in	ADP
ajst-8009	49	12	beijing	beijing	PROPN
ajst-8009	49	13	and	and	CCONJ
ajst-8009	49	14	collected	collect	VERB
ajst-8009	49	15	hourly	hourly	ADJ
ajst-8009	49	16	historical	historical	ADJ
ajst-8009	49	17	air	air	NOUN
ajst-8009	49	18	quality	quality	NOUN
ajst-8009	49	19	concentration	concentration	NOUN
ajst-8009	49	20	and	and	CCONJ
ajst-8009	49	21	meteorological	meteorological	ADJ
ajst-8009	49	22	data	datum	NOUN
ajst-8009	49	23	from	from	ADP
ajst-8009	49	24	january	january	PROPN
ajst-8009	49	25	1	1	NUM
ajst-8009	49	26	,	,	PUNCT
ajst-8009	49	27	2018	2018	NUM
ajst-8009	49	28	,	,	PUNCT
ajst-8009	49	29	to	to	ADP
ajst-8009	49	30	december	december	PROPN
ajst-8009	49	31	31	31	NUM
ajst-8009	49	32	,	,	PUNCT
ajst-8009	49	33	2018	2018	NUM
ajst-8009	49	34	,	,	PUNCT
ajst-8009	49	35	with	with	ADP
ajst-8009	49	36	a	a	DET
ajst-8009	49	37	total	total	NOUN
ajst-8009	49	38	of	of	ADP
ajst-8009	49	39	8674	8674	NUM
ajst-8009	49	40	sets	set	NOUN
ajst-8009	49	41	of	of	ADP
ajst-8009	49	42	data	datum	NOUN
ajst-8009	49	43	.	.	PUNCT
ajst-8009	50	1	the	the	DET
ajst-8009	50	2	statistical	statistical	ADJ
ajst-8009	50	3	information	information	NOUN
ajst-8009	50	4	of	of	ADP
ajst-8009	50	5	the	the	DET
ajst-8009	50	6	specific	specific	ADJ
ajst-8009	50	7	data	datum	NOUN
ajst-8009	50	8	is	be	AUX
ajst-8009	50	9	shown	show	VERB
ajst-8009	50	10	in	in	ADP
ajst-8009	50	11	table	table	NOUN
ajst-8009	50	12	1	1	NUM
ajst-8009	50	13	.	.	PUNCT
ajst-8009	51	1	the	the	DET
ajst-8009	51	2	locations	location	NOUN
ajst-8009	51	3	of	of	ADP
ajst-8009	51	4	the	the	DET
ajst-8009	51	5	air	air	NOUN
ajst-8009	51	6	quality	quality	NOUN
ajst-8009	51	7	monitoring	monitoring	NOUN
ajst-8009	51	8	stations	station	NOUN
ajst-8009	51	9	are	be	AUX
ajst-8009	51	10	shown	show	VERB
ajst-8009	51	11	in	in	ADP
ajst-8009	51	12	figure	figure	NOUN
ajst-8009	51	13	1	1	NUM
ajst-8009	51	14	.	.	PUNCT
ajst-8009	52	1	the	the	DET
ajst-8009	52	2	experimental	experimental	ADJ
ajst-8009	52	3	data	datum	NOUN
ajst-8009	52	4	includes	include	VERB
ajst-8009	52	5	concentrations	concentration	NOUN
ajst-8009	52	6	of	of	ADP
ajst-8009	52	7	pm10	pm10	PROPN
ajst-8009	52	8	,	,	PUNCT
ajst-8009	52	9	pm2.5	pm2.5	PROPN
ajst-8009	52	10	,	,	PUNCT
ajst-8009	52	11	so2	so2	PROPN
ajst-8009	52	12	,	,	PUNCT
ajst-8009	52	13	no2	no2	PROPN
ajst-8009	52	14	,	,	PUNCT
ajst-8009	52	15	o3	o3	PROPN
ajst-8009	52	16	,	,	PUNCT
ajst-8009	52	17	and	and	CCONJ
ajst-8009	52	18	co.	co.	VERB
ajst-8009	52	19	the	the	DET
ajst-8009	52	20	detailed	detailed	ADJ
ajst-8009	52	21	characteristics	characteristic	NOUN
ajst-8009	52	22	of	of	ADP
ajst-8009	52	23	the	the	DET
ajst-8009	52	24	air	air	NOUN
ajst-8009	52	25	quality	quality	NOUN
ajst-8009	52	26	monitoring	monitoring	NOUN
ajst-8009	52	27	stations	station	NOUN
ajst-8009	52	28	are	be	AUX
ajst-8009	52	29	also	also	ADV
ajst-8009	52	30	listed	list	VERB
ajst-8009	52	31	in	in	ADP
ajst-8009	52	32	table	table	NOUN
ajst-8009	52	33	1	1	NUM
ajst-8009	52	34	.	.	PUNCT
ajst-8009	53	1	the	the	DET
ajst-8009	53	2	meteorological	meteorological	ADJ
ajst-8009	53	3	data	datum	NOUN
ajst-8009	53	4	includes	include	VERB
ajst-8009	53	5	indicators	indicator	NOUN
ajst-8009	53	6	such	such	ADJ
ajst-8009	53	7	as	as	ADP
ajst-8009	53	8	atmospheric	atmospheric	ADJ
ajst-8009	53	9	pressure	pressure	NOUN
ajst-8009	53	10	,	,	PUNCT
ajst-8009	53	11	temperature	temperature	NOUN
ajst-8009	53	12	,	,	PUNCT
ajst-8009	53	13	wind	wind	NOUN
ajst-8009	53	14	speed	speed	NOUN
ajst-8009	53	15	,	,	PUNCT
ajst-8009	53	16	humidity	humidity	NOUN
ajst-8009	53	17	,	,	PUNCT
ajst-8009	53	18	and	and	CCONJ
ajst-8009	53	19	weather	weather	NOUN
ajst-8009	53	20	.	.	PUNCT
ajst-8009	54	1	data	datum	NOUN
ajst-8009	54	2	sources	source	NOUN
ajst-8009	54	3	include	include	VERB
ajst-8009	54	4	the	the	DET
ajst-8009	54	5	beijing	beijing	PROPN
ajst-8009	54	6	environmental	environmental	PROPN
ajst-8009	54	7	protection	protection	PROPN
ajst-8009	54	8	monitoring	monitoring	NOUN
ajst-8009	54	9	center	center	NOUN
ajst-8009	54	10	and	and	CCONJ
ajst-8009	54	11	the	the	DET
ajst-8009	54	12	european	european	ADJ
ajst-8009	54	13	center	center	PROPN
ajst-8009	54	14	for	for	ADP
ajst-8009	54	15	medium	medium	ADJ
ajst-8009	54	16	-	-	PUNCT
ajst-8009	54	17	range	range	NOUN
ajst-8009	54	18	weather	weather	NOUN
ajst-8009	54	19	forecasting	forecasting	NOUN
ajst-8009	54	20	reanalysis	reanalysis	NOUN
ajst-8009	54	21	data	datum	NOUN
ajst-8009	54	22	.	.	PUNCT
ajst-8009	55	1	for	for	SCONJ
ajst-8009	55	2	individual	individual	ADJ
ajst-8009	55	3	missing	miss	VERB
ajst-8009	55	4	data	datum	NOUN
ajst-8009	55	5	in	in	ADP
ajst-8009	55	6	the	the	DET
ajst-8009	55	7	used	use	VERB
ajst-8009	55	8	data	datum	NOUN
ajst-8009	55	9	,	,	PUNCT
ajst-8009	55	10	linear	linear	ADJ
ajst-8009	55	11	interpolation	interpolation	NOUN
ajst-8009	55	12	,	,	PUNCT
ajst-8009	55	13	pre	pre	ADJ
ajst-8009	55	14	-	-	ADJ
ajst-8009	55	15	filling	filling	ADJ
ajst-8009	55	16	,	,	PUNCT
ajst-8009	55	17	or	or	CCONJ
ajst-8009	55	18	post	post	ADJ
ajst-8009	55	19	-	-	ADJ
ajst-8009	55	20	filling	filling	ADJ
ajst-8009	55	21	methods	method	NOUN
ajst-8009	55	22	were	be	AUX
ajst-8009	55	23	used	use	VERB
ajst-8009	55	24	for	for	ADP
ajst-8009	55	25	filling	filling	NOUN
ajst-8009	55	26	,	,	PUNCT
ajst-8009	55	27	and	and	CCONJ
ajst-8009	55	28	then	then	ADV
ajst-8009	55	29	normalization	normalization	NOUN
ajst-8009	55	30	was	be	AUX
ajst-8009	55	31	carried	carry	VERB
ajst-8009	55	32	out	out	ADP
ajst-8009	55	33	.	.	PUNCT
ajst-8009	56	1	table	table	NOUN
ajst-8009	56	2	1	1	NUM
ajst-8009	56	3	.	.	PUNCT
ajst-8009	56	4	description	description	NOUN
ajst-8009	56	5	of	of	ADP
ajst-8009	56	6	dataset	dataset	ADJ
ajst-8009	56	7	data	data	NOUN
ajst-8009	56	8	type	type	NOUN
ajst-8009	56	9	data	datum	NOUN
ajst-8009	56	10	name	name	NOUN
ajst-8009	56	11	variable	variable	ADJ
ajst-8009	56	12	name	name	NOUN
ajst-8009	56	13	unit	unit	NOUN
ajst-8009	56	14	type	type	NOUN
ajst-8009	56	15	of	of	ADP
ajst-8009	56	16	variable	variable	ADJ
ajst-8009	56	17	air	air	NOUN
ajst-8009	56	18	quality	quality	NOUN
ajst-8009	56	19	data	data	VERB
ajst-8009	56	20	the	the	DET
ajst-8009	56	21	concentration	concentration	NOUN
ajst-8009	56	22	of	of	ADP
ajst-8009	56	23	pm2.5	pm2.5	DET
ajst-8009	56	24	pm2.5	pm2.5	PROPN
ajst-8009	56	25	μg	μg	PROPN
ajst-8009	56	26	/	/	SYM
ajst-8009	56	27	m3	m3	PROPN
ajst-8009	56	28	numerical	numerical	ADJ
ajst-8009	56	29	the	the	DET
ajst-8009	56	30	concentration	concentration	NOUN
ajst-8009	56	31	of	of	ADP
ajst-8009	56	32	pm10	pm10	PROPN
ajst-8009	56	33	pm10	pm10	PROPN
ajst-8009	56	34	μg	μg	PROPN
ajst-8009	56	35	/	/	SYM
ajst-8009	56	36	m3	m3	PROPN
ajst-8009	56	37	numerical	numerical	ADJ
ajst-8009	56	38	the	the	DET
ajst-8009	56	39	concentration	concentration	NOUN
ajst-8009	56	40	of	of	ADP
ajst-8009	56	41	no2	no2	NOUN
ajst-8009	56	42	no2	no2	PROPN
ajst-8009	56	43	μg	μg	PROPN
ajst-8009	56	44	/	/	SYM
ajst-8009	56	45	m3	m3	PROPN
ajst-8009	56	46	numerical	numerical	ADJ
ajst-8009	56	47	the	the	DET
ajst-8009	56	48	concentration	concentration	NOUN
ajst-8009	56	49	of	of	ADP
ajst-8009	56	50	co	co	X
ajst-8009	56	51	co	co	PROPN
ajst-8009	56	52	μg	μg	PROPN
ajst-8009	56	53	/	/	SYM
ajst-8009	56	54	m3	m3	PROPN
ajst-8009	56	55	numerical	numerical	ADJ
ajst-8009	56	56	the	the	DET
ajst-8009	56	57	concentration	concentration	NOUN
ajst-8009	56	58	of	of	ADP
ajst-8009	56	59	o3	o3	PROPN
ajst-8009	56	60	o3	o3	PROPN
ajst-8009	56	61	μg	μg	PROPN
ajst-8009	56	62	/	/	SYM
ajst-8009	56	63	m3	m3	PROPN
ajst-8009	56	64	numerical	numerical	ADJ
ajst-8009	56	65	the	the	DET
ajst-8009	56	66	concentration	concentration	NOUN
ajst-8009	56	67	of	of	ADP
ajst-8009	56	68	so2	so2	PROPN
ajst-8009	56	69	so2	so2	PROPN
ajst-8009	56	70	μg	μg	PROPN
ajst-8009	56	71	/	/	SYM
ajst-8009	56	72	m3	m3	PROPN
ajst-8009	56	73	numerical	numerical	PROPN
ajst-8009	56	74	climatological	climatological	PROPN
ajst-8009	56	75	data	data	PROPN
ajst-8009	56	76	weather	weather	PROPN
ajst-8009	56	77	weather	weather	NOUN
ajst-8009	56	78	category	category	NOUN
ajst-8009	56	79	(	(	PUNCT
ajst-8009	56	80	sunny	sunny	ADJ
ajst-8009	56	81	,	,	PUNCT
ajst-8009	56	82	cloudy	cloudy	ADJ
ajst-8009	56	83	,	,	PUNCT
ajst-8009	56	84	fog	fog	NOUN
ajst-8009	56	85	,	,	PUNCT
ajst-8009	56	86	snow	snow	NOUN
ajst-8009	56	87	,	,	PUNCT
ajst-8009	56	88	rain	rain	NOUN
ajst-8009	56	89	)	)	PUNCT
ajst-8009	56	90	temperature	temperature	NOUN
ajst-8009	56	91	temperature	temperature	NOUN
ajst-8009	57	1	°	°	PROPN
ajst-8009	57	2	c	c	PROPN
ajst-8009	57	3	numerical	numerical	ADJ
ajst-8009	57	4	pressure	pressure	NOUN
ajst-8009	57	5	pressure	pressure	NOUN
ajst-8009	57	6	hpa	hpa	X
ajst-8009	57	7	numerical	numerical	PROPN
ajst-8009	57	8	relative	relative	PROPN
ajst-8009	57	9	humidity	humidity	PROPN
ajst-8009	57	10	relative	relative	ADJ
ajst-8009	57	11	humidity	humidity	PROPN
ajst-8009	57	12	%	%	NOUN
ajst-8009	57	13	numerical	numerical	ADJ
ajst-8009	57	14	wind	wind	NOUN
ajst-8009	57	15	speed	speed	NOUN
ajst-8009	57	16	wind_speed	wind_spee	VERB
ajst-8009	57	17	m	m	PRON
ajst-8009	57	18	/	/	SYM
ajst-8009	57	19	s	s	PART
ajst-8009	57	20	numerical	numerical	ADJ
ajst-8009	57	21	wind	wind	NOUN
ajst-8009	57	22	direction	direction	NOUN
ajst-8009	57	23	wind_direction	wind_direction	NOUN
ajst-8009	57	24	category	category	NOUN
ajst-8009	57	25	(	(	PUNCT
ajst-8009	57	26	none	none	NOUN
ajst-8009	57	27	,	,	PUNCT
ajst-8009	57	28	southwest	southwest	ADJ
ajst-8009	57	29	,	,	PUNCT
ajst-8009	57	30	unstable	unstable	ADJ
ajst-8009	57	31	,	,	PUNCT
ajst-8009	57	32	southeast	southeast	ADJ
ajst-8009	57	33	,	,	PUNCT
ajst-8009	57	34	southwest	southwest	ADJ
ajst-8009	57	35	,	,	PUNCT
ajst-8009	57	36	northwest	northwest	ADJ
ajst-8009	57	37	)	)	PUNCT
ajst-8009	57	38	figure	figure	NOUN
ajst-8009	57	39	1	1	NUM
ajst-8009	57	40	.	.	PUNCT
ajst-8009	58	1	distribution	distribution	NOUN
ajst-8009	58	2	of	of	ADP
ajst-8009	58	3	air	air	NOUN
ajst-8009	58	4	quality	quality	NOUN
ajst-8009	58	5	monitoring	monitoring	NOUN
ajst-8009	58	6	stations	station	NOUN
ajst-8009	58	7	in	in	ADP
ajst-8009	58	8	beijing	beijing	PROPN
ajst-8009	58	9	2.2	2.2	NUM
ajst-8009	58	10	.	.	PUNCT
ajst-8009	59	1	tpa	tpa	NOUN
ajst-8009	59	2	mechanism	mechanism	NOUN
ajst-8009	59	3	the	the	DET
ajst-8009	59	4	proposed	propose	VERB
ajst-8009	59	5	model	model	NOUN
ajst-8009	59	6	utilizes	utilize	VERB
ajst-8009	59	7	one	one	NUM
ajst-8009	59	8	-	-	PUNCT
ajst-8009	59	9	dimensional	dimensional	ADJ
ajst-8009	59	10	cnn	cnn	NOUN
ajst-8009	59	11	to	to	PART
ajst-8009	59	12	learn	learn	VERB
ajst-8009	59	13	the	the	DET
ajst-8009	59	14	temporal	temporal	ADJ
ajst-8009	59	15	pattern	pattern	NOUN
ajst-8009	59	16	information	information	NOUN
ajst-8009	59	17	of	of	ADP
ajst-8009	59	18	time	time	NOUN
ajst-8009	59	19	series	series	PROPN
ajst-8009	59	20	data	data	PROPN
ajst-8009	59	21	,	,	PUNCT
ajst-8009	59	22	referred	refer	VERB
ajst-8009	59	23	to	to	ADP
ajst-8009	59	24	as	as	ADP
ajst-8009	59	25	tpa	tpa	PROPN
ajst-8009	59	26	(	(	PUNCT
ajst-8009	59	27	temporal	temporal	ADJ
ajst-8009	59	28	pattern	pattern	NOUN
ajst-8009	59	29	attention	attention	NOUN
ajst-8009	59	30	)	)	PUNCT
ajst-8009	59	31	,	,	PUNCT
ajst-8009	59	32	as	as	ADP
ajst-8009	59	33	a	a	DET
ajst-8009	59	34	local	local	ADJ
ajst-8009	59	35	feature	feature	NOUN
ajst-8009	59	36	learning	learn	VERB
ajst-8009	59	37	method	method	NOUN
ajst-8009	59	38	in	in	ADP
ajst-8009	59	39	the	the	DET
ajst-8009	59	40	network	network	NOUN
ajst-8009	59	41	.	.	PUNCT
ajst-8009	60	1	the	the	DET
ajst-8009	60	2	tpa	tpa	PROPN
ajst-8009	60	3	mechanism	mechanism	NOUN
ajst-8009	60	4	174	174	NUM
ajst-8009	60	5	structure	structure	NOUN
ajst-8009	60	6	is	be	AUX
ajst-8009	60	7	shown	show	VERB
ajst-8009	60	8	in	in	ADP
ajst-8009	60	9	figure	figure	NOUN
ajst-8009	60	10	2	2	NUM
ajst-8009	60	11	.	.	PUNCT
ajst-8009	61	1	the	the	DET
ajst-8009	61	2	leftmost	leftmost	ADJ
ajst-8009	61	3	arrow	arrow	NOUN
ajst-8009	61	4	in	in	ADP
ajst-8009	61	5	figure	figure	NOUN
ajst-8009	61	6	2	2	NUM
ajst-8009	61	7	represents	represent	VERB
ajst-8009	61	8	the	the	DET
ajst-8009	61	9	processing	processing	NOUN
ajst-8009	61	10	of	of	ADP
ajst-8009	61	11	variables	variable	NOUN
ajst-8009	61	12	,	,	PUNCT
ajst-8009	61	13	and	and	CCONJ
ajst-8009	61	14	each	each	DET
ajst-8009	61	15	row	row	NOUN
ajst-8009	61	16	represents	represent	VERB
ajst-8009	61	17	a	a	DET
ajst-8009	61	18	variable	variable	NOUN
ajst-8009	61	19	's	's	PART
ajst-8009	61	20	time	time	NOUN
ajst-8009	61	21	series	series	PROPN
ajst-8009	61	22	data	data	PROPN
ajst-8009	61	23	.	.	PUNCT
ajst-8009	62	1	the	the	DET
ajst-8009	62	2	time	time	NOUN
ajst-8009	62	3	pattern	pattern	NOUN
ajst-8009	62	4	matrix	matrix	NOUN
ajst-8009	62	5	,	,	PUNCT
ajst-8009	62	6	c	c	PROPN
ajst-8009	63	1	i	i	PRON
ajst-8009	63	2	jh	jh	PROPN
ajst-8009	63	3	of	of	ADP
ajst-8009	63	4	the	the	DET
ajst-8009	63	5	variable	variable	NOUN
ajst-8009	63	6	within	within	ADP
ajst-8009	63	7	the	the	DET
ajst-8009	63	8	convolutional	convolutional	ADJ
ajst-8009	63	9	kernel	kernel	NOUN
ajst-8009	63	10	range	range	NOUN
ajst-8009	63	11	is	be	AUX
ajst-8009	63	12	obtained	obtain	VERB
ajst-8009	63	13	by	by	ADP
ajst-8009	63	14	convolution	convolution	NOUN
ajst-8009	63	15	calculation	calculation	NOUN
ajst-8009	63	16	.	.	PUNCT
ajst-8009	64	1	the	the	DET
ajst-8009	64	2	scoring	scoring	NOUN
ajst-8009	64	3	function	function	NOUN
ajst-8009	64	4	calculates	calculate	VERB
ajst-8009	64	5	the	the	DET
ajst-8009	64	6	score	score	NOUN
ajst-8009	64	7	of	of	ADP
ajst-8009	64	8	the	the	DET
ajst-8009	64	9	time	time	NOUN
ajst-8009	64	10	pattern	pattern	NOUN
ajst-8009	64	11	matrix	matrix	NOUN
ajst-8009	64	12	and	and	CCONJ
ajst-8009	64	13	normalizes	normalize	VERB
ajst-8009	64	14	the	the	DET
ajst-8009	64	15	score	score	NOUN
ajst-8009	64	16	using	use	VERB
ajst-8009	64	17	the	the	DET
ajst-8009	64	18	sigmoid	sigmoid	NOUN
ajst-8009	64	19	function	function	NOUN
ajst-8009	64	20	to	to	PART
ajst-8009	64	21	obtain	obtain	VERB
ajst-8009	64	22	the	the	DET
ajst-8009	64	23	attention	attention	NOUN
ajst-8009	64	24	weight	weight	NOUN
ajst-8009	64	25			NOUN
ajst-8009	64	26	.	.	PUNCT
ajst-8009	65	1	the	the	DET
ajst-8009	65	2	context	context	PROPN
ajst-8009	65	3	vector	vector	PROPN
ajst-8009	65	4	vt	vt	PROPN
ajst-8009	65	5	is	be	AUX
ajst-8009	65	6	obtained	obtain	VERB
ajst-8009	65	7	by	by	ADP
ajst-8009	65	8	combining	combine	VERB
ajst-8009	65	9	the	the	DET
ajst-8009	65	10	time	time	NOUN
ajst-8009	65	11	pattern	pattern	NOUN
ajst-8009	65	12	matrix	matrix	NOUN
ajst-8009	65	13	and	and	CCONJ
ajst-8009	65	14	attention	attention	NOUN
ajst-8009	65	15	weight	weight	NOUN
ajst-8009	65	16	.	.	PUNCT
ajst-8009	66	1	the	the	DET
ajst-8009	66	2	context	context	PROPN
ajst-8009	66	3	vector	vector	PROPN
ajst-8009	66	4	vt	vt	PROPN
ajst-8009	66	5	from	from	ADP
ajst-8009	66	6	the	the	DET
ajst-8009	66	7	encoder	encoder	NOUN
ajst-8009	66	8	and	and	CCONJ
ajst-8009	66	9	the	the	DET
ajst-8009	66	10	hidden	hidden	ADJ
ajst-8009	66	11	state	state	NOUN
ajst-8009	66	12	h	h	NOUN
ajst-8009	66	13	are	be	AUX
ajst-8009	66	14	concatenated	concatenate	VERB
ajst-8009	66	15	and	and	CCONJ
ajst-8009	66	16	connected	connect	VERB
ajst-8009	66	17	with	with	ADP
ajst-8009	66	18	the	the	DET
ajst-8009	66	19	hidden	hidden	ADJ
ajst-8009	66	20	state	state	NOUN
ajst-8009	66	21	s	s	NOUN
ajst-8009	66	22	from	from	ADP
ajst-8009	66	23	the	the	DET
ajst-8009	66	24	decoder	decoder	NOUN
ajst-8009	66	25	.	.	PUNCT
ajst-8009	67	1	the	the	DET
ajst-8009	67	2	output	output	NOUN
ajst-8009	67	3	prediction	prediction	NOUN
ajst-8009	67	4	value	value	NOUN
ajst-8009	67	5	is	be	AUX
ajst-8009	67	6	calculated	calculate	VERB
ajst-8009	67	7	using	use	VERB
ajst-8009	67	8	the	the	DET
ajst-8009	67	9	output	output	NOUN
ajst-8009	67	10	layer	layer	NOUN
ajst-8009	67	11	and	and	CCONJ
ajst-8009	67	12	softmax	softmax	NOUN
ajst-8009	67	13	function	function	NOUN
ajst-8009	67	14	.	.	PUNCT
ajst-8009	68	1	the	the	DET
ajst-8009	68	2	following	follow	VERB
ajst-8009	68	3	describes	describe	VERB
ajst-8009	68	4	in	in	ADP
ajst-8009	68	5	detail	detail	NOUN
ajst-8009	68	6	the	the	DET
ajst-8009	68	7	role	role	NOUN
ajst-8009	68	8	of	of	ADP
ajst-8009	68	9	the	the	DET
ajst-8009	68	10	tpa	tpa	NOUN
ajst-8009	68	11	mechanism	mechanism	NOUN
ajst-8009	68	12	in	in	ADP
ajst-8009	68	13	the	the	DET
ajst-8009	68	14	proposed	propose	VERB
ajst-8009	68	15	model	model	NOUN
ajst-8009	68	16	.	.	PUNCT
ajst-8009	69	1	firstly	firstly	ADV
ajst-8009	69	2	,	,	PUNCT
ajst-8009	69	3	one	one	NUM
ajst-8009	69	4	-	-	PUNCT
ajst-8009	69	5	dimensional	dimensional	ADJ
ajst-8009	69	6	cnn	cnn	NOUN
ajst-8009	69	7	is	be	AUX
ajst-8009	69	8	used	use	VERB
ajst-8009	69	9	for	for	ADP
ajst-8009	69	10	convolution	convolution	NOUN
ajst-8009	69	11	calculation	calculation	NOUN
ajst-8009	69	12	.	.	PUNCT
ajst-8009	70	1	k	k	PROPN
ajst-8009	70	2	filters	filter	NOUN
ajst-8009	70	3	are	be	AUX
ajst-8009	70	4	set	set	VERB
ajst-8009	70	5	,	,	PUNCT
ajst-8009	70	6	and	and	CCONJ
ajst-8009	70	7	the	the	DET
ajst-8009	70	8	kernel	kernel	NOUN
ajst-8009	70	9	size	size	NOUN
ajst-8009	70	10	is	be	AUX
ajst-8009	70	11	1k	1k	NUM
ajst-8009	70	12	.	.	PUNCT
ajst-8009	71	1	t	t	PROPN
ajst-8009	71	2	represents	represent	VERB
ajst-8009	71	3	the	the	DET
ajst-8009	71	4	range	range	NOUN
ajst-8009	71	5	covered	cover	VERB
ajst-8009	71	6	by	by	ADP
ajst-8009	71	7	attention	attention	NOUN
ajst-8009	71	8	,	,	PUNCT
ajst-8009	71	9	usually	usually	ADV
ajst-8009	71	10	set	set	VERB
ajst-8009	71	11	to	to	ADP
ajst-8009	71	12	t	t	PROPN
ajst-8009	71	13	=	=	NOUN
ajst-8009	71	14	w.	w.	NOUN
ajst-8009	71	15	the	the	DET
ajst-8009	71	16	convolution	convolution	NOUN
ajst-8009	71	17	is	be	AUX
ajst-8009	71	18	calculated	calculate	VERB
ajst-8009	71	19	along	along	ADP
ajst-8009	71	20	the	the	DET
ajst-8009	71	21	row	row	NOUN
ajst-8009	71	22	vector	vector	NOUN
ajst-8009	71	23	of	of	ADP
ajst-8009	71	24	the	the	DET
ajst-8009	71	25	hidden	hidden	ADJ
ajst-8009	71	26	state	state	NOUN
ajst-8009	71	27	matrix	matrix	NOUN
ajst-8009	71	28	h	h	NOUN
ajst-8009	71	29	using	use	VERB
ajst-8009	71	30	the	the	DET
ajst-8009	71	31	above	above	ADJ
ajst-8009	71	32	kernel	kernel	NOUN
ajst-8009	71	33	to	to	PART
ajst-8009	71	34	extract	extract	VERB
ajst-8009	71	35	the	the	DET
ajst-8009	71	36	time	time	NOUN
ajst-8009	71	37	pattern	pattern	NOUN
ajst-8009	71	38	matrix	matrix	NOUN
ajst-8009	71	39	,	,	PUNCT
ajst-8009	72	1	c	c	PROPN
ajst-8009	73	1	i	i	PRON
ajst-8009	73	2	jh	jh	PROPN
ajst-8009	73	3	of	of	ADP
ajst-8009	73	4	the	the	DET
ajst-8009	73	5	variable	variable	NOUN
ajst-8009	73	6	within	within	ADP
ajst-8009	73	7	the	the	DET
ajst-8009	73	8	convolutional	convolutional	ADJ
ajst-8009	73	9	kernel	kernel	NOUN
ajst-8009	73	10	range	range	NOUN
ajst-8009	73	11	,	,	PUNCT
ajst-8009	73	12	as	as	SCONJ
ajst-8009	73	13	shown	show	VERB
ajst-8009	73	14	in	in	ADP
ajst-8009	73	15	equation	equation	NOUN
ajst-8009	73	16	(	(	PUNCT
ajst-8009	73	17	1	1	NUM
ajst-8009	73	18	)	)	PUNCT
ajst-8009	73	19	.	.	PUNCT
ajst-8009	74	1			PROPN
ajst-8009	74	2			PROPN
ajst-8009	74	3	,	,	PUNCT
ajst-8009	74	4	,	,	PUNCT
ajst-8009	74	5	,	,	PUNCT
ajst-8009	74	6	1	1	NUM
ajst-8009	74	7	1	1	NUM
ajst-8009	74	8	*	*	PUNCT
ajst-8009	74	9	c	c	VERB
ajst-8009	75	1	i	i	PRON
ajst-8009	75	2	j	j	PROPN
ajst-8009	76	1	j	j	PROPN
ajst-8009	76	2	t	t	PROPN
ajst-8009	76	3	l	l	NOUN
ajst-8009	77	1	i	i	PRON
ajst-8009	77	2	t	t	X
ajst-8009	77	3	l	l	NOUN
ajst-8009	77	4	l	l	NOUN
ajst-8009	77	5	c	c	NOUN
ajst-8009	78	1			X
ajst-8009	78	2			X
ajst-8009	78	3			PUNCT
ajst-8009	78	4			PUNCT
ajst-8009	78	5			PROPN
ajst-8009	78	6			PROPN
ajst-8009	78	7			VERB
ajst-8009	78	8			PROPN
ajst-8009	78	9	h	h	PROPN
ajst-8009	78	10	h	h	PROPN
ajst-8009	78	11	(	(	PUNCT
ajst-8009	78	12	1	1	X
ajst-8009	78	13	)	)	PUNCT
ajst-8009	78	14	figure.2	figure.2	ADJ
ajst-8009	78	15	tpa	tpa	NOUN
ajst-8009	78	16	mechanism	mechanism	NOUN
ajst-8009	78	17	structure	structure	NOUN
ajst-8009	78	18	among	among	ADP
ajst-8009	78	19	them	they	PRON
ajst-8009	78	20	,	,	PUNCT
ajst-8009	78	21	,	,	PUNCT
ajst-8009	78	22	c	c	AUX
ajst-8009	78	23	i	i	PRON
ajst-8009	78	24	jh	jh	PROPN
ajst-8009	78	25	represents	represent	VERB
ajst-8009	78	26	the	the	DET
ajst-8009	78	27	temporal	temporal	ADJ
ajst-8009	78	28	pattern	pattern	NOUN
ajst-8009	78	29	matrix	matrix	NOUN
ajst-8009	78	30	of	of	ADP
ajst-8009	78	31	the	the	DET
ajst-8009	78	32	j	j	PROPN
ajst-8009	78	33	-	-	PUNCT
ajst-8009	78	34	th	th	VERB
ajst-8009	78	35	filter	filter	NOUN
ajst-8009	78	36	for	for	ADP
ajst-8009	78	37	the	the	DET
ajst-8009	78	38	attention	attention	NOUN
ajst-8009	78	39	mechanism	mechanism	NOUN
ajst-8009	78	40	of	of	ADP
ajst-8009	78	41	the	the	DET
ajst-8009	78	42	i	i	PROPN
ajst-8009	78	43	-	-	PUNCT
ajst-8009	78	44	th	th	VERB
ajst-8009	78	45	row	row	NOUN
ajst-8009	78	46	in	in	ADP
ajst-8009	78	47	the	the	DET
ajst-8009	78	48	interval	interval	NOUN
ajst-8009	78	49	[	[	X
ajst-8009	78	50	t-	t-	X
ajst-8009	78	51	,	,	PUNCT
ajst-8009	78	52	t-1	t-1	PROPN
ajst-8009	78	53	]	]	PUNCT
ajst-8009	78	54	through	through	ADP
ajst-8009	78	55	convolution	convolution	NOUN
ajst-8009	78	56	calculation	calculation	NOUN
ajst-8009	78	57	.	.	PUNCT
ajst-8009	79	1			PROPN
ajst-8009	79	2			PROPN
ajst-8009	79	3	,	,	PUNCT
ajst-8009	79	4	1i	1i	NOUN
ajst-8009	79	5	t	t	NOUN
ajst-8009	80	1	l	l	ADP
ajst-8009	80	2			PROPN
ajst-8009	80	3	h	h	NOUN
ajst-8009	80	4	determines	determine	VERB
ajst-8009	80	5	the	the	DET
ajst-8009	80	6	scope	scope	NOUN
ajst-8009	80	7	of	of	ADP
ajst-8009	80	8	the	the	DET
ajst-8009	80	9	convolution	convolution	NOUN
ajst-8009	80	10	calculation	calculation	NOUN
ajst-8009	80	11	.	.	PUNCT
ajst-8009	81	1	the	the	DET
ajst-8009	81	2	convolution	convolution	NOUN
ajst-8009	81	3	kernel	kernel	PROPN
ajst-8009	81	4			PROPN
ajst-8009	81	5	,i	,i	X
ajst-8009	81	6	t	t	PROPN
ajst-8009	81	7	lc	lc	PROPN
ajst-8009	81	8			PUNCT
ajst-8009	81	9			PUNCT
ajst-8009	81	10	performs	perform	VERB
ajst-8009	81	11	convolution	convolution	NOUN
ajst-8009	81	12	calculation	calculation	NOUN
ajst-8009	81	13	on	on	ADP
ajst-8009	81	14	the	the	DET
ajst-8009	81	15	data	datum	NOUN
ajst-8009	81	16	within	within	ADP
ajst-8009	81	17	this	this	DET
ajst-8009	81	18	range	range	NOUN
ajst-8009	81	19	.	.	PUNCT
ajst-8009	82	1	the	the	DET
ajst-8009	82	2	learned	learn	VERB
ajst-8009	82	3	temporal	temporal	ADJ
ajst-8009	82	4	patterns	pattern	NOUN
ajst-8009	82	5	are	be	AUX
ajst-8009	82	6	scored	score	VERB
ajst-8009	82	7	using	use	VERB
ajst-8009	82	8	the	the	DET
ajst-8009	82	9	following	follow	VERB
ajst-8009	82	10	formula	formula	NOUN
ajst-8009	82	11	:	:	PUNCT
ajst-8009	82	12			NOUN
ajst-8009	83	1			PROPN
ajst-8009	83	2			PROPN
ajst-8009	84	1			PROPN
ajst-8009	84	2	,	,	PUNCT
ajst-8009	84	3	tc	tc	PRON
ajst-8009	84	4	c	c	NOUN
ajst-8009	85	1	i	i	PRON
ajst-8009	85	2	t	t	VERB
ajst-8009	86	1	i	i	PRON
ajst-8009	86	2	tf	tf	INTJ
ajst-8009	86	3	h	h	NOUN
ajst-8009	86	4	h	h	NOUN
ajst-8009	86	5	h	h	PROPN
ajst-8009	87	1	w	w	PROPN
ajst-8009	87	2	h	h	PROPN
ajst-8009	87	3	(	(	PUNCT
ajst-8009	87	4	2	2	NUM
ajst-8009	87	5	)	)	PUNCT
ajst-8009	87	6	where	where	SCONJ
ajst-8009	87	7	c	c	AUX
ajst-8009	87	8	ih	ih	PROPN
ajst-8009	87	9	represents	represent	VERB
ajst-8009	87	10	the	the	DET
ajst-8009	87	11	i	i	PROPN
ajst-8009	87	12	-	-	PUNCT
ajst-8009	87	13	th	th	X
ajst-8009	87	14	row	row	NOUN
ajst-8009	87	15	of	of	ADP
ajst-8009	87	16	the	the	DET
ajst-8009	87	17	time	time	NOUN
ajst-8009	87	18	mode	mode	NOUN
ajst-8009	87	19	matrix	matrix	NOUN
ajst-8009	87	20	ch	ch	NOUN
ajst-8009	87	21	,	,	PUNCT
ajst-8009	87	22	th	th	X
ajst-8009	87	23	represents	represent	VERB
ajst-8009	87	24	the	the	DET
ajst-8009	87	25	hidden	hidden	ADJ
ajst-8009	87	26	state	state	NOUN
ajst-8009	87	27	of	of	ADP
ajst-8009	87	28	the	the	DET
ajst-8009	87	29	encoder	encoder	NOUN
ajst-8009	87	30	output	output	NOUN
ajst-8009	87	31	,	,	PUNCT
ajst-8009	87	32	and	and	CCONJ
ajst-8009	87	33	w	w	NOUN
ajst-8009	87	34	represents	represent	VERB
ajst-8009	87	35	the	the	DET
ajst-8009	87	36	weight	weight	NOUN
ajst-8009	87	37	parameter	parameter	NOUN
ajst-8009	87	38	.	.	PUNCT
ajst-8009	88	1	the	the	DET
ajst-8009	88	2	attention	attention	NOUN
ajst-8009	88	3	weights	weight	VERB
ajst-8009	88	4	i	i	NOUN
ajst-8009	88	5	are	be	AUX
ajst-8009	88	6	:	:	PUNCT
ajst-8009	88	7			PROPN
ajst-8009	88	8			NOUN
ajst-8009	88	9	,ci	,ci	PRON
ajst-8009	89	1	i	i	PRON
ajst-8009	89	2	tsigmoid	tsigmoid	VERB
ajst-8009	90	1	f	f	PROPN
ajst-8009	90	2			PROPN
ajst-8009	90	3	h	h	NOUN
ajst-8009	90	4	h	h	NOUN
ajst-8009	90	5	(	(	PUNCT
ajst-8009	90	6	3	3	X
ajst-8009	90	7	)	)	PUNCT
ajst-8009	90	8	weighted	weight	VERB
ajst-8009	90	9	sum	sum	NOUN
ajst-8009	90	10	each	each	DET
ajst-8009	90	11	row	row	NOUN
ajst-8009	90	12	of	of	ADP
ajst-8009	90	13	hcc	hcc	PROPN
ajst-8009	90	14	to	to	PART
ajst-8009	90	15	obtain	obtain	VERB
ajst-8009	90	16	the	the	DET
ajst-8009	90	17	context	context	NOUN
ajst-8009	90	18	vector	vector	NOUN
ajst-8009	90	19	tv	tv	NOUN
ajst-8009	90	20	:	:	PUNCT
ajst-8009	90	21	1	1	NUM
ajst-8009	90	22	m	m	NOUN
ajst-8009	90	23	c	c	NOUN
ajst-8009	90	24	t	t	NOUN
ajst-8009	91	1	i	i	PRON
ajst-8009	91	2	i	i	PRON
ajst-8009	91	3	i	i	PRON
ajst-8009	91	4			X
ajst-8009	91	5			NUM
ajst-8009	91	6	v	v	NOUN
ajst-8009	91	7	h	h	NOUN
ajst-8009	91	8	(	(	PUNCT
ajst-8009	91	9	4	4	NUM
ajst-8009	91	10	)	)	PUNCT
ajst-8009	91	11	among	among	ADP
ajst-8009	91	12	them	they	PRON
ajst-8009	91	13	,	,	PUNCT
ajst-8009	91	14	n	n	X
ajst-8009	91	15	is	be	AUX
ajst-8009	91	16	the	the	DET
ajst-8009	91	17	number	number	NOUN
ajst-8009	91	18	of	of	ADP
ajst-8009	91	19	features	feature	NOUN
ajst-8009	91	20	of	of	ADP
ajst-8009	91	21	the	the	DET
ajst-8009	91	22	input	input	NOUN
ajst-8009	91	23	variable	variable	NOUN
ajst-8009	91	24	x.	x.	NOUN
ajst-8009	92	1	the	the	DET
ajst-8009	92	2	output	output	NOUN
ajst-8009	92	3	value	value	NOUN
ajst-8009	92	4	of	of	ADP
ajst-8009	92	5	the	the	DET
ajst-8009	92	6	tpa	tpa	PROPN
ajst-8009	92	7	-	-	PUNCT
ajst-8009	92	8	cnn	cnn	PROPN
ajst-8009	92	9	-	-	PUNCT
ajst-8009	92	10	lstm	lstm	ADJ
ajst-8009	92	11	model	model	NOUN
ajst-8009	92	12	is	be	AUX
ajst-8009	92	13	obtained	obtain	VERB
ajst-8009	92	14	by	by	ADP
ajst-8009	92	15	adding	add	VERB
ajst-8009	92	16	the	the	DET
ajst-8009	92	17	attention	attention	NOUN
ajst-8009	92	18	vector	vector	NOUN
ajst-8009	92	19	tv	tv	NOUN
ajst-8009	92	20	linearly	linearly	ADV
ajst-8009	92	21	mapped	map	VERB
ajst-8009	92	22	with	with	ADP
ajst-8009	92	23	the	the	DET
ajst-8009	92	24	hidden	hidden	ADJ
ajst-8009	92	25	state	state	NOUN
ajst-8009	92	26	th	th	X
ajst-8009	92	27	of	of	ADP
ajst-8009	92	28	the	the	DET
ajst-8009	92	29	time	time	NOUN
ajst-8009	92	30	step	step	NOUN
ajst-8009	92	31	t	t	PROPN
ajst-8009	92	32	,	,	PUNCT
ajst-8009	92	33	as	as	SCONJ
ajst-8009	92	34	shown	show	VERB
ajst-8009	92	35	in	in	ADP
ajst-8009	92	36	equation	equation	NOUN
ajst-8009	92	37	(	(	PUNCT
ajst-8009	92	38	5	5	NUM
ajst-8009	92	39	)	)	PUNCT
ajst-8009	92	40	.	.	PUNCT
ajst-8009	93	1			PROPN
ajst-8009	93	2	1	1	PROPN
ajst-8009	93	3	t	t	PROPN
ajst-8009	93	4	h	h	PROPN
ajst-8009	94	1	t	t	PROPN
ajst-8009	94	2	v	v	ADP
ajst-8009	94	3	th	th	PROPN
ajst-8009	94	4	y	y	PROPN
ajst-8009	94	5	v	v	NOUN
ajst-8009	94	6			PROPN
ajst-8009	94	7			PROPN
ajst-8009	94	8	w	w	PROPN
ajst-8009	95	1	w	w	PROPN
ajst-8009	95	2	h	h	PROPN
ajst-8009	95	3	w	w	PROPN
ajst-8009	96	1	(	(	PUNCT
ajst-8009	96	2	5	5	NUM
ajst-8009	96	3	)	)	PUNCT
ajst-8009	96	4	in	in	ADP
ajst-8009	96	5	the	the	DET
ajst-8009	96	6	formula	formula	NOUN
ajst-8009	96	7	,	,	PUNCT
ajst-8009	96	8	1ty	1ty	ADJ
ajst-8009	96	9			PROPN
ajst-8009	96	10			PROPN
ajst-8009	96	11	represents	represent	VERB
ajst-8009	96	12	the	the	DET
ajst-8009	96	13	output	output	NOUN
ajst-8009	96	14	value	value	NOUN
ajst-8009	96	15	,	,	PUNCT
ajst-8009	96	16			PUNCT
ajst-8009	96	17	represents	represent	VERB
ajst-8009	96	18	the	the	DET
ajst-8009	96	19	predicted	predict	VERB
ajst-8009	96	20	time	time	NOUN
ajst-8009	96	21	length	length	NOUN
ajst-8009	96	22	,	,	PUNCT
ajst-8009	96	23	and	and	CCONJ
ajst-8009	96	24	hw	hw	PROPN
ajst-8009	96	25	,	,	PUNCT
ajst-8009	96	26	hw	hw	INTJ
ajst-8009	96	27	,	,	PUNCT
ajst-8009	96	28	and	and	CCONJ
ajst-8009	96	29	vw	vw	PROPN
ajst-8009	96	30	represent	represent	VERB
ajst-8009	96	31	the	the	DET
ajst-8009	96	32	weight	weight	NOUN
ajst-8009	96	33	parameter	parameter	NOUN
ajst-8009	96	34	matrices	matrix	NOUN
ajst-8009	96	35	of	of	ADP
ajst-8009	96	36	the	the	DET
ajst-8009	96	37	corresponding	corresponding	ADJ
ajst-8009	96	38	variables	variable	NOUN
ajst-8009	96	39	.	.	PUNCT
ajst-8009	97	1	2.3	2.3	NUM
ajst-8009	97	2	.	.	PUNCT
ajst-8009	98	1	convolutional	convolutional	ADJ
ajst-8009	98	2	neural	neural	ADJ
ajst-8009	98	3	network	network	NOUN
ajst-8009	98	4	convolutional	convolutional	ADJ
ajst-8009	98	5	neural	neural	ADJ
ajst-8009	98	6	network	network	NOUN
ajst-8009	98	7	(	(	PUNCT
ajst-8009	98	8	cnn	cnn	PROPN
ajst-8009	98	9	)	)	PUNCT
ajst-8009	98	10	have	have	AUX
ajst-8009	98	11	achieved	achieve	VERB
ajst-8009	98	12	excellent	excellent	ADJ
ajst-8009	98	13	performance	performance	NOUN
ajst-8009	98	14	in	in	ADP
ajst-8009	98	15	the	the	DET
ajst-8009	98	16	fields	field	NOUN
ajst-8009	98	17	of	of	ADP
ajst-8009	98	18	computer	computer	NOUN
ajst-8009	98	19	vision	vision	NOUN
ajst-8009	98	20	and	and	CCONJ
ajst-8009	98	21	natural	natural	ADJ
ajst-8009	98	22	language	language	NOUN
ajst-8009	98	23	processing	processing	NOUN
ajst-8009	98	24	,	,	PUNCT
ajst-8009	98	25	thanks	thank	NOUN
ajst-8009	98	26	to	to	ADP
ajst-8009	98	27	their	their	PRON
ajst-8009	98	28	powerful	powerful	ADJ
ajst-8009	98	29	feature	feature	NOUN
ajst-8009	98	30	extraction	extraction	NOUN
ajst-8009	98	31	and	and	CCONJ
ajst-8009	98	32	recognition	recognition	NOUN
ajst-8009	98	33	capabilities	capability	NOUN
ajst-8009	98	34	.	.	PUNCT
ajst-8009	99	1	typically	typically	ADV
ajst-8009	99	2	,	,	PUNCT
ajst-8009	99	3	onedimensional	onedimensional	ADJ
ajst-8009	99	4	convolution	convolution	NOUN
ajst-8009	99	5	is	be	AUX
ajst-8009	99	6	used	use	VERB
ajst-8009	99	7	for	for	ADP
ajst-8009	99	8	processing	processing	NOUN
ajst-8009	99	9	time	time	NOUN
ajst-8009	99	10	data	datum	NOUN
ajst-8009	99	11	,	,	PUNCT
ajst-8009	99	12	two	two	NUM
ajst-8009	99	13	-	-	PUNCT
ajst-8009	99	14	dimensional	dimensional	ADJ
ajst-8009	99	15	convolution	convolution	NOUN
ajst-8009	99	16	is	be	AUX
ajst-8009	99	17	used	use	VERB
ajst-8009	99	18	for	for	ADP
ajst-8009	99	19	spatial	spatial	ADJ
ajst-8009	99	20	convolution	convolution	NOUN
ajst-8009	99	21	in	in	ADP
ajst-8009	99	22	images	image	NOUN
ajst-8009	99	23	,	,	PUNCT
ajst-8009	99	24	and	and	CCONJ
ajst-8009	99	25	three	three	NUM
ajst-8009	99	26	-	-	PUNCT
ajst-8009	99	27	dimensional	dimensional	ADJ
ajst-8009	99	28	convolution	convolution	NOUN
ajst-8009	99	29	is	be	AUX
ajst-8009	99	30	used	use	VERB
ajst-8009	99	31	for	for	ADP
ajst-8009	99	32	spatial	spatial	ADJ
ajst-8009	99	33	convolution	convolution	NOUN
ajst-8009	99	34	in	in	ADP
ajst-8009	99	35	three	three	NUM
ajst-8009	99	36	-	-	PUNCT
ajst-8009	99	37	dimensional	dimensional	ADJ
ajst-8009	99	38	space	space	NOUN
ajst-8009	99	39	.	.	PUNCT
ajst-8009	100	1	in	in	ADP
ajst-8009	100	2	this	this	DET
ajst-8009	100	3	paper	paper	NOUN
ajst-8009	100	4	,	,	PUNCT
ajst-8009	100	5	we	we	PRON
ajst-8009	100	6	believe	believe	VERB
ajst-8009	100	7	that	that	SCONJ
ajst-8009	100	8	the	the	DET
ajst-8009	100	9	convolution	convolution	NOUN
ajst-8009	100	10	kernel	kernel	NOUN
ajst-8009	100	11	in	in	ADP
ajst-8009	100	12	cnn	cnn	PROPN
ajst-8009	100	13	is	be	AUX
ajst-8009	100	14	a	a	DET
ajst-8009	100	15	onedimensional	onedimensional	ADJ
ajst-8009	100	16	structure	structure	NOUN
ajst-8009	100	17	.	.	PUNCT
ajst-8009	101	1	by	by	ADP
ajst-8009	101	2	using	use	VERB
ajst-8009	101	3	cnn	cnn	PROPN
ajst-8009	101	4	,	,	PUNCT
ajst-8009	101	5	multi	multi	ADJ
ajst-8009	101	6	-	-	ADJ
ajst-8009	101	7	variable	variable	ADJ
ajst-8009	101	8	time	time	NOUN
ajst-8009	101	9	series	series	PROPN
ajst-8009	101	10	data	datum	NOUN
ajst-8009	101	11	such	such	ADJ
ajst-8009	101	12	as	as	ADP
ajst-8009	101	13	meteorological	meteorological	ADJ
ajst-8009	101	14	data	datum	NOUN
ajst-8009	101	15	,	,	PUNCT
ajst-8009	101	16	air	air	NOUN
ajst-8009	101	17	pollution	pollution	NOUN
ajst-8009	101	18	data	datum	NOUN
ajst-8009	101	19	,	,	PUNCT
ajst-8009	101	20	and	and	CCONJ
ajst-8009	101	21	data	datum	NOUN
ajst-8009	101	22	from	from	ADP
ajst-8009	101	23	adjacent	adjacent	ADJ
ajst-8009	101	24	stations	station	NOUN
ajst-8009	101	25	needed	need	VERB
ajst-8009	101	26	for	for	ADP
ajst-8009	101	27	pm2.5	pm2.5	DET
ajst-8009	101	28	prediction	prediction	NOUN
ajst-8009	101	29	can	can	AUX
ajst-8009	101	30	be	be	AUX
ajst-8009	101	31	input	input	VERB
ajst-8009	101	32	through	through	ADP
ajst-8009	101	33	different	different	ADJ
ajst-8009	101	34	channels	channel	NOUN
ajst-8009	101	35	to	to	PART
ajst-8009	101	36	maximize	maximize	VERB
ajst-8009	101	37	information	information	NOUN
ajst-8009	101	38	retention	retention	NOUN
ajst-8009	101	39	.	.	PUNCT
ajst-8009	102	1	cnn	cnn	PROPN
ajst-8009	102	2	mainly	mainly	ADV
ajst-8009	102	3	consists	consist	VERB
ajst-8009	102	4	of	of	ADP
ajst-8009	102	5	three	three	NUM
ajst-8009	102	6	modules	module	NOUN
ajst-8009	102	7	:	:	PUNCT
ajst-8009	102	8	it	it	PRON
ajst-8009	102	9	is	be	AUX
ajst-8009	102	10	a	a	DET
ajst-8009	102	11	feedforward	feedforward	ADJ
ajst-8009	102	12	neural	neural	ADJ
ajst-8009	102	13	network	network	NOUN
ajst-8009	102	14	.	.	PUNCT
ajst-8009	103	1	its	its	PRON
ajst-8009	103	2	basic	basic	ADJ
ajst-8009	103	3	structure	structure	NOUN
ajst-8009	103	4	consists	consist	VERB
ajst-8009	103	5	of	of	ADP
ajst-8009	103	6	an	an	DET
ajst-8009	103	7	input	input	NOUN
ajst-8009	103	8	layer	layer	NOUN
ajst-8009	103	9	,	,	PUNCT
ajst-8009	103	10	a	a	DET
ajst-8009	103	11	convolutional	convolutional	ADJ
ajst-8009	103	12	layer	layer	NOUN
ajst-8009	103	13	,	,	PUNCT
ajst-8009	103	14	a	a	DET
ajst-8009	103	15	pooling	pool	VERB
ajst-8009	103	16	layer	layer	NOUN
ajst-8009	103	17	,	,	PUNCT
ajst-8009	103	18	a	a	DET
ajst-8009	103	19	fully	fully	ADV
ajst-8009	103	20	connected	connect	VERB
ajst-8009	103	21	layer	layer	NOUN
ajst-8009	103	22	(	(	PUNCT
ajst-8009	103	23	fc	fc	INTJ
ajst-8009	103	24	)	)	PUNCT
ajst-8009	103	25	,	,	PUNCT
ajst-8009	103	26	and	and	CCONJ
ajst-8009	103	27	an	an	DET
ajst-8009	103	28	output	output	NOUN
ajst-8009	103	29	layer	layer	NOUN
ajst-8009	103	30	,	,	PUNCT
ajst-8009	103	31	as	as	SCONJ
ajst-8009	103	32	shown	show	VERB
ajst-8009	103	33	in	in	ADP
ajst-8009	103	34	figure	figure	NOUN
ajst-8009	103	35	3	3	NUM
ajst-8009	103	36	.	.	NOUN
ajst-8009	103	37	175	175	NUM
ajst-8009	103	38	stride	stride	ADJ
ajst-8009	103	39	convolution	convolution	NOUN
ajst-8009	103	40	  	  	SPACE
ajst-8009	103	41	kernel	kernel	PROPN
ajst-8009	103	42	convolution	convolution	NOUN
ajst-8009	103	43	pooling	pooling	NOUN
ajst-8009	103	44	...	...	PUNCT
ajst-8009	103	45	...	...	PUNCT
ajst-8009	104	1	full	full	ADJ
ajst-8009	104	2	 	 	SPACE
ajst-8009	104	3	connection	connection	NOUN
ajst-8009	104	4	figure	figure	NOUN
ajst-8009	104	5	3	3	NUM
ajst-8009	104	6	.	.	PUNCT
ajst-8009	104	7	structure	structure	NOUN
ajst-8009	104	8	of	of	ADP
ajst-8009	104	9	one	one	NUM
ajst-8009	104	10	-	-	PUNCT
ajst-8009	104	11	dimensional	dimensional	ADJ
ajst-8009	104	12	convolutional	convolutional	ADJ
ajst-8009	104	13	neural	neural	ADJ
ajst-8009	104	14	network	network	NOUN
ajst-8009	104	15	cnn	cnn	PROPN
ajst-8009	104	16	has	have	VERB
ajst-8009	104	17	the	the	DET
ajst-8009	104	18	ability	ability	NOUN
ajst-8009	104	19	to	to	PART
ajst-8009	104	20	automatically	automatically	ADV
ajst-8009	104	21	learn	learn	VERB
ajst-8009	104	22	data	data	NOUN
ajst-8009	104	23	features	feature	NOUN
ajst-8009	104	24	and	and	CCONJ
ajst-8009	104	25	has	have	VERB
ajst-8009	104	26	characteristics	characteristic	NOUN
ajst-8009	104	27	such	such	ADJ
ajst-8009	104	28	as	as	ADP
ajst-8009	104	29	local	local	ADJ
ajst-8009	104	30	connectivity	connectivity	NOUN
ajst-8009	104	31	,	,	PUNCT
ajst-8009	104	32	weight	weight	NOUN
ajst-8009	104	33	sharing	sharing	NOUN
ajst-8009	104	34	,	,	PUNCT
ajst-8009	104	35	pooling	pool	VERB
ajst-8009	104	36	operations	operation	NOUN
ajst-8009	104	37	,	,	PUNCT
ajst-8009	104	38	and	and	CCONJ
ajst-8009	104	39	multi	multi	ADJ
ajst-8009	104	40	-	-	ADJ
ajst-8009	104	41	layer	layer	ADJ
ajst-8009	104	42	structure	structure	NOUN
ajst-8009	104	43	.	.	PUNCT
ajst-8009	105	1	these	these	DET
ajst-8009	105	2	features	feature	NOUN
ajst-8009	105	3	significantly	significantly	ADV
ajst-8009	105	4	reduce	reduce	VERB
ajst-8009	105	5	the	the	DET
ajst-8009	105	6	complexity	complexity	NOUN
ajst-8009	105	7	of	of	ADP
ajst-8009	105	8	the	the	DET
ajst-8009	105	9	pm2.5	pm2.5	PRON
ajst-8009	105	10	concentration	concentration	NOUN
ajst-8009	105	11	prediction	prediction	NOUN
ajst-8009	105	12	model	model	NOUN
ajst-8009	105	13	and	and	CCONJ
ajst-8009	105	14	reduce	reduce	VERB
ajst-8009	105	15	overfitting	overfitte	VERB
ajst-8009	105	16	through	through	ADP
ajst-8009	105	17	gradient	gradient	ADJ
ajst-8009	105	18	descent	descent	NOUN
ajst-8009	105	19	optimization	optimization	NOUN
ajst-8009	105	20	,	,	PUNCT
ajst-8009	105	21	thus	thus	ADV
ajst-8009	105	22	improving	improve	VERB
ajst-8009	105	23	generalization	generalization	NOUN
ajst-8009	105	24	ability	ability	NOUN
ajst-8009	105	25	.	.	PUNCT
ajst-8009	106	1	the	the	DET
ajst-8009	106	2	calculation	calculation	NOUN
ajst-8009	106	3	formulas	formula	VERB
ajst-8009	106	4	for	for	ADP
ajst-8009	106	5	the	the	DET
ajst-8009	106	6	convolution	convolution	NOUN
ajst-8009	106	7	layer	layer	NOUN
ajst-8009	106	8	,	,	PUNCT
ajst-8009	106	9	pooling	pool	VERB
ajst-8009	106	10	layer	layer	NOUN
ajst-8009	106	11	,	,	PUNCT
ajst-8009	106	12	and	and	CCONJ
ajst-8009	106	13	fully	fully	ADV
ajst-8009	106	14	connected	connected	ADJ
ajst-8009	106	15	layer	layer	NOUN
ajst-8009	106	16	are	be	AUX
ajst-8009	106	17	:	:	PUNCT
ajst-8009	106	18			NOUN
ajst-8009	106	19	1i	1i	PUNCT
ajst-8009	107	1	i	i	PRON
ajst-8009	107	2	i	i	PRON
ajst-8009	108	1	ih	ih	INTJ
ajst-8009	108	2	f	f	PROPN
ajst-8009	108	3	h	h	NOUN
ajst-8009	109	1	b	b	PROPN
ajst-8009	109	2			PROPN
ajst-8009	110	1	w	w	NOUN
ajst-8009	110	2	(	(	PUNCT
ajst-8009	110	3	6	6	NUM
ajst-8009	110	4	)	)	PUNCT
ajst-8009	110	5			NOUN
ajst-8009	110	6	1i	1i	PUNCT
ajst-8009	111	1	iz	iz	ADP
ajst-8009	111	2	subsampling	subsample	VERB
ajst-8009	111	3	z	z	NOUN
ajst-8009	111	4			X
ajst-8009	111	5	(	(	PUNCT
ajst-8009	111	6	7	7	NUM
ajst-8009	111	7	)	)	PUNCT
ajst-8009	111	8			NOUN
ajst-8009	111	9			PUNCT
ajst-8009	111	10			NOUN
ajst-8009	112	1			NOUN
ajst-8009	113	1	0	0	INTJ
ajst-8009	114	1	;	;	PUNCT
ajst-8009	114	2	,	,	PUNCT
ajst-8009	114	3	i	i	PRON
ajst-8009	114	4	ay	ay	VERB
ajst-8009	114	5	i	i	NOUN
ajst-8009	114	6	l	l	NOUN
ajst-8009	115	1	l	l	NOUN
ajst-8009	116	1	h	h	NOUN
ajst-8009	116	2	w	w	VERB
ajst-8009	116	3			PROPN
ajst-8009	116	4	b	b	PROPN
ajst-8009	116	5	(	(	PUNCT
ajst-8009	116	6	8)	8)	NUM
ajst-8009	116	7	in	in	ADP
ajst-8009	116	8	the	the	DET
ajst-8009	116	9	formula	formula	NOUN
ajst-8009	116	10	,	,	PUNCT
ajst-8009	116	11	ih	ih	PRON
ajst-8009	116	12	represents	represent	VERB
ajst-8009	116	13	a	a	DET
ajst-8009	116	14	convolutional	convolutional	ADJ
ajst-8009	116	15	layer	layer	NOUN
ajst-8009	116	16	,	,	PUNCT
ajst-8009	116	17	iw	iw	PROPN
ajst-8009	116	18	represents	represent	VERB
ajst-8009	116	19	a	a	DET
ajst-8009	116	20	weight	weight	NOUN
ajst-8009	116	21	vector	vector	NOUN
ajst-8009	116	22	,	,	PUNCT
ajst-8009	116	23			PROPN
ajst-8009	116	24	represents	represent	VERB
ajst-8009	116	25	convolution	convolution	NOUN
ajst-8009	116	26	operation	operation	NOUN
ajst-8009	116	27	,	,	PUNCT
ajst-8009	116	28	ib	ib	PROPN
ajst-8009	116	29	represents	represent	VERB
ajst-8009	116	30	bias	bias	NOUN
ajst-8009	116	31	parameters	parameter	NOUN
ajst-8009	116	32	,	,	PUNCT
ajst-8009	116	33	ab	ab	PROPN
ajst-8009	116	34	represents	represent	VERB
ajst-8009	116	35	offset	offset	VERB
ajst-8009	116	36	vector	vector	NOUN
ajst-8009	116	37	,	,	PUNCT
ajst-8009	116	38			NOUN
ajst-8009	116	39	f	f	PROPN
ajst-8009	116	40			PROPN
ajst-8009	116	41	represents	represent	VERB
ajst-8009	116	42	activation	activation	NOUN
ajst-8009	116	43	function	function	NOUN
ajst-8009	116	44	,	,	PUNCT
ajst-8009	116	45	iz	iz	ADP
ajst-8009	116	46	represents	represent	VERB
ajst-8009	116	47	upsampling	upsample	VERB
ajst-8009	116	48	layer	layer	NOUN
ajst-8009	116	49	,	,	PUNCT
ajst-8009	116	50	subsamping	subsamping	NOUN
ajst-8009	116	51	represents	represent	VERB
ajst-8009	116	52	sampling	sample	VERB
ajst-8009	116	53	process	process	NOUN
ajst-8009	116	54	,	,	PUNCT
ajst-8009	116	55			NOUN
ajst-8009	116	56	y	y	PUNCT
ajst-8009	116	57			PROPN
ajst-8009	116	58	represents	represent	VERB
ajst-8009	116	59	output	output	NOUN
ajst-8009	116	60	,	,	PUNCT
ajst-8009	116	61	l	l	NOUN
ajst-8009	116	62	represents	represent	VERB
ajst-8009	116	63	loss	loss	NOUN
ajst-8009	116	64	function	function	NOUN
ajst-8009	116	65	,	,	PUNCT
ajst-8009	116	66	and	and	CCONJ
ajst-8009	116	67	il	il	PROPN
ajst-8009	116	68	represents	represent	VERB
ajst-8009	116	69	class	class	NOUN
ajst-8009	116	70	labels	label	NOUN
ajst-8009	116	71	.	.	PUNCT
ajst-8009	117	1	2.4	2.4	NUM
ajst-8009	117	2	.	.	PUNCT
ajst-8009	118	1	lstm	lstm	NOUN
ajst-8009	118	2	model	model	NOUN
ajst-8009	118	3	the	the	DET
ajst-8009	118	4	lstm	lstm	NOUN
ajst-8009	118	5	controls	control	VERB
ajst-8009	118	6	the	the	DET
ajst-8009	118	7	transmission	transmission	NOUN
ajst-8009	118	8	state	state	NOUN
ajst-8009	118	9	through	through	ADP
ajst-8009	118	10	a	a	DET
ajst-8009	118	11	gate	gate	NOUN
ajst-8009	118	12	structure	structure	NOUN
ajst-8009	118	13	,	,	PUNCT
ajst-8009	118	14	divided	divide	VERB
ajst-8009	118	15	into	into	ADP
ajst-8009	118	16	forgetting	forget	VERB
ajst-8009	118	17	gates	gate	NOUN
ajst-8009	118	18	,	,	PUNCT
ajst-8009	118	19	selective	selective	ADJ
ajst-8009	118	20	memory	memory	NOUN
ajst-8009	118	21	gates	gate	NOUN
ajst-8009	118	22	,	,	PUNCT
ajst-8009	118	23	and	and	CCONJ
ajst-8009	118	24	output	output	NOUN
ajst-8009	118	25	gates	gate	NOUN
ajst-8009	118	26	,	,	PUNCT
ajst-8009	118	27	as	as	SCONJ
ajst-8009	118	28	shown	show	VERB
ajst-8009	118	29	in	in	ADP
ajst-8009	118	30	figure	figure	NOUN
ajst-8009	118	31	4	4	NUM
ajst-8009	118	32	.	.	PUNCT
ajst-8009	118	33	figure	figure	VERB
ajst-8009	118	34	4	4	NUM
ajst-8009	118	35	.	.	PUNCT
ajst-8009	119	1	lstm	lstm	PROPN
ajst-8009	119	2	network	network	NOUN
ajst-8009	119	3	structure	structure	NOUN
ajst-8009	119	4	diagram	diagram	NOUN
ajst-8009	119	5	the	the	DET
ajst-8009	119	6	forgetting	forget	VERB
ajst-8009	119	7	gate	gate	NOUN
ajst-8009	119	8	determines	determine	VERB
ajst-8009	119	9	the	the	DET
ajst-8009	119	10	extent	extent	NOUN
ajst-8009	119	11	to	to	PART
ajst-8009	119	12	which	which	PRON
ajst-8009	119	13	the	the	DET
ajst-8009	119	14	previous	previous	ADJ
ajst-8009	119	15	unit	unit	NOUN
ajst-8009	119	16	state	state	NOUN
ajst-8009	119	17	is	be	AUX
ajst-8009	119	18	forgotten	forget	VERB
ajst-8009	119	19	and	and	CCONJ
ajst-8009	119	20	can	can	AUX
ajst-8009	119	21	be	be	AUX
ajst-8009	119	22	expressed	express	VERB
ajst-8009	119	23	as	as	ADP
ajst-8009	119	24	:	:	PUNCT
ajst-8009	119	25			ADJ
ajst-8009	119	26			NOUN
ajst-8009	120	1	1,t	1,t	NOUN
ajst-8009	121	1	f	f	PROPN
ajst-8009	121	2	t	t	PROPN
ajst-8009	121	3	t	t	PROPN
ajst-8009	121	4	fx	fx	PROPN
ajst-8009	121	5			ADJ
ajst-8009	121	6			PROPN
ajst-8009	121	7	g	g	PROPN
ajst-8009	121	8	w	w	PROPN
ajst-8009	121	9	h	h	PROPN
ajst-8009	121	10	b	b	PROPN
ajst-8009	121	11	(	(	PUNCT
ajst-8009	121	12	9	9	NUM
ajst-8009	121	13	)	)	PUNCT
ajst-8009	121	14	in	in	ADP
ajst-8009	121	15	the	the	DET
ajst-8009	121	16	formula	formula	NOUN
ajst-8009	121	17	:	:	PUNCT
ajst-8009	121	18	tg	tg	PROPN
ajst-8009	121	19	is	be	AUX
ajst-8009	121	20	the	the	DET
ajst-8009	121	21	state	state	NOUN
ajst-8009	121	22	matrix	matrix	NOUN
ajst-8009	121	23	of	of	ADP
ajst-8009	121	24	the	the	DET
ajst-8009	121	25	forget	forget	NOUN
ajst-8009	121	26	gate	gate	NOUN
ajst-8009	121	27	at	at	ADP
ajst-8009	121	28	time	time	NOUN
ajst-8009	121	29	t	t	PROPN
ajst-8009	121	30	;	;	PUNCT
ajst-8009	121	31	1th	1th	NUM
ajst-8009	121	32	is	be	AUX
ajst-8009	121	33	the	the	DET
ajst-8009	121	34	state	state	NOUN
ajst-8009	121	35	matrix	matrix	NOUN
ajst-8009	121	36	of	of	ADP
ajst-8009	121	37	the	the	DET
ajst-8009	121	38	previous	previous	ADJ
ajst-8009	121	39	time	time	NOUN
ajst-8009	121	40	step	step	NOUN
ajst-8009	121	41	's	's	PART
ajst-8009	121	42	state	state	NOUN
ajst-8009	121	43	unit	unit	NOUN
ajst-8009	121	44	;	;	PUNCT
ajst-8009	121	45	tx	tx	PROPN
ajst-8009	121	46	is	be	AUX
ajst-8009	121	47	the	the	DET
ajst-8009	121	48	input	input	NOUN
ajst-8009	121	49	value	value	NOUN
ajst-8009	121	50	at	at	ADP
ajst-8009	121	51	time	time	NOUN
ajst-8009	121	52	t	t	PROPN
ajst-8009	121	53	;	;	PUNCT
ajst-8009	121	54			NOUN
ajst-8009	121	55			VERB
ajst-8009	121	56			NUM
ajst-8009	121	57	is	be	AUX
ajst-8009	121	58	the	the	DET
ajst-8009	121	59	activation	activation	NOUN
ajst-8009	121	60	function	function	NOUN
ajst-8009	121	61	;	;	PUNCT
ajst-8009	121	62	fw	fw	X
ajst-8009	121	63	is	be	AUX
ajst-8009	121	64	the	the	DET
ajst-8009	121	65	weight	weight	NOUN
ajst-8009	121	66	matrix	matrix	NOUN
ajst-8009	121	67	of	of	ADP
ajst-8009	121	68	the	the	DET
ajst-8009	121	69	forget	forget	NOUN
ajst-8009	121	70	gate	gate	NOUN
ajst-8009	121	71	;	;	PUNCT
ajst-8009	121	72	fb	fb	INTJ
ajst-8009	121	73	is	be	AUX
ajst-8009	121	74	the	the	DET
ajst-8009	121	75	constant	constant	ADJ
ajst-8009	121	76	parameter	parameter	NOUN
ajst-8009	121	77	matrix	matrix	NOUN
ajst-8009	121	78	of	of	ADP
ajst-8009	121	79	the	the	DET
ajst-8009	121	80	forget	forget	NOUN
ajst-8009	121	81	gate	gate	NOUN
ajst-8009	121	82	.	.	PUNCT
ajst-8009	122	1	we	we	PRON
ajst-8009	122	2	choose	choose	VERB
ajst-8009	122	3	a	a	DET
ajst-8009	122	4	memory	memory	NOUN
ajst-8009	122	5	gate	gate	NOUN
ajst-8009	122	6	and	and	CCONJ
ajst-8009	122	7	an	an	DET
ajst-8009	122	8	activation	activation	NOUN
ajst-8009	122	9	function	function	NOUN
ajst-8009	122	10	to	to	PART
ajst-8009	122	11	control	control	VERB
ajst-8009	122	12	the	the	DET
ajst-8009	122	13	range	range	NOUN
ajst-8009	122	14	of	of	ADP
ajst-8009	122	15	new	new	ADJ
ajst-8009	122	16	information	information	NOUN
ajst-8009	122	17	being	be	AUX
ajst-8009	122	18	added	add	VERB
ajst-8009	122	19	.	.	PUNCT
ajst-8009	123	1	by	by	ADP
ajst-8009	123	2	using	use	VERB
ajst-8009	123	3	the	the	DET
ajst-8009	123	4	joint	joint	ADJ
ajst-8009	123	5	effect	effect	NOUN
ajst-8009	123	6	of	of	ADP
ajst-8009	123	7	the	the	DET
ajst-8009	123	8	forget	forget	NOUN
ajst-8009	123	9	gate	gate	NOUN
ajst-8009	123	10	and	and	CCONJ
ajst-8009	123	11	the	the	DET
ajst-8009	123	12	memory	memory	NOUN
ajst-8009	123	13	gate	gate	NOUN
ajst-8009	123	14	output	output	NOUN
ajst-8009	123	15	,	,	PUNCT
ajst-8009	123	16	we	we	PRON
ajst-8009	123	17	update	update	VERB
ajst-8009	123	18	the	the	DET
ajst-8009	123	19	unit	unit	NOUN
ajst-8009	123	20	state	state	NOUN
ajst-8009	123	21	of	of	ADP
ajst-8009	123	22	this	this	DET
ajst-8009	123	23	unit	unit	NOUN
ajst-8009	123	24	,	,	PUNCT
ajst-8009	123	25	which	which	PRON
ajst-8009	123	26	can	can	AUX
ajst-8009	123	27	be	be	AUX
ajst-8009	123	28	expressed	express	VERB
ajst-8009	123	29	as	as	ADP
ajst-8009	123	30	:	:	PUNCT
ajst-8009	123	31			ADJ
ajst-8009	123	32			NOUN
ajst-8009	123	33	1,t	1,t	NOUN
ajst-8009	123	34	s	s	PART
ajst-8009	123	35	t	t	NOUN
ajst-8009	123	36	t	t	NOUN
ajst-8009	123	37	sx	sx	PROPN
ajst-8009	123	38			ADJ
ajst-8009	123	39			PROPN
ajst-8009	123	40	i	i	PROPN
ajst-8009	123	41	w	w	PROPN
ajst-8009	123	42	h	h	PROPN
ajst-8009	123	43	b	b	PROPN
ajst-8009	123	44	(	(	PUNCT
ajst-8009	123	45	10	10	NUM
ajst-8009	123	46	)	)	PUNCT
ajst-8009	123	47	176	176	NUM
ajst-8009	123	48			NOUN
ajst-8009	123	49			ADJ
ajst-8009	123	50	1tanh	1tanh	NOUN
ajst-8009	123	51	,	,	PUNCT
ajst-8009	123	52	t	t	PROPN
ajst-8009	123	53	c	c	PROPN
ajst-8009	123	54	t	t	PROPN
ajst-8009	123	55	t	t	PROPN
ajst-8009	123	56	cx	cx	PROPN
ajst-8009	123	57			PROPN
ajst-8009	123	58			PROPN
ajst-8009	123	59	�	�	PROPN
ajst-8009	123	60	c	c	PROPN
ajst-8009	123	61	w	w	PROPN
ajst-8009	123	62	h	h	PROPN
ajst-8009	123	63	b	b	PROPN
ajst-8009	123	64	(	(	PUNCT
ajst-8009	123	65	11	11	NUM
ajst-8009	123	66	)	)	PUNCT
ajst-8009	123	67	1	1	NUM
ajst-8009	123	68	t	t	NOUN
ajst-8009	123	69	t	t	NOUN
ajst-8009	123	70	t	t	PROPN
ajst-8009	123	71	t	t	PROPN
ajst-8009	123	72	t	t	PROPN
ajst-8009	123	73			PUNCT
ajst-8009	123	74	�	�	PROPN
ajst-8009	123	75	c	c	PROPN
ajst-8009	123	76	gc	gc	PROPN
ajst-8009	123	77	i	i	PRON
ajst-8009	123	78	c	c	PROPN
ajst-8009	123	79	(	(	PUNCT
ajst-8009	123	80	12	12	NUM
ajst-8009	123	81	)	)	PUNCT
ajst-8009	123	82	where	where	SCONJ
ajst-8009	123	83	:	:	PUNCT
ajst-8009	123	84	ti	ti	PROPN
ajst-8009	123	85	,	,	PUNCT
ajst-8009	123	86	t	t	PROPN
ajst-8009	123	87	�	�	PROPN
ajst-8009	123	88	c	c	PROPN
ajst-8009	123	89	,	,	PUNCT
ajst-8009	123	90	tc	tc	PRON
ajst-8009	123	91	represent	represent	VERB
ajst-8009	123	92	the	the	DET
ajst-8009	123	93	state	state	NOUN
ajst-8009	123	94	matrices	matrix	NOUN
ajst-8009	123	95	of	of	ADP
ajst-8009	123	96	the	the	DET
ajst-8009	123	97	input	input	NOUN
ajst-8009	123	98	gate	gate	NOUN
ajst-8009	123	99	,	,	PUNCT
ajst-8009	123	100	the	the	DET
ajst-8009	123	101	intermediate	intermediate	ADJ
ajst-8009	123	102	quantity	quantity	NOUN
ajst-8009	123	103	input	input	NOUN
ajst-8009	123	104	,	,	PUNCT
ajst-8009	123	105	and	and	CCONJ
ajst-8009	123	106	the	the	DET
ajst-8009	123	107	tuple	tuple	ADJ
ajst-8009	123	108	state	state	NOUN
ajst-8009	123	109	at	at	ADP
ajst-8009	123	110	time	time	NOUN
ajst-8009	123	111	t	t	PROPN
ajst-8009	123	112	,	,	PUNCT
ajst-8009	123	113	tanh	tanh	PROPN
ajst-8009	123	114	respectively	respectively	ADV
ajst-8009	123	115	;	;	PUNCT
ajst-8009	123	116	are	be	AUX
ajst-8009	123	117	hyperbolic	hyperbolic	ADJ
ajst-8009	123	118	tangent	tangent	NOUN
ajst-8009	123	119	activation	activation	NOUN
ajst-8009	123	120	functions	function	NOUN
ajst-8009	123	121	;	;	PUNCT
ajst-8009	123	122	sw	sw	PROPN
ajst-8009	123	123	,	,	PUNCT
ajst-8009	123	124	cw	cw	PRON
ajst-8009	123	125	represent	represent	VERB
ajst-8009	123	126	the	the	DET
ajst-8009	123	127	weight	weight	NOUN
ajst-8009	123	128	matrices	matrix	NOUN
ajst-8009	123	129	of	of	ADP
ajst-8009	123	130	the	the	DET
ajst-8009	123	131	input	input	NOUN
ajst-8009	123	132	gate	gate	NOUN
ajst-8009	123	133	and	and	CCONJ
ajst-8009	123	134	the	the	DET
ajst-8009	123	135	intermediate	intermediate	ADJ
ajst-8009	123	136	quantity	quantity	NOUN
ajst-8009	123	137	output	output	NOUN
ajst-8009	123	138	,	,	PUNCT
ajst-8009	123	139	respectively	respectively	ADV
ajst-8009	123	140	;	;	PUNCT
ajst-8009	123	141	sb	sb	PROPN
ajst-8009	123	142	,	,	PUNCT
ajst-8009	123	143	cb	cb	PROPN
ajst-8009	123	144	are	be	AUX
ajst-8009	123	145	the	the	DET
ajst-8009	123	146	memory	memory	NOUN
ajst-8009	123	147	gate	gate	NOUN
ajst-8009	123	148	constant	constant	ADJ
ajst-8009	123	149	parameter	parameter	NOUN
ajst-8009	123	150	matrices	matrix	NOUN
ajst-8009	123	151	.	.	PUNCT
ajst-8009	124	1	the	the	DET
ajst-8009	124	2	output	output	NOUN
ajst-8009	124	3	gate	gate	NOUN
ajst-8009	124	4	controls	control	VERB
ajst-8009	124	5	the	the	DET
ajst-8009	124	6	degree	degree	NOUN
ajst-8009	124	7	to	to	PART
ajst-8009	124	8	which	which	PRON
ajst-8009	124	9	the	the	DET
ajst-8009	124	10	current	current	ADJ
ajst-8009	124	11	unit	unit	NOUN
ajst-8009	124	12	is	be	AUX
ajst-8009	124	13	filtered	filter	VERB
ajst-8009	124	14	and	and	CCONJ
ajst-8009	124	15	can	can	AUX
ajst-8009	124	16	be	be	AUX
ajst-8009	124	17	expressed	express	VERB
ajst-8009	124	18	as	as	ADP
ajst-8009	124	19	:	:	PUNCT
ajst-8009	124	20			ADJ
ajst-8009	124	21	1,t	1,t	NOUN
ajst-8009	124	22	o	o	NOUN
ajst-8009	124	23	t	t	X
ajst-8009	124	24	t	t	PROPN
ajst-8009	124	25	ox	ox	PROPN
ajst-8009	124	26	b	b	PROPN
ajst-8009	124	27			ADP
ajst-8009	124	28			PROPN
ajst-8009	124	29			NOUN
ajst-8009	124	30	o	o	VERB
ajst-8009	124	31	w	w	PROPN
ajst-8009	124	32	h	h	PROPN
ajst-8009	124	33	(	(	PUNCT
ajst-8009	124	34	13	13	NUM
ajst-8009	124	35	)	)	PUNCT
ajst-8009	124	36			NOUN
ajst-8009	124	37	tanht	tanht	PROPN
ajst-8009	125	1	t	t	NOUN
ajst-8009	125	2	th	th	NOUN
ajst-8009	126	1	o	o	X
ajst-8009	126	2	c	c	NOUN
ajst-8009	126	3	(	(	PUNCT
ajst-8009	126	4	14	14	NUM
ajst-8009	126	5	)	)	PUNCT
ajst-8009	126	6	where	where	SCONJ
ajst-8009	126	7	:	:	PUNCT
ajst-8009	126	8	to	to	PART
ajst-8009	126	9	is	be	AUX
ajst-8009	126	10	the	the	DET
ajst-8009	126	11	state	state	NOUN
ajst-8009	126	12	matrix	matrix	NOUN
ajst-8009	126	13	of	of	ADP
ajst-8009	126	14	the	the	DET
ajst-8009	126	15	output	output	NOUN
ajst-8009	126	16	gate	gate	NOUN
ajst-8009	126	17	at	at	ADP
ajst-8009	126	18	time	time	NOUN
ajst-8009	126	19	t	t	PROPN
ajst-8009	126	20	;	;	PUNCT
ajst-8009	126	21	ow	ow	INTJ
ajst-8009	126	22	is	be	AUX
ajst-8009	126	23	the	the	DET
ajst-8009	126	24	weight	weight	NOUN
ajst-8009	126	25	matrix	matrix	NOUN
ajst-8009	126	26	of	of	ADP
ajst-8009	126	27	the	the	DET
ajst-8009	126	28	output	output	NOUN
ajst-8009	126	29	gate	gate	NOUN
ajst-8009	126	30	;	;	PUNCT
ajst-8009	126	31	ob	ob	NUM
ajst-8009	126	32	is	be	AUX
ajst-8009	126	33	the	the	DET
ajst-8009	126	34	output	output	NOUN
ajst-8009	126	35	gate	gate	NOUN
ajst-8009	126	36	constant	constant	ADJ
ajst-8009	126	37	parameter	parameter	NOUN
ajst-8009	126	38	matrix	matrix	NOUN
ajst-8009	126	39	.	.	PUNCT
ajst-8009	127	1	3	3	X
ajst-8009	127	2	.	.	X
ajst-8009	127	3	tpa	tpa	PROPN
ajst-8009	127	4	-	-	PUNCT
ajst-8009	127	5	cnn	cnn	PROPN
ajst-8009	127	6	-	-	PUNCT
ajst-8009	127	7	lstm	lstm	PROPN
ajst-8009	127	8	model	model	NOUN
ajst-8009	127	9	design	design	PROPN
ajst-8009	127	10	3.1	3.1	NUM
ajst-8009	127	11	.	.	PUNCT
ajst-8009	127	12	model	model	NOUN
ajst-8009	127	13	parameter	parameter	PROPN
ajst-8009	127	14	design	design	VERB
ajst-8009	127	15	the	the	DET
ajst-8009	127	16	parameters	parameter	NOUN
ajst-8009	127	17	of	of	ADP
ajst-8009	127	18	the	the	DET
ajst-8009	127	19	prediction	prediction	NOUN
ajst-8009	127	20	model	model	NOUN
ajst-8009	127	21	were	be	AUX
ajst-8009	127	22	set	set	VERB
ajst-8009	127	23	as	as	SCONJ
ajst-8009	127	24	follows	follow	VERB
ajst-8009	127	25	:	:	PUNCT
ajst-8009	127	26	the	the	DET
ajst-8009	127	27	number	number	NOUN
ajst-8009	127	28	of	of	ADP
ajst-8009	127	29	convolutional	convolutional	ADJ
ajst-8009	127	30	layers	layer	NOUN
ajst-8009	127	31	was	be	AUX
ajst-8009	127	32	1	1	NUM
ajst-8009	127	33	,	,	PUNCT
ajst-8009	127	34	the	the	DET
ajst-8009	127	35	number	number	NOUN
ajst-8009	127	36	of	of	ADP
ajst-8009	127	37	filters	filter	NOUN
ajst-8009	127	38	was	be	AUX
ajst-8009	127	39	16	16	NUM
ajst-8009	127	40	,	,	PUNCT
ajst-8009	127	41	the	the	DET
ajst-8009	127	42	convolutional	convolutional	ADJ
ajst-8009	127	43	kernel	kernel	NOUN
ajst-8009	127	44	size	size	NOUN
ajst-8009	127	45	was	be	AUX
ajst-8009	127	46	5	5	NUM
ajst-8009	127	47	5	5	NUM
ajst-8009	127	48	,	,	PUNCT
ajst-8009	127	49	the	the	DET
ajst-8009	127	50	learning	learning	NOUN
ajst-8009	127	51	rate	rate	NOUN
ajst-8009	127	52	was	be	AUX
ajst-8009	127	53	0.001	0.001	NUM
ajst-8009	127	54	,	,	PUNCT
ajst-8009	127	55	the	the	DET
ajst-8009	127	56	epoch	epoch	NOUN
ajst-8009	127	57	was	be	AUX
ajst-8009	127	58	100	100	NUM
ajst-8009	127	59	,	,	PUNCT
ajst-8009	127	60	the	the	DET
ajst-8009	127	61	batch	batch	NOUN
ajst-8009	127	62	size	size	NOUN
ajst-8009	127	63	was	be	AUX
ajst-8009	127	64	32	32	NUM
ajst-8009	127	65	,	,	PUNCT
ajst-8009	127	66	and	and	CCONJ
ajst-8009	127	67	the	the	DET
ajst-8009	127	68	optimizer	optimizer	NOUN
ajst-8009	127	69	was	be	AUX
ajst-8009	127	70	adam	adam	PROPN
ajst-8009	127	71	.	.	PUNCT
ajst-8009	128	1	the	the	DET
ajst-8009	128	2	parameter	parameter	NOUN
ajst-8009	128	3	selection	selection	NOUN
ajst-8009	128	4	method	method	NOUN
ajst-8009	128	5	was	be	AUX
ajst-8009	128	6	the	the	DET
ajst-8009	128	7	grid	grid	NOUN
ajst-8009	128	8	search	search	NOUN
ajst-8009	128	9	method	method	NOUN
ajst-8009	128	10	.	.	PUNCT
ajst-8009	129	1	3.2	3.2	NUM
ajst-8009	129	2	.	.	PUNCT
ajst-8009	129	3	selection	selection	NOUN
ajst-8009	129	4	of	of	ADP
ajst-8009	129	5	evaluation	evaluation	NOUN
ajst-8009	129	6	indicators	indicator	NOUN
ajst-8009	129	7	the	the	DET
ajst-8009	129	8	root	root	NOUN
ajst-8009	129	9	mean	mean	VERB
ajst-8009	129	10	square	square	ADJ
ajst-8009	129	11	error	error	NOUN
ajst-8009	129	12	(	(	PUNCT
ajst-8009	129	13	rmse	rmse	NOUN
ajst-8009	129	14	)	)	PUNCT
ajst-8009	129	15	(	(	PUNCT
ajst-8009	129	16	15	15	NUM
ajst-8009	129	17	)	)	PUNCT
ajst-8009	129	18	and	and	CCONJ
ajst-8009	129	19	mean	mean	VERB
ajst-8009	129	20	absolute	absolute	ADJ
ajst-8009	129	21	error	error	NOUN
ajst-8009	129	22	(	(	PUNCT
ajst-8009	129	23	mae	mae	PROPN
ajst-8009	129	24	)	)	PUNCT
ajst-8009	129	25	(	(	PUNCT
ajst-8009	129	26	16	16	NUM
ajst-8009	129	27	)	)	PUNCT
ajst-8009	129	28	were	be	AUX
ajst-8009	129	29	chosen	choose	VERB
ajst-8009	129	30	as	as	SCONJ
ajst-8009	129	31	metrics	metric	NOUN
ajst-8009	129	32	to	to	PART
ajst-8009	129	33	evaluate	evaluate	VERB
ajst-8009	129	34	the	the	DET
ajst-8009	129	35	prediction	prediction	NOUN
ajst-8009	129	36	performance	performance	NOUN
ajst-8009	129	37	of	of	ADP
ajst-8009	129	38	the	the	DET
ajst-8009	129	39	tpa	tpa	PROPN
ajst-8009	129	40	-	-	PUNCT
ajst-8009	129	41	cnn	cnn	PROPN
ajst-8009	129	42	-	-	PUNCT
ajst-8009	129	43	lstm	lstm	ADJ
ajst-8009	129	44	model	model	NOUN
ajst-8009	129	45	at	at	ADP
ajst-8009	129	46	each	each	DET
ajst-8009	129	47	site	site	NOUN
ajst-8009	129	48	and	and	CCONJ
ajst-8009	129	49	to	to	PART
ajst-8009	129	50	compare	compare	VERB
ajst-8009	129	51	it	it	PRON
ajst-8009	129	52	with	with	ADP
ajst-8009	129	53	other	other	ADJ
ajst-8009	129	54	models	model	NOUN
ajst-8009	129	55	.	.	PUNCT
ajst-8009	130	1			NOUN
ajst-8009	130	2	2	2	ADJ
ajst-8009	130	3	1	1	NUM
ajst-8009	130	4	1	1	NUM
ajst-8009	130	5	ˆ	ˆ	NOUN
ajst-8009	130	6	n	n	NOUN
ajst-8009	131	1	i	i	PRON
ajst-8009	131	2	i	i	PRON
ajst-8009	132	1	i	i	PRON
ajst-8009	132	2	rmse	rmse	VERB
ajst-8009	132	3	y	y	PROPN
ajst-8009	132	4	y	y	PROPN
ajst-8009	133	1	n	n	CCONJ
ajst-8009	133	2			NUM
ajst-8009	133	3			NUM
ajst-8009	134	1			NOUN
ajst-8009	134	2	(	(	PUNCT
ajst-8009	134	3	15	15	NUM
ajst-8009	134	4	)	)	SYM
ajst-8009	134	5	1	1	NUM
ajst-8009	134	6	1	1	NUM
ajst-8009	134	7	ˆ	ˆ	NOUN
ajst-8009	134	8	n	n	NOUN
ajst-8009	135	1	i	i	PRON
ajst-8009	135	2	i	i	PRON
ajst-8009	136	1	i	i	PRON
ajst-8009	136	2	mae	mae	PROPN
ajst-8009	136	3	y	y	PROPN
ajst-8009	136	4	y	y	PROPN
ajst-8009	137	1	n	n	CCONJ
ajst-8009	137	2			NUM
ajst-8009	137	3			NUM
ajst-8009	138	1			NOUN
ajst-8009	138	2	(	(	PUNCT
ajst-8009	138	3	16	16	NUM
ajst-8009	138	4	)	)	PUNCT
ajst-8009	138	5	where	where	SCONJ
ajst-8009	138	6	,	,	PUNCT
ajst-8009	138	7	iy	iy	INTJ
ajst-8009	138	8	,	,	PUNCT
ajst-8009	138	9	ˆiy	ˆiy	NOUN
ajst-8009	138	10	and	and	CCONJ
ajst-8009	138	11	n	n	PRON
ajst-8009	138	12	denote	denote	VERB
ajst-8009	138	13	the	the	DET
ajst-8009	138	14	observed	observed	ADJ
ajst-8009	138	15	,	,	PUNCT
ajst-8009	138	16	predicted	predict	VERB
ajst-8009	138	17	and	and	CCONJ
ajst-8009	138	18	evaluated	evaluate	VERB
ajst-8009	138	19	sample	sample	NOUN
ajst-8009	138	20	sizes	size	NOUN
ajst-8009	138	21	for	for	ADP
ajst-8009	138	22	each	each	DET
ajst-8009	138	23	station	station	NOUN
ajst-8009	138	24	respectively	respectively	ADV
ajst-8009	138	25	.	.	PUNCT
ajst-8009	139	1	y	y	PROPN
ajst-8009	139	2	is	be	AUX
ajst-8009	139	3	the	the	DET
ajst-8009	139	4	average	average	NOUN
ajst-8009	139	5	of	of	ADP
ajst-8009	139	6	the	the	DET
ajst-8009	139	7	data	datum	NOUN
ajst-8009	139	8	from	from	ADP
ajst-8009	139	9	the	the	DET
ajst-8009	139	10	n	n	NUM
ajst-8009	139	11	observation	observation	NOUN
ajst-8009	139	12	samples	sample	NOUN
ajst-8009	139	13	.	.	PUNCT
ajst-8009	140	1	rmse	rmse	PROPN
ajst-8009	140	2	and	and	CCONJ
ajst-8009	140	3	mae	mae	PROPN
ajst-8009	140	4	are	be	AUX
ajst-8009	140	5	common	common	ADJ
ajst-8009	140	6	evaluation	evaluation	NOUN
ajst-8009	140	7	metrics	metric	NOUN
ajst-8009	140	8	used	use	VERB
ajst-8009	140	9	in	in	ADP
ajst-8009	140	10	time	time	NOUN
ajst-8009	140	11	series	series	PROPN
ajst-8009	140	12	forecasting	forecasting	PROPN
ajst-8009	140	13	.	.	PUNCT
ajst-8009	141	1	the	the	PRON
ajst-8009	141	2	lower	low	ADJ
ajst-8009	141	3	their	their	PRON
ajst-8009	141	4	values	value	NOUN
ajst-8009	141	5	are	be	AUX
ajst-8009	141	6	,	,	PUNCT
ajst-8009	141	7	the	the	PRON
ajst-8009	141	8	better	well	ADJ
ajst-8009	141	9	the	the	DET
ajst-8009	141	10	performance	performance	NOUN
ajst-8009	141	11	of	of	ADP
ajst-8009	141	12	the	the	DET
ajst-8009	141	13	model	model	NOUN
ajst-8009	141	14	.	.	PUNCT
ajst-8009	142	1	3.3	3.3	NUM
ajst-8009	142	2	.	.	PUNCT
ajst-8009	143	1	tpa	tpa	PROPN
ajst-8009	143	2	-	-	PUNCT
ajst-8009	143	3	cnn	cnn	PROPN
ajst-8009	143	4	-	-	PUNCT
ajst-8009	143	5	lstm	lstm	ADJ
ajst-8009	143	6	prediction	prediction	NOUN
ajst-8009	143	7	model	model	NOUN
ajst-8009	143	8	construction	construction	NOUN
ajst-8009	143	9	in	in	ADP
ajst-8009	143	10	order	order	NOUN
ajst-8009	143	11	to	to	PART
ajst-8009	143	12	overcome	overcome	VERB
ajst-8009	143	13	the	the	DET
ajst-8009	143	14	limitation	limitation	NOUN
ajst-8009	143	15	of	of	ADP
ajst-8009	143	16	cnn	cnn	PROPN
ajst-8009	143	17	in	in	ADP
ajst-8009	143	18	capturing	capture	VERB
ajst-8009	143	19	only	only	ADV
ajst-8009	143	20	local	local	ADJ
ajst-8009	143	21	information	information	NOUN
ajst-8009	143	22	,	,	PUNCT
ajst-8009	143	23	this	this	DET
ajst-8009	143	24	paper	paper	NOUN
ajst-8009	143	25	proposes	propose	VERB
ajst-8009	143	26	a	a	DET
ajst-8009	143	27	model	model	NOUN
ajst-8009	143	28	that	that	PRON
ajst-8009	143	29	combines	combine	VERB
ajst-8009	143	30	tpa	tpa	PROPN
ajst-8009	143	31	with	with	ADP
ajst-8009	143	32	cnn	cnn	PROPN
ajst-8009	143	33	.	.	PUNCT
ajst-8009	144	1	the	the	DET
ajst-8009	144	2	model	model	NOUN
ajst-8009	144	3	first	first	ADV
ajst-8009	144	4	performs	perform	VERB
ajst-8009	144	5	feature	feature	NOUN
ajst-8009	144	6	extraction	extraction	NOUN
ajst-8009	144	7	on	on	ADP
ajst-8009	144	8	the	the	DET
ajst-8009	144	9	input	input	NOUN
ajst-8009	144	10	time	time	NOUN
ajst-8009	144	11	series	series	PROPN
ajst-8009	144	12	sample	sample	NOUN
ajst-8009	144	13	and	and	CCONJ
ajst-8009	144	14	then	then	ADV
ajst-8009	144	15	uses	use	VERB
ajst-8009	144	16	it	it	PRON
ajst-8009	144	17	as	as	ADP
ajst-8009	144	18	the	the	DET
ajst-8009	144	19	input	input	NOUN
ajst-8009	144	20	to	to	ADP
ajst-8009	144	21	the	the	DET
ajst-8009	144	22	cnn	cnn	PROPN
ajst-8009	144	23	convolutional	convolutional	ADJ
ajst-8009	144	24	layer	layer	NOUN
ajst-8009	144	25	.	.	PUNCT
ajst-8009	145	1	this	this	DET
ajst-8009	145	2	combination	combination	NOUN
ajst-8009	145	3	can	can	AUX
ajst-8009	145	4	reduce	reduce	VERB
ajst-8009	145	5	the	the	DET
ajst-8009	145	6	computational	computational	ADJ
ajst-8009	145	7	complexity	complexity	NOUN
ajst-8009	145	8	of	of	ADP
ajst-8009	145	9	cnn	cnn	PROPN
ajst-8009	145	10	and	and	CCONJ
ajst-8009	145	11	speed	speed	VERB
ajst-8009	145	12	up	up	ADP
ajst-8009	145	13	the	the	DET
ajst-8009	145	14	feature	feature	NOUN
ajst-8009	145	15	extraction	extraction	NOUN
ajst-8009	145	16	process	process	NOUN
ajst-8009	145	17	.	.	PUNCT
ajst-8009	146	1	after	after	SCONJ
ajst-8009	146	2	the	the	DET
ajst-8009	146	3	cnn	cnn	PROPN
ajst-8009	146	4	performs	perform	VERB
ajst-8009	146	5	the	the	DET
ajst-8009	146	6	second	second	ADJ
ajst-8009	146	7	feature	feature	NOUN
ajst-8009	146	8	extraction	extraction	NOUN
ajst-8009	146	9	,	,	PUNCT
ajst-8009	146	10	the	the	DET
ajst-8009	146	11	output	output	NOUN
ajst-8009	146	12	time	time	NOUN
ajst-8009	146	13	series	series	PROPN
ajst-8009	146	14	is	be	AUX
ajst-8009	146	15	processed	process	VERB
ajst-8009	146	16	for	for	ADP
ajst-8009	146	17	dimensionality	dimensionality	NOUN
ajst-8009	146	18	reduction	reduction	NOUN
ajst-8009	146	19	.	.	PUNCT
ajst-8009	147	1	considering	consider	VERB
ajst-8009	147	2	the	the	DET
ajst-8009	147	3	complex	complex	ADJ
ajst-8009	147	4	model	model	NOUN
ajst-8009	147	5	structure	structure	NOUN
ajst-8009	147	6	of	of	ADP
ajst-8009	147	7	lstm	lstm	PROPN
ajst-8009	147	8	,	,	PUNCT
ajst-8009	147	9	when	when	SCONJ
ajst-8009	147	10	inputting	inputte	VERB
ajst-8009	147	11	long	long	ADJ
ajst-8009	147	12	time	time	NOUN
ajst-8009	147	13	series	series	PROPN
ajst-8009	147	14	,	,	PUNCT
ajst-8009	147	15	the	the	DET
ajst-8009	147	16	model	model	NOUN
ajst-8009	147	17	training	training	NOUN
ajst-8009	147	18	time	time	NOUN
ajst-8009	147	19	is	be	AUX
ajst-8009	147	20	long	long	ADJ
ajst-8009	147	21	.	.	PUNCT
ajst-8009	148	1	however	however	ADV
ajst-8009	148	2	,	,	PUNCT
ajst-8009	148	3	using	use	VERB
ajst-8009	148	4	the	the	DET
ajst-8009	148	5	time	time	NOUN
ajst-8009	148	6	series	series	NOUN
ajst-8009	148	7	extracted	extract	VERB
ajst-8009	148	8	and	and	CCONJ
ajst-8009	148	9	dimensionality	dimensionality	NOUN
ajst-8009	148	10	-	-	PUNCT
ajst-8009	148	11	reduced	reduce	VERB
ajst-8009	148	12	by	by	ADP
ajst-8009	148	13	tpa	tpa	PROPN
ajst-8009	148	14	-	-	PUNCT
ajst-8009	148	15	cnn	cnn	PROPN
ajst-8009	148	16	as	as	SCONJ
ajst-8009	148	17	the	the	DET
ajst-8009	148	18	input	input	NOUN
ajst-8009	148	19	to	to	ADP
ajst-8009	148	20	lstm	lstm	NOUN
ajst-8009	148	21	can	can	AUX
ajst-8009	148	22	improve	improve	VERB
ajst-8009	148	23	the	the	DET
ajst-8009	148	24	accuracy	accuracy	NOUN
ajst-8009	148	25	of	of	ADP
ajst-8009	148	26	lstm	lstm	NOUN
ajst-8009	148	27	prediction	prediction	NOUN
ajst-8009	148	28	and	and	CCONJ
ajst-8009	148	29	reduce	reduce	VERB
ajst-8009	148	30	its	its	PRON
ajst-8009	148	31	operating	operating	NOUN
ajst-8009	148	32	time	time	NOUN
ajst-8009	148	33	.	.	PUNCT
ajst-8009	149	1	the	the	DET
ajst-8009	149	2	prediction	prediction	NOUN
ajst-8009	149	3	process	process	NOUN
ajst-8009	149	4	of	of	ADP
ajst-8009	149	5	the	the	DET
ajst-8009	149	6	entire	entire	ADJ
ajst-8009	149	7	tpa	tpa	NOUN
ajst-8009	149	8	-	-	PUNCT
ajst-8009	149	9	cnn	cnn	NOUN
ajst-8009	149	10	-	-	PUNCT
ajst-8009	149	11	lstm	lstm	ADJ
ajst-8009	149	12	model	model	NOUN
ajst-8009	149	13	is	be	AUX
ajst-8009	149	14	shown	show	VERB
ajst-8009	149	15	in	in	ADP
ajst-8009	149	16	figure	figure	NOUN
ajst-8009	149	17	5	5	NUM
ajst-8009	149	18	.	.	PUNCT
ajst-8009	149	19	meteorological	meteorological	ADJ
ajst-8009	149	20	  	  	SPACE
ajst-8009	149	21	data	data	PROPN
ajst-8009	149	22	air	air	NOUN
ajst-8009	149	23	 	 	SPACE
ajst-8009	149	24	pollutant	pollutant	ADJ
ajst-8009	149	25	 	 	SPACE
ajst-8009	149	26	data	datum	NOUN
ajst-8009	149	27	adjacent	adjacent	ADJ
ajst-8009	149	28	  	  	SPACE
ajst-8009	149	29	monitoring	monitoring	NOUN
ajst-8009	149	30	 	 	SPACE
ajst-8009	149	31	station	station	NOUN
ajst-8009	149	32	  	  	SPACE
ajst-8009	149	33	data	datum	NOUN
ajst-8009	149	34	t1	t1	PROPN
ajst-8009	149	35	t2	t2	PROPN
ajst-8009	149	36	...	...	PUNCT
ajst-8009	149	37	end	end	NOUN
ajst-8009	149	38	t1	t1	PROPN
ajst-8009	149	39	t2	t2	PROPN
ajst-8009	149	40	...	...	PUNCT
ajst-8009	149	41	end	end	NOUN
ajst-8009	149	42	t1	t1	PROPN
ajst-8009	149	43	t2	t2	PROPN
ajst-8009	149	44	...	...	PUNCT
ajst-8009	149	45	end	end	NOUN
ajst-8009	149	46	normalization	normalization	NOUN
ajst-8009	149	47	tpa	tpa	PROPN
ajst-8009	149	48	cnn	cnn	PROPN
ajst-8009	149	49	train	train	NOUN
ajst-8009	149	50	prediciton	prediciton	PROPN
ajst-8009	149	51	feature	feature	NOUN
ajst-8009	149	52	  	  	SPACE
ajst-8009	149	53	extraction	extraction	NOUN
ajst-8009	149	54	lstm	lstm	NOUN
ajst-8009	149	55	denormalization	denormalization	NOUN
ajst-8009	149	56	output	output	NOUN
ajst-8009	149	57	figure	figure	NOUN
ajst-8009	149	58	5	5	NUM
ajst-8009	149	59	.	.	PUNCT
ajst-8009	149	60	flow	flow	VERB
ajst-8009	149	61	chart	chart	NOUN
ajst-8009	149	62	of	of	ADP
ajst-8009	149	63	tpa	tpa	PROPN
ajst-8009	149	64	-	-	PUNCT
ajst-8009	149	65	cnn	cnn	PROPN
ajst-8009	149	66	-	-	PUNCT
ajst-8009	149	67	lstm	lstm	PROPN
ajst-8009	149	68	model	model	NOUN
ajst-8009	149	69	the	the	DET
ajst-8009	149	70	main	main	ADJ
ajst-8009	149	71	steps	step	NOUN
ajst-8009	149	72	of	of	ADP
ajst-8009	149	73	the	the	DET
ajst-8009	149	74	pm2.5	pm2.5	PRON
ajst-8009	149	75	concentration	concentration	NOUN
ajst-8009	149	76	prediction	prediction	NOUN
ajst-8009	149	77	model	model	NOUN
ajst-8009	149	78	based	base	VERB
ajst-8009	149	79	on	on	ADP
ajst-8009	149	80	tpa	tpa	PROPN
ajst-8009	149	81	-	-	PUNCT
ajst-8009	149	82	cnn	cnn	NOUN
ajst-8009	149	83	-	-	PUNCT
ajst-8009	149	84	lstm	lstm	NOUN
ajst-8009	149	85	are	be	AUX
ajst-8009	149	86	as	as	SCONJ
ajst-8009	149	87	follows	follow	VERB
ajst-8009	149	88	:	:	PUNCT
ajst-8009	149	89	step	step	NOUN
ajst-8009	149	90	1	1	NUM
ajst-8009	149	91	:	:	PUNCT
ajst-8009	149	92	correlation	correlation	NOUN
ajst-8009	149	93	analysis	analysis	NOUN
ajst-8009	149	94	.	.	PUNCT
ajst-8009	150	1	analyze	analyze	VERB
ajst-8009	150	2	the	the	DET
ajst-8009	150	3	factors	factor	NOUN
ajst-8009	150	4	affecting	affect	VERB
ajst-8009	150	5	the	the	DET
ajst-8009	150	6	pm2.5	pm2.5	ADJ
ajst-8009	150	7	concentration	concentration	NOUN
ajst-8009	150	8	changes	change	NOUN
ajst-8009	150	9	and	and	CCONJ
ajst-8009	150	10	determine	determine	VERB
ajst-8009	150	11	the	the	DET
ajst-8009	150	12	input	input	NOUN
ajst-8009	150	13	sequence	sequence	NOUN
ajst-8009	150	14	.	.	PUNCT
ajst-8009	151	1	step	step	NOUN
ajst-8009	151	2	2	2	NUM
ajst-8009	151	3	:	:	PUNCT
ajst-8009	151	4	data	datum	NOUN
ajst-8009	151	5	preprocessing	preprocessing	NOUN
ajst-8009	151	6	.	.	PUNCT
ajst-8009	152	1	handle	handle	VERB
ajst-8009	152	2	missing	miss	VERB
ajst-8009	152	3	values	value	NOUN
ajst-8009	152	4	and	and	CCONJ
ajst-8009	152	5	outliers	outlier	NOUN
ajst-8009	152	6	,	,	PUNCT
ajst-8009	152	7	and	and	CCONJ
ajst-8009	152	8	normalize	normalize	VERB
ajst-8009	152	9	the	the	DET
ajst-8009	152	10	input	input	NOUN
ajst-8009	152	11	time	time	NOUN
ajst-8009	152	12	series	series	PROPN
ajst-8009	152	13	.	.	PUNCT
ajst-8009	153	1	step	step	VERB
ajst-8009	153	2	3	3	NUM
ajst-8009	153	3	:	:	PUNCT
ajst-8009	153	4	feature	feature	NOUN
ajst-8009	153	5	extraction	extraction	NOUN
ajst-8009	153	6	.	.	PUNCT
ajst-8009	154	1	use	use	VERB
ajst-8009	154	2	tpa	tpa	PROPN
ajst-8009	154	3	-	-	PUNCT
ajst-8009	154	4	cnn	cnn	PROPN
ajst-8009	154	5	to	to	PART
ajst-8009	154	6	extract	extract	VERB
ajst-8009	154	7	important	important	ADJ
ajst-8009	154	8	features	feature	NOUN
ajst-8009	154	9	from	from	ADP
ajst-8009	154	10	the	the	DET
ajst-8009	154	11	normalized	normalize	VERB
ajst-8009	154	12	time	time	NOUN
ajst-8009	154	13	series	series	PROPN
ajst-8009	154	14	.	.	PUNCT
ajst-8009	155	1	step	step	VERB
ajst-8009	155	2	4	4	NUM
ajst-8009	155	3	:	:	PUNCT
ajst-8009	155	4	pm2.5	pm2.5	PRON
ajst-8009	155	5	concentration	concentration	NOUN
ajst-8009	155	6	prediction	prediction	NOUN
ajst-8009	155	7	.	.	PUNCT
ajst-8009	156	1	input	input	VERB
ajst-8009	156	2	the	the	DET
ajst-8009	156	3	tpa	tpa	PROPN
ajst-8009	156	4	-	-	PUNCT
ajst-8009	156	5	cnn	cnn	PROPN
ajst-8009	156	6	processed	process	VERB
ajst-8009	156	7	time	time	NOUN
ajst-8009	156	8	series	series	NOUN
ajst-8009	156	9	into	into	ADP
ajst-8009	156	10	the	the	DET
ajst-8009	156	11	lstm	lstm	PROPN
ajst-8009	156	12	network	network	NOUN
ajst-8009	156	13	for	for	ADP
ajst-8009	156	14	model	model	NOUN
ajst-8009	156	15	training	training	NOUN
ajst-8009	156	16	.	.	PUNCT
ajst-8009	157	1	step	step	NOUN
ajst-8009	157	2	5	5	NUM
ajst-8009	157	3	:	:	PUNCT
ajst-8009	157	4	output	output	NOUN
ajst-8009	157	5	.	.	PUNCT
ajst-8009	158	1	reverse	reverse	PROPN
ajst-8009	158	2	normalize	normalize	VERB
ajst-8009	158	3	the	the	DET
ajst-8009	158	4	predicted	predict	VERB
ajst-8009	158	5	data	datum	NOUN
ajst-8009	158	6	and	and	CCONJ
ajst-8009	158	7	output	output	VERB
ajst-8009	158	8	the	the	DET
ajst-8009	158	9	results	result	NOUN
ajst-8009	158	10	.	.	PUNCT
ajst-8009	159	1	through	through	ADP
ajst-8009	159	2	the	the	DET
ajst-8009	159	3	above	above	ADJ
ajst-8009	159	4	steps	step	NOUN
ajst-8009	159	5	,	,	PUNCT
ajst-8009	159	6	the	the	DET
ajst-8009	159	7	goal	goal	NOUN
ajst-8009	159	8	of	of	ADP
ajst-8009	159	9	improving	improve	VERB
ajst-8009	159	10	prediction	prediction	NOUN
ajst-8009	159	11	accuracy	accuracy	NOUN
ajst-8009	159	12	and	and	CCONJ
ajst-8009	159	13	computational	computational	ADJ
ajst-8009	159	14	efficiency	efficiency	NOUN
ajst-8009	159	15	is	be	AUX
ajst-8009	159	16	achieved	achieve	VERB
ajst-8009	159	17	.	.	PUNCT
ajst-8009	160	1	177	177	NUM
ajst-8009	160	2	4	4	NUM
ajst-8009	160	3	.	.	PUNCT
ajst-8009	160	4	experimental	experimental	ADJ
ajst-8009	160	5	results	result	NOUN
ajst-8009	160	6	and	and	CCONJ
ajst-8009	160	7	analysis	analysis	NOUN
ajst-8009	160	8	4.1	4.1	NUM
ajst-8009	160	9	.	.	PUNCT
ajst-8009	161	1	correlation	correlation	NOUN
ajst-8009	161	2	analysis	analysis	NOUN
ajst-8009	161	3	this	this	DET
ajst-8009	161	4	paper	paper	NOUN
ajst-8009	161	5	uses	use	VERB
ajst-8009	161	6	the	the	DET
ajst-8009	161	7	pearson	pearson	PROPN
ajst-8009	161	8	correlation	correlation	NOUN
ajst-8009	161	9	coefficient	coefficient	VERB
ajst-8009	161	10	analysis	analysis	NOUN
ajst-8009	161	11	method	method	NOUN
ajst-8009	161	12	to	to	PART
ajst-8009	161	13	analyze	analyze	VERB
ajst-8009	161	14	the	the	DET
ajst-8009	161	15	correlation	correlation	NOUN
ajst-8009	161	16	between	between	ADP
ajst-8009	161	17	various	various	ADJ
ajst-8009	161	18	variables	variable	NOUN
ajst-8009	161	19	and	and	CCONJ
ajst-8009	161	20	pm2.5	pm2.5	DET
ajst-8009	161	21	concentration	concentration	NOUN
ajst-8009	161	22	.	.	PUNCT
ajst-8009	162	1	the	the	DET
ajst-8009	162	2	pearson	pearson	PROPN
ajst-8009	162	3	correlation	correlation	NOUN
ajst-8009	162	4	coefficient	coefficient	NOUN
ajst-8009	162	5	is	be	AUX
ajst-8009	162	6	used	use	VERB
ajst-8009	162	7	to	to	PART
ajst-8009	162	8	describe	describe	VERB
ajst-8009	162	9	the	the	DET
ajst-8009	162	10	correlation	correlation	NOUN
ajst-8009	162	11	between	between	ADP
ajst-8009	162	12	two	two	NUM
ajst-8009	162	13	variables	variable	NOUN
ajst-8009	162	14	and	and	CCONJ
ajst-8009	162	15	is	be	AUX
ajst-8009	162	16	often	often	ADV
ajst-8009	162	17	expressed	express	VERB
ajst-8009	162	18	as	as	ADP
ajst-8009	162	19	an	an	DET
ajst-8009	162	20	r2	r2	NOUN
ajst-8009	162	21	score	score	NOUN
ajst-8009	162	22	.	.	PUNCT
ajst-8009	163	1	12	12	NUM
ajst-8009	163	2	(	(	PUNCT
ajst-8009	163	3	)	)	PUNCT
ajst-8009	163	4	(	(	PUNCT
ajst-8009	163	5	)	)	PUNCT
ajst-8009	163	6	1	1	NUM
ajst-8009	163	7	1	1	NUM
ajst-8009	163	8	i	i	PRON
ajst-8009	163	9	i	i	VERB
ajst-8009	163	10	x	x	VERB
ajst-8009	163	11	y	y	VERB
ajst-8009	163	12	n	n	ADV
ajst-8009	163	13	x	x	PUNCT
ajst-8009	163	14	x	x	SYM
ajst-8009	163	15	y	y	NOUN
ajst-8009	163	16	y	y	NOUN
ajst-8009	163	17	r	r	NOUN
ajst-8009	163	18	score	score	NOUN
ajst-8009	164	1	n	n	ADV
ajst-8009	164	2	i	i	PRON
ajst-8009	164	3			NUM
ajst-8009	164	4			NUM
ajst-8009	164	5			PROPN
ajst-8009	164	6			PROPN
ajst-8009	164	7			PRON
ajst-8009	164	8			X
ajst-8009	164	9			VERB
ajst-8009	164	10			PRON
ajst-8009	164	11	(	(	PUNCT
ajst-8009	164	12	17	17	NUM
ajst-8009	164	13	)	)	PUNCT
ajst-8009	165	1	where	where	SCONJ
ajst-8009	165	2	(	(	PUNCT
ajst-8009	165	3	)	)	PUNCT
ajst-8009	165	4	i	i	PRON
ajst-8009	165	5	x	x	NOUN
ajst-8009	165	6	x	x	PUNCT
ajst-8009	165	7	x	x	SYM
ajst-8009	165	8			NUM
ajst-8009	165	9			NOUN
ajst-8009	165	10	,	,	PUNCT
ajst-8009	165	11	x	x	X
ajst-8009	165	12	,	,	PUNCT
ajst-8009	165	13	and	and	CCONJ
ajst-8009	165	14	x	x	PROPN
ajst-8009	165	15	are	be	AUX
ajst-8009	165	16	respectively	respectively	ADV
ajst-8009	165	17	the	the	DET
ajst-8009	165	18	standardized	standardized	ADJ
ajst-8009	165	19	scores	score	NOUN
ajst-8009	165	20	,	,	PUNCT
ajst-8009	165	21	sample	sample	NOUN
ajst-8009	165	22	mean	mean	VERB
ajst-8009	165	23	,	,	PUNCT
ajst-8009	165	24	and	and	CCONJ
ajst-8009	165	25	sample	sample	NOUN
ajst-8009	165	26	standard	standard	ADJ
ajst-8009	165	27	deviation	deviation	NOUN
ajst-8009	165	28	of	of	ADP
ajst-8009	165	29	sample	sample	NOUN
ajst-8009	165	30	ix	ix	ADV
ajst-8009	165	31	.	.	PUNCT
ajst-8009	166	1	the	the	DET
ajst-8009	166	2	value	value	NOUN
ajst-8009	166	3	of	of	ADP
ajst-8009	166	4	r	r	NOUN
ajst-8009	166	5	ranges	range	VERB
ajst-8009	166	6	from	from	ADP
ajst-8009	166	7	[	[	X
ajst-8009	166	8	-1,1	-1,1	NOUN
ajst-8009	166	9	]	]	X
ajst-8009	166	10	,	,	PUNCT
ajst-8009	166	11	and	and	CCONJ
ajst-8009	166	12	the	the	PRON
ajst-8009	166	13	closer	close	ADJ
ajst-8009	166	14	its	its	PRON
ajst-8009	166	15	value	value	NOUN
ajst-8009	166	16	is	be	AUX
ajst-8009	166	17	to	to	ADP
ajst-8009	166	18	1	1	NUM
ajst-8009	166	19	or	or	CCONJ
ajst-8009	166	20	-1	-1	ADP
ajst-8009	166	21	,	,	PUNCT
ajst-8009	166	22	the	the	PRON
ajst-8009	166	23	stronger	strong	ADJ
ajst-8009	166	24	the	the	DET
ajst-8009	166	25	correlation	correlation	NOUN
ajst-8009	166	26	.	.	PUNCT
ajst-8009	167	1	the	the	PRON
ajst-8009	167	2	closer	close	ADV
ajst-8009	167	3	its	its	PRON
ajst-8009	167	4	value	value	NOUN
ajst-8009	167	5	is	be	AUX
ajst-8009	167	6	to	to	ADP
ajst-8009	167	7	0	0	NUM
ajst-8009	167	8	,	,	PUNCT
ajst-8009	167	9	the	the	PRON
ajst-8009	167	10	weaker	weak	ADJ
ajst-8009	167	11	the	the	DET
ajst-8009	167	12	correlation	correlation	NOUN
ajst-8009	167	13	.	.	PUNCT
ajst-8009	168	1	the	the	DET
ajst-8009	168	2	pm2.5	pm2.5	ADJ
ajst-8009	168	3	concentration	concentration	NOUN
ajst-8009	168	4	value	value	NOUN
ajst-8009	168	5	of	of	ADP
ajst-8009	168	6	a	a	DET
ajst-8009	168	7	region	region	NOUN
ajst-8009	168	8	can	can	AUX
ajst-8009	168	9	be	be	AUX
ajst-8009	168	10	analyzed	analyze	VERB
ajst-8009	168	11	from	from	ADP
ajst-8009	168	12	two	two	NUM
ajst-8009	168	13	aspects	aspect	NOUN
ajst-8009	168	14	:	:	PUNCT
ajst-8009	168	15	historical	historical	ADJ
ajst-8009	168	16	concentration	concentration	NOUN
ajst-8009	168	17	values	value	NOUN
ajst-8009	168	18	and	and	CCONJ
ajst-8009	168	19	current	current	ADJ
ajst-8009	168	20	meteorological	meteorological	ADJ
ajst-8009	168	21	conditions	condition	NOUN
ajst-8009	168	22	.	.	PUNCT
ajst-8009	169	1	historical	historical	ADJ
ajst-8009	169	2	concentration	concentration	NOUN
ajst-8009	169	3	values	value	NOUN
ajst-8009	169	4	will	will	AUX
ajst-8009	169	5	have	have	VERB
ajst-8009	169	6	a	a	DET
ajst-8009	169	7	certain	certain	ADJ
ajst-8009	169	8	impact	impact	NOUN
ajst-8009	169	9	on	on	ADP
ajst-8009	169	10	the	the	DET
ajst-8009	169	11	concentration	concentration	NOUN
ajst-8009	169	12	value	value	NOUN
ajst-8009	169	13	of	of	ADP
ajst-8009	169	14	the	the	DET
ajst-8009	169	15	next	next	ADJ
ajst-8009	169	16	moment	moment	NOUN
ajst-8009	169	17	,	,	PUNCT
ajst-8009	169	18	while	while	SCONJ
ajst-8009	169	19	other	other	ADJ
ajst-8009	169	20	pollutants	pollutant	NOUN
ajst-8009	169	21	and	and	CCONJ
ajst-8009	169	22	meteorological	meteorological	ADJ
ajst-8009	169	23	factors	factor	NOUN
ajst-8009	169	24	will	will	AUX
ajst-8009	169	25	have	have	VERB
ajst-8009	169	26	a	a	DET
ajst-8009	169	27	greater	great	ADJ
ajst-8009	169	28	impact	impact	NOUN
ajst-8009	169	29	on	on	ADP
ajst-8009	169	30	the	the	DET
ajst-8009	169	31	concentration	concentration	NOUN
ajst-8009	169	32	value	value	NOUN
ajst-8009	169	33	of	of	ADP
ajst-8009	169	34	the	the	DET
ajst-8009	169	35	next	next	ADJ
ajst-8009	169	36	moment	moment	NOUN
ajst-8009	169	37	.	.	PUNCT
ajst-8009	170	1	4.1.1	4.1.1	X
ajst-8009	170	2	.	.	PUNCT
ajst-8009	171	1	correlation	correlation	NOUN
ajst-8009	171	2	analysis	analysis	NOUN
ajst-8009	171	3	of	of	ADP
ajst-8009	171	4	pm2.5	pm2.5	DET
ajst-8009	171	5	concentrations	concentration	NOUN
ajst-8009	171	6	with	with	ADP
ajst-8009	171	7	other	other	ADJ
ajst-8009	171	8	pollutants	pollutant	NOUN
ajst-8009	171	9	figure	figure	VERB
ajst-8009	171	10	6	6	NUM
ajst-8009	171	11	analyzes	analyze	VERB
ajst-8009	171	12	the	the	DET
ajst-8009	171	13	correlation	correlation	NOUN
ajst-8009	171	14	between	between	ADP
ajst-8009	171	15	pm2.5	pm2.5	PROPN
ajst-8009	171	16	and	and	CCONJ
ajst-8009	171	17	other	other	ADJ
ajst-8009	171	18	pollutants	pollutant	NOUN
ajst-8009	171	19	.	.	PUNCT
ajst-8009	172	1	it	it	PRON
ajst-8009	172	2	can	can	AUX
ajst-8009	172	3	be	be	AUX
ajst-8009	172	4	seen	see	VERB
ajst-8009	172	5	that	that	SCONJ
ajst-8009	172	6	pm10	pm10	PROPN
ajst-8009	172	7	,	,	PUNCT
ajst-8009	172	8	no2	no2	PROPN
ajst-8009	172	9	,	,	PUNCT
ajst-8009	172	10	co	co	NOUN
ajst-8009	172	11	,	,	PUNCT
ajst-8009	172	12	and	and	CCONJ
ajst-8009	172	13	so2	so2	PROPN
ajst-8009	172	14	are	be	AUX
ajst-8009	172	15	positively	positively	ADV
ajst-8009	172	16	correlated	correlate	VERB
ajst-8009	172	17	with	with	ADP
ajst-8009	172	18	pm2.5	pm2.5	PROPN
ajst-8009	172	19	,	,	PUNCT
ajst-8009	172	20	while	while	SCONJ
ajst-8009	172	21	o3	o3	PROPN
ajst-8009	172	22	is	be	AUX
ajst-8009	172	23	negatively	negatively	ADV
ajst-8009	172	24	correlated	correlate	VERB
ajst-8009	172	25	with	with	ADP
ajst-8009	172	26	pm2.5	pm2.5	PROPN
ajst-8009	172	27	.	.	PUNCT
ajst-8009	173	1	this	this	PRON
ajst-8009	173	2	indicates	indicate	VERB
ajst-8009	173	3	that	that	SCONJ
ajst-8009	173	4	the	the	DET
ajst-8009	173	5	concentration	concentration	NOUN
ajst-8009	173	6	of	of	ADP
ajst-8009	173	7	pm2.5	pm2.5	DET
ajst-8009	173	8	increases	increase	NOUN
ajst-8009	173	9	with	with	ADP
ajst-8009	173	10	the	the	DET
ajst-8009	173	11	increase	increase	NOUN
ajst-8009	173	12	of	of	ADP
ajst-8009	173	13	pm10	pm10	PROPN
ajst-8009	173	14	,	,	PUNCT
ajst-8009	173	15	no2	no2	PROPN
ajst-8009	173	16	,	,	PUNCT
ajst-8009	173	17	co	co	NOUN
ajst-8009	173	18	,	,	PUNCT
ajst-8009	173	19	and	and	CCONJ
ajst-8009	173	20	so2	so2	NOUN
ajst-8009	173	21	concentrations	concentration	NOUN
ajst-8009	173	22	,	,	PUNCT
ajst-8009	173	23	and	and	CCONJ
ajst-8009	173	24	decreases	decrease	VERB
ajst-8009	173	25	with	with	ADP
ajst-8009	173	26	the	the	DET
ajst-8009	173	27	increase	increase	NOUN
ajst-8009	173	28	of	of	ADP
ajst-8009	173	29	o3	o3	PROPN
ajst-8009	173	30	concentration	concentration	NOUN
ajst-8009	173	31	.	.	PUNCT
ajst-8009	174	1	figure	figure	NOUN
ajst-8009	174	2	6	6	NUM
ajst-8009	174	3	.	.	PUNCT
ajst-8009	174	4	correlation	correlation	NOUN
ajst-8009	174	5	coefficient	coefficient	NOUN
ajst-8009	174	6	of	of	ADP
ajst-8009	174	7	pm2.5	pm2.5	PROPN
ajst-8009	174	8	with	with	ADP
ajst-8009	174	9	other	other	ADJ
ajst-8009	174	10	pollutants	pollutant	NOUN
ajst-8009	174	11	4.1.2	4.1.2	NUM
ajst-8009	174	12	.	.	PUNCT
ajst-8009	174	13	correlation	correlation	NOUN
ajst-8009	174	14	analysis	analysis	NOUN
ajst-8009	174	15	between	between	ADP
ajst-8009	174	16	pm2.5	pm2.5	ADJ
ajst-8009	174	17	concentration	concentration	NOUN
ajst-8009	174	18	and	and	CCONJ
ajst-8009	174	19	meteorological	meteorological	ADJ
ajst-8009	174	20	factors	factor	NOUN
ajst-8009	174	21	figure	figure	VERB
ajst-8009	174	22	7	7	NUM
ajst-8009	174	23	analyzes	analyze	VERB
ajst-8009	174	24	the	the	DET
ajst-8009	174	25	correlation	correlation	NOUN
ajst-8009	174	26	between	between	ADP
ajst-8009	174	27	pm2.5	pm2.5	ADJ
ajst-8009	174	28	and	and	CCONJ
ajst-8009	174	29	meteorological	meteorological	ADJ
ajst-8009	174	30	factors	factor	NOUN
ajst-8009	174	31	.	.	PUNCT
ajst-8009	175	1	it	it	PRON
ajst-8009	175	2	can	can	AUX
ajst-8009	175	3	be	be	AUX
ajst-8009	175	4	seen	see	VERB
ajst-8009	175	5	from	from	ADP
ajst-8009	175	6	the	the	DET
ajst-8009	175	7	graph	graph	NOUN
ajst-8009	175	8	that	that	SCONJ
ajst-8009	175	9	there	there	PRON
ajst-8009	175	10	is	be	VERB
ajst-8009	175	11	only	only	ADV
ajst-8009	175	12	a	a	DET
ajst-8009	175	13	certain	certain	ADJ
ajst-8009	175	14	correlation	correlation	NOUN
ajst-8009	175	15	between	between	ADP
ajst-8009	175	16	pm2.5	pm2.5	ADJ
ajst-8009	175	17	and	and	CCONJ
ajst-8009	175	18	meteorological	meteorological	ADJ
ajst-8009	175	19	factors	factor	NOUN
ajst-8009	175	20	.	.	PUNCT
ajst-8009	176	1	however	however	ADV
ajst-8009	176	2	,	,	PUNCT
ajst-8009	176	3	the	the	DET
ajst-8009	176	4	correlation	correlation	NOUN
ajst-8009	176	5	between	between	ADP
ajst-8009	176	6	weather	weather	NOUN
ajst-8009	176	7	,	,	PUNCT
ajst-8009	176	8	humidity	humidity	NOUN
ajst-8009	176	9	,	,	PUNCT
ajst-8009	176	10	wind	wind	NOUN
ajst-8009	176	11	speed	speed	NOUN
ajst-8009	176	12	,	,	PUNCT
ajst-8009	176	13	and	and	CCONJ
ajst-8009	176	14	pm2.5	pm2.5	PROPN
ajst-8009	176	15	is	be	AUX
ajst-8009	176	16	relatively	relatively	ADV
ajst-8009	176	17	strong	strong	ADJ
ajst-8009	176	18	,	,	PUNCT
ajst-8009	176	19	while	while	SCONJ
ajst-8009	176	20	the	the	DET
ajst-8009	176	21	correlation	correlation	NOUN
ajst-8009	176	22	between	between	ADP
ajst-8009	176	23	other	other	ADJ
ajst-8009	176	24	meteorological	meteorological	ADJ
ajst-8009	176	25	factors	factor	NOUN
ajst-8009	176	26	and	and	CCONJ
ajst-8009	176	27	pm2.5	pm2.5	PROPN
ajst-8009	176	28	is	be	AUX
ajst-8009	176	29	weak	weak	ADJ
ajst-8009	176	30	.	.	PUNCT
ajst-8009	177	1	among	among	ADP
ajst-8009	177	2	them	they	PRON
ajst-8009	177	3	,	,	PUNCT
ajst-8009	177	4	weather	weather	NOUN
ajst-8009	177	5	,	,	PUNCT
ajst-8009	177	6	humidity	humidity	NOUN
ajst-8009	177	7	,	,	PUNCT
ajst-8009	177	8	and	and	CCONJ
ajst-8009	177	9	pm2.5	pm2.5	PROPN
ajst-8009	177	10	are	be	AUX
ajst-8009	177	11	positively	positively	ADV
ajst-8009	177	12	correlated	correlate	VERB
ajst-8009	177	13	,	,	PUNCT
ajst-8009	177	14	while	while	SCONJ
ajst-8009	177	15	wind	wind	NOUN
ajst-8009	177	16	speed	speed	NOUN
ajst-8009	177	17	and	and	CCONJ
ajst-8009	177	18	pm2.5	pm2.5	PROPN
ajst-8009	177	19	are	be	AUX
ajst-8009	177	20	negatively	negatively	ADV
ajst-8009	177	21	correlated	correlate	VERB
ajst-8009	177	22	.	.	PUNCT
ajst-8009	178	1	although	although	SCONJ
ajst-8009	178	2	the	the	DET
ajst-8009	178	3	correlation	correlation	NOUN
ajst-8009	178	4	between	between	ADP
ajst-8009	178	5	other	other	ADJ
ajst-8009	178	6	factors	factor	NOUN
ajst-8009	178	7	and	and	CCONJ
ajst-8009	178	8	pm2.5	pm2.5	PROPN
ajst-8009	178	9	is	be	AUX
ajst-8009	178	10	small	small	ADJ
ajst-8009	178	11	,	,	PUNCT
ajst-8009	178	12	there	there	PRON
ajst-8009	178	13	is	be	VERB
ajst-8009	178	14	still	still	ADV
ajst-8009	178	15	a	a	DET
ajst-8009	178	16	certain	certain	ADJ
ajst-8009	178	17	correlation	correlation	NOUN
ajst-8009	178	18	.	.	PUNCT
ajst-8009	179	1	therefore	therefore	ADV
ajst-8009	179	2	,	,	PUNCT
ajst-8009	179	3	this	this	DET
ajst-8009	179	4	paper	paper	NOUN
ajst-8009	179	5	will	will	AUX
ajst-8009	179	6	select	select	VERB
ajst-8009	179	7	these	these	DET
ajst-8009	179	8	data	datum	NOUN
ajst-8009	179	9	for	for	ADP
ajst-8009	179	10	training	training	NOUN
ajst-8009	179	11	.	.	PUNCT
ajst-8009	180	1	figure	figure	NOUN
ajst-8009	180	2	7	7	NUM
ajst-8009	180	3	.	.	PUNCT
ajst-8009	180	4	correlation	correlation	NOUN
ajst-8009	180	5	coefficient	coefficient	NOUN
ajst-8009	180	6	of	of	ADP
ajst-8009	180	7	pm2.5	pm2.5	PROPN
ajst-8009	180	8	with	with	ADP
ajst-8009	180	9	other	other	ADJ
ajst-8009	180	10	pollutants	pollutant	NOUN
ajst-8009	180	11	178	178	NUM
ajst-8009	180	12	4.2	4.2	NUM
ajst-8009	180	13	.	.	PUNCT
ajst-8009	181	1	model	model	NOUN
ajst-8009	181	2	comparison	comparison	NOUN
ajst-8009	181	3	experiment	experiment	NOUN
ajst-8009	181	4	results	result	NOUN
ajst-8009	181	5	to	to	PART
ajst-8009	181	6	validate	validate	VERB
ajst-8009	181	7	the	the	DET
ajst-8009	181	8	stronger	strong	ADJ
ajst-8009	181	9	learning	learning	NOUN
ajst-8009	181	10	capability	capability	NOUN
ajst-8009	181	11	and	and	CCONJ
ajst-8009	181	12	more	more	ADV
ajst-8009	181	13	accurate	accurate	ADJ
ajst-8009	181	14	prediction	prediction	NOUN
ajst-8009	181	15	results	result	NOUN
ajst-8009	181	16	of	of	ADP
ajst-8009	181	17	the	the	DET
ajst-8009	181	18	tpa	tpa	PROPN
ajst-8009	181	19	-	-	PUNCT
ajst-8009	181	20	cnn	cnn	PROPN
ajst-8009	181	21	-	-	PUNCT
ajst-8009	181	22	lstm	lstm	PROPN
ajst-8009	181	23	model	model	NOUN
ajst-8009	181	24	proposed	propose	VERB
ajst-8009	181	25	in	in	ADP
ajst-8009	181	26	this	this	DET
ajst-8009	181	27	paper	paper	NOUN
ajst-8009	181	28	,	,	PUNCT
ajst-8009	181	29	the	the	DET
ajst-8009	181	30	lstm	lstm	PROPN
ajst-8009	181	31	model	model	NOUN
ajst-8009	181	32	and	and	CCONJ
ajst-8009	181	33	cnn	cnn	PROPN
ajst-8009	181	34	-	-	PUNCT
ajst-8009	181	35	lstm	lstm	PROPN
ajst-8009	181	36	model	model	NOUN
ajst-8009	181	37	were	be	AUX
ajst-8009	181	38	used	use	VERB
ajst-8009	181	39	to	to	PART
ajst-8009	181	40	predict	predict	VERB
ajst-8009	181	41	pm2.5	pm2.5	DET
ajst-8009	181	42	concentration	concentration	NOUN
ajst-8009	181	43	on	on	ADP
ajst-8009	181	44	the	the	DET
ajst-8009	181	45	same	same	ADJ
ajst-8009	181	46	dataset	dataset	NOUN
ajst-8009	181	47	,	,	PUNCT
ajst-8009	181	48	and	and	CCONJ
ajst-8009	181	49	the	the	DET
ajst-8009	181	50	results	result	NOUN
ajst-8009	181	51	of	of	ADP
ajst-8009	181	52	the	the	DET
ajst-8009	181	53	three	three	NUM
ajst-8009	181	54	predictions	prediction	NOUN
ajst-8009	181	55	were	be	AUX
ajst-8009	181	56	compared	compare	VERB
ajst-8009	181	57	and	and	CCONJ
ajst-8009	181	58	analyzed	analyze	VERB
ajst-8009	181	59	,	,	PUNCT
ajst-8009	181	60	as	as	SCONJ
ajst-8009	181	61	shown	show	VERB
ajst-8009	181	62	in	in	ADP
ajst-8009	181	63	figure	figure	NOUN
ajst-8009	181	64	8	8	NUM
ajst-8009	181	65	.	.	NOUN
ajst-8009	181	66	0	0	NUM
ajst-8009	182	1	100	100	NUM
ajst-8009	182	2	200	200	NUM
ajst-8009	182	3	300	300	NUM
ajst-8009	182	4	400	400	NUM
ajst-8009	182	5	500	500	NUM
ajst-8009	182	6	0	0	NUM
ajst-8009	182	7	200	200	NUM
ajst-8009	182	8	p	p	NOUN
ajst-8009	182	9	m	m	NOUN
ajst-8009	182	10	2	2	NUM
ajst-8009	182	11	.	.	PUNCT
ajst-8009	182	12	5	5	NUM
ajst-8009	182	13	(	(	PUNCT
ajst-8009	182	14	μ	μ	NOUN
ajst-8009	182	15	g	g	PROPN
ajst-8009	182	16	/	/	SYM
ajst-8009	182	17	m	m	PROPN
ajst-8009	182	18	3	3	NUM
ajst-8009	182	19	)	)	PUNCT
ajst-8009	182	20	time	time	NOUN
ajst-8009	182	21	/	/	SYM
ajst-8009	182	22	h	h	NOUN
ajst-8009	182	23	true	true	ADJ
ajst-8009	182	24	lstm	lstm	PROPN
ajst-8009	182	25	cnn	cnn	PROPN
ajst-8009	182	26	-	-	PUNCT
ajst-8009	182	27	lstm	lstm	PROPN
ajst-8009	182	28	tpa	tpa	PROPN
ajst-8009	182	29	-	-	PUNCT
ajst-8009	182	30	cnn	cnn	PROPN
ajst-8009	182	31	-	-	PUNCT
ajst-8009	182	32	lstm	lstm	ADJ
ajst-8009	182	33	figure	figure	NOUN
ajst-8009	182	34	8	8	NUM
ajst-8009	182	35	.	.	PUNCT
ajst-8009	183	1	overall	overall	ADJ
ajst-8009	183	2	comparison	comparison	NOUN
ajst-8009	183	3	of	of	ADP
ajst-8009	183	4	results	result	NOUN
ajst-8009	183	5	of	of	ADP
ajst-8009	183	6	different	different	ADJ
ajst-8009	183	7	prediction	prediction	NOUN
ajst-8009	183	8	models	model	NOUN
ajst-8009	183	9	according	accord	VERB
ajst-8009	183	10	to	to	PART
ajst-8009	183	11	figure	figure	NOUN
ajst-8009	183	12	8	8	NUM
ajst-8009	183	13	,	,	PUNCT
ajst-8009	183	14	the	the	DET
ajst-8009	183	15	three	three	NUM
ajst-8009	183	16	prediction	prediction	NOUN
ajst-8009	183	17	models	model	NOUN
ajst-8009	183	18	have	have	VERB
ajst-8009	183	19	consistent	consistent	ADJ
ajst-8009	183	20	trends	trend	NOUN
ajst-8009	183	21	with	with	ADP
ajst-8009	183	22	the	the	DET
ajst-8009	183	23	actual	actual	ADJ
ajst-8009	183	24	values	value	NOUN
ajst-8009	183	25	,	,	PUNCT
ajst-8009	183	26	with	with	ADP
ajst-8009	183	27	the	the	DET
ajst-8009	183	28	tpa	tpa	NOUN
ajst-8009	183	29	-	-	PUNCT
ajst-8009	183	30	cnnlstm	cnnlstm	NOUN
ajst-8009	183	31	model	model	NOUN
ajst-8009	183	32	having	having	AUX
ajst-8009	183	33	predicted	predict	VERB
ajst-8009	183	34	values	value	NOUN
ajst-8009	183	35	that	that	PRON
ajst-8009	183	36	are	be	AUX
ajst-8009	183	37	closer	close	ADJ
ajst-8009	183	38	to	to	ADP
ajst-8009	183	39	the	the	DET
ajst-8009	183	40	real	real	ADJ
ajst-8009	183	41	values	value	NOUN
ajst-8009	183	42	,	,	PUNCT
ajst-8009	183	43	while	while	SCONJ
ajst-8009	183	44	the	the	DET
ajst-8009	183	45	lstm	lstm	NOUN
ajst-8009	183	46	model	model	NOUN
ajst-8009	183	47	has	have	VERB
ajst-8009	183	48	a	a	DET
ajst-8009	183	49	larger	large	ADJ
ajst-8009	183	50	deviation	deviation	NOUN
ajst-8009	183	51	in	in	ADP
ajst-8009	183	52	its	its	PRON
ajst-8009	183	53	predicted	predict	VERB
ajst-8009	183	54	values	value	NOUN
ajst-8009	183	55	.	.	PUNCT
ajst-8009	184	1	cnn	cnn	PROPN
ajst-8009	184	2	can	can	AUX
ajst-8009	184	3	perform	perform	VERB
ajst-8009	184	4	network	network	NOUN
ajst-8009	184	5	learning	learning	NOUN
ajst-8009	184	6	from	from	ADP
ajst-8009	184	7	the	the	DET
ajst-8009	184	8	original	original	ADJ
ajst-8009	184	9	input	input	NOUN
ajst-8009	184	10	sequence	sequence	NOUN
ajst-8009	184	11	,	,	PUNCT
ajst-8009	184	12	avoiding	avoid	VERB
ajst-8009	184	13	the	the	DET
ajst-8009	184	14	accumulation	accumulation	NOUN
ajst-8009	184	15	of	of	ADP
ajst-8009	184	16	errors	error	NOUN
ajst-8009	184	17	caused	cause	VERB
ajst-8009	184	18	by	by	ADP
ajst-8009	184	19	manually	manually	ADV
ajst-8009	184	20	extracting	extract	VERB
ajst-8009	184	21	features	feature	NOUN
ajst-8009	184	22	.	.	PUNCT
ajst-8009	185	1	by	by	ADP
ajst-8009	185	2	adding	add	VERB
ajst-8009	185	3	the	the	DET
ajst-8009	185	4	tpa	tpa	PROPN
ajst-8009	185	5	network	network	NOUN
ajst-8009	185	6	,	,	PUNCT
ajst-8009	185	7	important	important	ADJ
ajst-8009	185	8	information	information	NOUN
ajst-8009	185	9	can	can	AUX
ajst-8009	185	10	be	be	AUX
ajst-8009	185	11	prioritized	prioritize	VERB
ajst-8009	185	12	and	and	CCONJ
ajst-8009	185	13	the	the	DET
ajst-8009	185	14	input	input	NOUN
ajst-8009	185	15	data	datum	NOUN
ajst-8009	185	16	of	of	ADP
ajst-8009	185	17	the	the	DET
ajst-8009	185	18	lstm	lstm	PROPN
ajst-8009	185	19	network	network	NOUN
ajst-8009	185	20	can	can	AUX
ajst-8009	185	21	be	be	AUX
ajst-8009	185	22	optimized	optimize	VERB
ajst-8009	185	23	.	.	PUNCT
ajst-8009	186	1	therefore	therefore	ADV
ajst-8009	186	2	,	,	PUNCT
ajst-8009	186	3	the	the	DET
ajst-8009	186	4	tpa	tpa	PROPN
ajst-8009	186	5	-	-	PUNCT
ajst-8009	186	6	cnn	cnn	PROPN
ajst-8009	186	7	-	-	PUNCT
ajst-8009	186	8	lstm	lstm	ADJ
ajst-8009	186	9	network	network	NOUN
ajst-8009	186	10	can	can	AUX
ajst-8009	186	11	more	more	ADV
ajst-8009	186	12	effectively	effectively	ADV
ajst-8009	186	13	extract	extract	VERB
ajst-8009	186	14	features	feature	NOUN
ajst-8009	186	15	of	of	ADP
ajst-8009	186	16	the	the	DET
ajst-8009	186	17	time	time	NOUN
ajst-8009	186	18	series	series	NOUN
ajst-8009	186	19	of	of	ADP
ajst-8009	186	20	pm2.5	pm2.5	DET
ajst-8009	186	21	concentration	concentration	NOUN
ajst-8009	186	22	and	and	CCONJ
ajst-8009	186	23	related	related	ADJ
ajst-8009	186	24	influencing	influence	VERB
ajst-8009	186	25	factors	factor	NOUN
ajst-8009	186	26	and	and	CCONJ
ajst-8009	186	27	improve	improve	VERB
ajst-8009	186	28	the	the	DET
ajst-8009	186	29	prediction	prediction	NOUN
ajst-8009	186	30	accuracy	accuracy	NOUN
ajst-8009	186	31	.	.	PUNCT
ajst-8009	187	1	table	table	NOUN
ajst-8009	187	2	2	2	NUM
ajst-8009	187	3	and	and	CCONJ
ajst-8009	187	4	table	table	NOUN
ajst-8009	187	5	3	3	NUM
ajst-8009	187	6	summarize	summarize	VERB
ajst-8009	187	7	the	the	DET
ajst-8009	187	8	evaluation	evaluation	NOUN
ajst-8009	187	9	indicators	indicator	NOUN
ajst-8009	187	10	for	for	ADP
ajst-8009	187	11	the	the	DET
ajst-8009	187	12	three	three	NUM
ajst-8009	187	13	prediction	prediction	NOUN
ajst-8009	187	14	models	model	NOUN
ajst-8009	187	15	.	.	PUNCT
ajst-8009	188	1	although	although	SCONJ
ajst-8009	188	2	the	the	DET
ajst-8009	188	3	prediction	prediction	NOUN
ajst-8009	188	4	accuracy	accuracy	NOUN
ajst-8009	188	5	of	of	ADP
ajst-8009	188	6	these	these	DET
ajst-8009	188	7	three	three	NUM
ajst-8009	188	8	models	model	NOUN
ajst-8009	188	9	decreases	decrease	VERB
ajst-8009	188	10	over	over	ADP
ajst-8009	188	11	time	time	NOUN
ajst-8009	188	12	,	,	PUNCT
ajst-8009	188	13	it	it	PRON
ajst-8009	188	14	is	be	AUX
ajst-8009	188	15	noteworthy	noteworthy	ADJ
ajst-8009	188	16	that	that	SCONJ
ajst-8009	188	17	the	the	DET
ajst-8009	188	18	tpa	tpa	PROPN
ajst-8009	188	19	-	-	PUNCT
ajst-8009	188	20	cnn	cnn	PROPN
ajst-8009	188	21	-	-	PUNCT
ajst-8009	188	22	lstm	lstm	ADJ
ajst-8009	188	23	model	model	NOUN
ajst-8009	188	24	performs	perform	VERB
ajst-8009	188	25	better	well	ADV
ajst-8009	188	26	in	in	ADP
ajst-8009	188	27	the	the	DET
ajst-8009	188	28	prediction	prediction	NOUN
ajst-8009	188	29	results	result	NOUN
ajst-8009	188	30	for	for	ADP
ajst-8009	188	31	each	each	DET
ajst-8009	188	32	hour	hour	NOUN
ajst-8009	188	33	,	,	PUNCT
ajst-8009	188	34	and	and	CCONJ
ajst-8009	188	35	its	its	PRON
ajst-8009	188	36	prediction	prediction	NOUN
ajst-8009	188	37	accuracy	accuracy	NOUN
ajst-8009	188	38	is	be	AUX
ajst-8009	188	39	higher	high	ADJ
ajst-8009	188	40	than	than	ADP
ajst-8009	188	41	that	that	PRON
ajst-8009	188	42	of	of	ADP
ajst-8009	188	43	the	the	DET
ajst-8009	188	44	lstm	lstm	PROPN
ajst-8009	188	45	and	and	CCONJ
ajst-8009	188	46	cnn	cnn	PROPN
ajst-8009	188	47	-	-	PUNCT
ajst-8009	188	48	lstm	lstm	ADJ
ajst-8009	188	49	models	model	NOUN
ajst-8009	188	50	.	.	PUNCT
ajst-8009	189	1	this	this	DET
ajst-8009	189	2	result	result	NOUN
ajst-8009	189	3	is	be	AUX
ajst-8009	189	4	also	also	ADV
ajst-8009	189	5	confirmed	confirm	VERB
ajst-8009	189	6	at	at	ADP
ajst-8009	189	7	the	the	DET
ajst-8009	189	8	average	average	ADJ
ajst-8009	189	9	prediction	prediction	NOUN
ajst-8009	189	10	level	level	NOUN
ajst-8009	189	11	of	of	ADP
ajst-8009	189	12	6	6	NUM
ajst-8009	189	13	hours	hour	NOUN
ajst-8009	189	14	.	.	PUNCT
ajst-8009	190	1	table	table	NOUN
ajst-8009	190	2	2	2	NUM
ajst-8009	190	3	.	.	PUNCT
ajst-8009	191	1	the	the	DET
ajst-8009	191	2	performances	performance	NOUN
ajst-8009	191	3	of	of	ADP
ajst-8009	191	4	the	the	DET
ajst-8009	191	5	different	different	ADJ
ajst-8009	191	6	models	model	NOUN
ajst-8009	191	7	in	in	ADP
ajst-8009	191	8	terms	term	NOUN
ajst-8009	191	9	of	of	ADP
ajst-8009	191	10	root	root	NOUN
ajst-8009	191	11	-	-	PUNCT
ajst-8009	191	12	mean	mean	ADJ
ajst-8009	191	13	-	-	PUNCT
ajst-8009	191	14	square	square	NOUN
ajst-8009	191	15	error	error	NOUN
ajst-8009	191	16	(	(	PUNCT
ajst-8009	191	17	rmse	rmse	ADJ
ajst-8009	191	18	)	)	PUNCT
ajst-8009	191	19	time	time	NOUN
ajst-8009	191	20	/	/	SYM
ajst-8009	191	21	h	h	NOUN
ajst-8009	191	22	rmse/(μg	rmse/(μg	PROPN
ajst-8009	191	23	/	/	SYM
ajst-8009	191	24	m3	m3	PROPN
ajst-8009	191	25	)	)	PUNCT
ajst-8009	191	26	lstm	lstm	PROPN
ajst-8009	191	27	cnn	cnn	PROPN
ajst-8009	191	28	-	-	PUNCT
ajst-8009	191	29	lstm	lstm	PROPN
ajst-8009	191	30	tpa	tpa	PROPN
ajst-8009	191	31	-	-	PUNCT
ajst-8009	191	32	cnn	cnn	NOUN
ajst-8009	191	33	-	-	PUNCT
ajst-8009	191	34	lstm	lstm	NOUN
ajst-8009	191	35	t+1	t+1	X
ajst-8009	191	36	23.83	23.83	NUM
ajst-8009	191	37	21.77	21.77	NUM
ajst-8009	191	38	20.65	20.65	NUM
ajst-8009	191	39	t+2	t+2	NUM
ajst-8009	191	40	28.34	28.34	NUM
ajst-8009	191	41	27.40	27.40	NUM
ajst-8009	191	42	23.51	23.51	NUM
ajst-8009	191	43	t+3	t+3	VERB
ajst-8009	191	44	36.51	36.51	NUM
ajst-8009	191	45	32.26	32.26	NUM
ajst-8009	191	46	27.34	27.34	NUM
ajst-8009	191	47	t+4	t+4	NUM
ajst-8009	191	48	38.82	38.82	NUM
ajst-8009	191	49	36.18	36.18	NUM
ajst-8009	191	50	30.97	30.97	NUM
ajst-8009	191	51	t+5	t+5	NUM
ajst-8009	191	52	41.62	41.62	NUM
ajst-8009	191	53	38.53	38.53	NUM
ajst-8009	191	54	33.62	33.62	NUM
ajst-8009	191	55	t+6	t+6	PROPN
ajst-8009	191	56	44.18	44.18	NUM
ajst-8009	191	57	40.72	40.72	NUM
ajst-8009	191	58	37.72	37.72	NUM
ajst-8009	191	59	average	average	NOUN
ajst-8009	191	60	35.55	35.55	NUM
ajst-8009	191	61	32.81	32.81	NUM
ajst-8009	191	62	28.97	28.97	NUM
ajst-8009	191	63	table	table	NOUN
ajst-8009	191	64	6	6	NUM
ajst-8009	191	65	.	.	PUNCT
ajst-8009	192	1	the	the	DET
ajst-8009	192	2	performances	performance	NOUN
ajst-8009	192	3	of	of	ADP
ajst-8009	192	4	the	the	DET
ajst-8009	192	5	different	different	ADJ
ajst-8009	192	6	models	model	NOUN
ajst-8009	192	7	in	in	ADP
ajst-8009	192	8	terms	term	NOUN
ajst-8009	192	9	of	of	ADP
ajst-8009	192	10	mean	mean	ADJ
ajst-8009	192	11	absolute	absolute	ADJ
ajst-8009	192	12	error(mae	error(mae	NOUN
ajst-8009	192	13	)	)	PUNCT
ajst-8009	192	14	time	time	NOUN
ajst-8009	192	15	/	/	SYM
ajst-8009	192	16	h	h	NOUN
ajst-8009	192	17	mae/(μg	mae/(μg	PROPN
ajst-8009	192	18	/	/	SYM
ajst-8009	192	19	m3	m3	PROPN
ajst-8009	192	20	)	)	PUNCT
ajst-8009	192	21	lstm	lstm	PROPN
ajst-8009	192	22	cnn	cnn	PROPN
ajst-8009	192	23	-	-	PUNCT
ajst-8009	192	24	lstm	lstm	PROPN
ajst-8009	192	25	tpa	tpa	PROPN
ajst-8009	192	26	-	-	PUNCT
ajst-8009	192	27	cnn	cnn	NOUN
ajst-8009	192	28	-	-	PUNCT
ajst-8009	192	29	lstm	lstm	NOUN
ajst-8009	192	30	t+1	t+1	X
ajst-8009	192	31	14.07	14.07	NUM
ajst-8009	192	32	12.78	12.78	NUM
ajst-8009	192	33	11.09	11.09	NUM
ajst-8009	192	34	t+2	t+2	NUM
ajst-8009	192	35	18.65	18.65	NUM
ajst-8009	192	36	16.59	16.59	NUM
ajst-8009	192	37	14.87	14.87	NUM
ajst-8009	192	38	t+3	t+3	PROPN
ajst-8009	192	39	21.32	21.32	NUM
ajst-8009	192	40	19.42	19.42	NUM
ajst-8009	192	41	18.54	18.54	NUM
ajst-8009	192	42	t+4	t+4	NUM
ajst-8009	192	43	24.18	24.18	NUM
ajst-8009	192	44	22.42	22.42	NUM
ajst-8009	192	45	20.94	20.94	NUM
ajst-8009	192	46	t+5	t+5	NUM
ajst-8009	192	47	27.52	27.52	NUM
ajst-8009	192	48	24.58	24.58	NUM
ajst-8009	192	49	22.43	22.43	NUM
ajst-8009	192	50	t+6	t+6	PROPN
ajst-8009	192	51	29.84	29.84	NUM
ajst-8009	192	52	26.41	26.41	NUM
ajst-8009	192	53	24.67	24.67	NUM
ajst-8009	192	54	average	average	NOUN
ajst-8009	192	55	22.60	22.60	NUM
ajst-8009	192	56	20.37	20.37	NUM
ajst-8009	192	57	18.76	18.76	NUM
ajst-8009	192	58	179	179	NUM
ajst-8009	192	59	from	from	ADP
ajst-8009	192	60	the	the	DET
ajst-8009	192	61	table	table	NOUN
ajst-8009	192	62	,	,	PUNCT
ajst-8009	192	63	it	it	PRON
ajst-8009	192	64	can	can	AUX
ajst-8009	192	65	be	be	AUX
ajst-8009	192	66	seen	see	VERB
ajst-8009	192	67	that	that	SCONJ
ajst-8009	192	68	in	in	ADP
ajst-8009	192	69	the	the	DET
ajst-8009	192	70	multi	multi	ADJ
ajst-8009	192	71	-	-	ADJ
ajst-8009	192	72	hour	hour	NOUN
ajst-8009	192	73	prediction	prediction	NOUN
ajst-8009	192	74	task	task	NOUN
ajst-8009	192	75	,	,	PUNCT
ajst-8009	192	76	the	the	DET
ajst-8009	192	77	tpa	tpa	PROPN
ajst-8009	192	78	-	-	PUNCT
ajst-8009	192	79	cnn	cnn	PROPN
ajst-8009	192	80	-	-	PUNCT
ajst-8009	192	81	lstm	lstm	ADJ
ajst-8009	192	82	model	model	NOUN
ajst-8009	192	83	has	have	VERB
ajst-8009	192	84	the	the	DET
ajst-8009	192	85	lowest	low	ADJ
ajst-8009	192	86	mae	mae	PROPN
ajst-8009	192	87	and	and	CCONJ
ajst-8009	192	88	rmse	rmse	NOUN
ajst-8009	192	89	.	.	PUNCT
ajst-8009	193	1	the	the	DET
ajst-8009	193	2	predicted	predict	VERB
ajst-8009	193	3	values	value	NOUN
ajst-8009	193	4	of	of	ADP
ajst-8009	193	5	the	the	DET
ajst-8009	193	6	tpa	tpa	NOUN
ajst-8009	193	7	-	-	PUNCT
ajst-8009	193	8	cnnlstm	cnnlstm	NOUN
ajst-8009	193	9	model	model	NOUN
ajst-8009	193	10	in	in	ADP
ajst-8009	193	11	the	the	DET
ajst-8009	193	12	multi	multi	ADJ
ajst-8009	193	13	-	-	ADJ
ajst-8009	193	14	scale	scale	ADJ
ajst-8009	193	15	prediction	prediction	NOUN
ajst-8009	193	16	task	task	NOUN
ajst-8009	193	17	are	be	AUX
ajst-8009	193	18	closer	close	ADJ
ajst-8009	193	19	to	to	ADP
ajst-8009	193	20	the	the	DET
ajst-8009	193	21	true	true	ADJ
ajst-8009	193	22	values	value	NOUN
ajst-8009	193	23	.	.	PUNCT
ajst-8009	194	1	in	in	ADP
ajst-8009	194	2	addition	addition	NOUN
ajst-8009	194	3	,	,	PUNCT
ajst-8009	194	4	in	in	ADP
ajst-8009	194	5	the	the	DET
ajst-8009	194	6	one	one	NUM
ajst-8009	194	7	-	-	PUNCT
ajst-8009	194	8	hour	hour	NOUN
ajst-8009	194	9	pm2.5	pm2.5	PROPN
ajst-8009	194	10	prediction	prediction	NOUN
ajst-8009	194	11	task	task	NOUN
ajst-8009	194	12	in	in	ADP
ajst-8009	194	13	figure	figure	NOUN
ajst-8009	194	14	8	8	NUM
ajst-8009	194	15	,	,	PUNCT
ajst-8009	194	16	the	the	DET
ajst-8009	194	17	r2	r2	NOUN
ajst-8009	194	18	of	of	ADP
ajst-8009	194	19	the	the	DET
ajst-8009	194	20	tpa	tpa	NOUN
ajst-8009	194	21	-	-	PUNCT
ajst-8009	194	22	tcn	tcn	NOUN
ajst-8009	194	23	-	-	PUNCT
ajst-8009	194	24	lstm	lstm	ADJ
ajst-8009	194	25	model	model	NOUN
ajst-8009	194	26	is	be	AUX
ajst-8009	194	27	the	the	DET
ajst-8009	194	28	highest	high	ADJ
ajst-8009	194	29	.	.	PUNCT
ajst-8009	195	1	after	after	ADP
ajst-8009	195	2	adding	add	VERB
ajst-8009	195	3	the	the	DET
ajst-8009	195	4	time	time	NOUN
ajst-8009	195	5	pattern	pattern	NOUN
ajst-8009	195	6	attention	attention	NOUN
ajst-8009	195	7	mechanism	mechanism	NOUN
ajst-8009	195	8	,	,	PUNCT
ajst-8009	195	9	the	the	DET
ajst-8009	195	10	performance	performance	NOUN
ajst-8009	195	11	of	of	ADP
ajst-8009	195	12	tpa	tpa	PROPN
ajst-8009	195	13	-	-	PUNCT
ajst-8009	195	14	cnn	cnn	NOUN
ajst-8009	195	15	-	-	PUNCT
ajst-8009	195	16	lstm	lstm	NOUN
ajst-8009	195	17	in	in	ADP
ajst-8009	195	18	the	the	DET
ajst-8009	195	19	multi	multi	ADJ
ajst-8009	195	20	-	-	ADJ
ajst-8009	195	21	scale	scale	ADJ
ajst-8009	195	22	prediction	prediction	NOUN
ajst-8009	195	23	task	task	NOUN
ajst-8009	195	24	is	be	AUX
ajst-8009	195	25	better	well	ADJ
ajst-8009	195	26	than	than	ADP
ajst-8009	195	27	that	that	PRON
ajst-8009	195	28	of	of	ADP
ajst-8009	195	29	lstm	lstm	PROPN
ajst-8009	195	30	and	and	CCONJ
ajst-8009	195	31	cnn	cnn	PROPN
ajst-8009	195	32	-	-	PUNCT
ajst-8009	195	33	lstm	lstm	PROPN
ajst-8009	195	34	.	.	PUNCT
ajst-8009	196	1	the	the	DET
ajst-8009	196	2	results	result	NOUN
ajst-8009	196	3	indicate	indicate	VERB
ajst-8009	196	4	that	that	SCONJ
ajst-8009	196	5	the	the	DET
ajst-8009	196	6	proposed	propose	VERB
ajst-8009	196	7	tpa	tpa	PROPN
ajst-8009	196	8	-	-	PUNCT
ajst-8009	196	9	cnn	cnn	PROPN
ajst-8009	196	10	-	-	PUNCT
ajst-8009	196	11	lstm	lstm	ADJ
ajst-8009	196	12	model	model	NOUN
ajst-8009	196	13	can	can	AUX
ajst-8009	196	14	effectively	effectively	ADV
ajst-8009	196	15	learn	learn	VERB
ajst-8009	196	16	the	the	DET
ajst-8009	196	17	spatiotemporal	spatiotemporal	ADJ
ajst-8009	196	18	correlations	correlation	NOUN
ajst-8009	196	19	of	of	ADP
ajst-8009	196	20	air	air	NOUN
ajst-8009	196	21	pollutants	pollutant	NOUN
ajst-8009	196	22	and	and	CCONJ
ajst-8009	196	23	is	be	AUX
ajst-8009	196	24	suitable	suitable	ADJ
ajst-8009	196	25	for	for	ADP
ajst-8009	196	26	related	related	ADJ
ajst-8009	196	27	tasks	task	NOUN
ajst-8009	196	28	of	of	ADP
ajst-8009	196	29	predicting	predict	VERB
ajst-8009	196	30	pm2.5	pm2.5	DET
ajst-8009	196	31	concentration	concentration	NOUN
ajst-8009	196	32	.	.	PUNCT
ajst-8009	197	1	5	5	X
ajst-8009	197	2	.	.	X
ajst-8009	197	3	conclusions	conclusion	NOUN
ajst-8009	197	4	and	and	CCONJ
ajst-8009	197	5	future	future	ADJ
ajst-8009	197	6	work	work	NOUN
ajst-8009	197	7	this	this	DET
ajst-8009	197	8	paper	paper	NOUN
ajst-8009	197	9	proposes	propose	VERB
ajst-8009	197	10	a	a	DET
ajst-8009	197	11	cnn	cnn	PROPN
ajst-8009	197	12	-	-	PUNCT
ajst-8009	197	13	lstm	lstm	ADJ
ajst-8009	197	14	model	model	NOUN
ajst-8009	197	15	based	base	VERB
ajst-8009	197	16	on	on	ADP
ajst-8009	197	17	temporal	temporal	ADJ
ajst-8009	197	18	pattern	pattern	NOUN
ajst-8009	197	19	attention	attention	NOUN
ajst-8009	197	20	for	for	ADP
ajst-8009	197	21	predicting	predict	VERB
ajst-8009	197	22	the	the	DET
ajst-8009	197	23	concentration	concentration	NOUN
ajst-8009	197	24	of	of	ADP
ajst-8009	197	25	pm2.5	pm2.5	PROPN
ajst-8009	197	26	over	over	ADP
ajst-8009	197	27	multiple	multiple	ADJ
ajst-8009	197	28	hours	hour	NOUN
ajst-8009	197	29	.	.	PUNCT
ajst-8009	198	1	the	the	DET
ajst-8009	198	2	model	model	NOUN
ajst-8009	198	3	uses	use	VERB
ajst-8009	198	4	air	air	PROPN
ajst-8009	198	5	quality	quality	PROPN
ajst-8009	198	6	data	datum	NOUN
ajst-8009	198	7	,	,	PUNCT
ajst-8009	198	8	meteorological	meteorological	ADJ
ajst-8009	198	9	data	datum	NOUN
ajst-8009	198	10	,	,	PUNCT
ajst-8009	198	11	and	and	CCONJ
ajst-8009	198	12	pm2.5	pm2.5	DET
ajst-8009	198	13	concentrations	concentration	NOUN
ajst-8009	198	14	from	from	ADP
ajst-8009	198	15	neighboring	neighbor	VERB
ajst-8009	198	16	monitoring	monitoring	NOUN
ajst-8009	198	17	stations	station	NOUN
ajst-8009	198	18	as	as	ADP
ajst-8009	198	19	inputs	input	NOUN
ajst-8009	198	20	to	to	PART
ajst-8009	198	21	capture	capture	VERB
ajst-8009	198	22	the	the	DET
ajst-8009	198	23	spatiotemporal	spatiotemporal	ADJ
ajst-8009	198	24	correlation	correlation	NOUN
ajst-8009	198	25	and	and	CCONJ
ajst-8009	198	26	long	long	ADJ
ajst-8009	198	27	-	-	PUNCT
ajst-8009	198	28	term	term	NOUN
ajst-8009	198	29	dependency	dependency	NOUN
ajst-8009	198	30	of	of	ADP
ajst-8009	198	31	pm2.5	pm2.5	PROPN
ajst-8009	198	32	.	.	PUNCT
ajst-8009	199	1	the	the	DET
ajst-8009	199	2	temporal	temporal	ADJ
ajst-8009	199	3	pattern	pattern	NOUN
ajst-8009	199	4	attention	attention	NOUN
ajst-8009	199	5	mechanism	mechanism	NOUN
ajst-8009	199	6	helps	help	VERB
ajst-8009	199	7	to	to	PART
ajst-8009	199	8	capture	capture	VERB
ajst-8009	199	9	the	the	DET
ajst-8009	199	10	importance	importance	NOUN
ajst-8009	199	11	of	of	ADP
ajst-8009	199	12	different	different	ADJ
ajst-8009	199	13	feature	feature	NOUN
ajst-8009	199	14	states	state	NOUN
ajst-8009	199	15	and	and	CCONJ
ajst-8009	199	16	improve	improve	VERB
ajst-8009	199	17	the	the	DET
ajst-8009	199	18	prediction	prediction	NOUN
ajst-8009	199	19	accuracy	accuracy	NOUN
ajst-8009	199	20	of	of	ADP
ajst-8009	199	21	the	the	DET
ajst-8009	199	22	model	model	NOUN
ajst-8009	199	23	.	.	PUNCT
ajst-8009	200	1	experimental	experimental	ADJ
ajst-8009	200	2	results	result	NOUN
ajst-8009	200	3	show	show	VERB
ajst-8009	200	4	that	that	SCONJ
ajst-8009	200	5	the	the	DET
ajst-8009	200	6	tpa	tpa	PROPN
ajst-8009	200	7	-	-	PUNCT
ajst-8009	200	8	cnn	cnn	PROPN
ajst-8009	200	9	-	-	PUNCT
ajst-8009	200	10	lstm	lstm	ADJ
ajst-8009	200	11	model	model	NOUN
ajst-8009	200	12	performs	perform	VERB
ajst-8009	200	13	well	well	ADV
ajst-8009	200	14	in	in	ADP
ajst-8009	200	15	multi	multi	ADJ
ajst-8009	200	16	-	-	ADJ
ajst-8009	200	17	scale	scale	ADJ
ajst-8009	200	18	prediction	prediction	NOUN
ajst-8009	200	19	tasks	task	NOUN
ajst-8009	200	20	.	.	PUNCT
ajst-8009	201	1	the	the	DET
ajst-8009	201	2	main	main	ADJ
ajst-8009	201	3	conclusions	conclusion	NOUN
ajst-8009	201	4	of	of	ADP
ajst-8009	201	5	this	this	DET
ajst-8009	201	6	paper	paper	NOUN
ajst-8009	201	7	are	be	AUX
ajst-8009	201	8	as	as	SCONJ
ajst-8009	201	9	follows	follow	VERB
ajst-8009	201	10	:	:	PUNCT
ajst-8009	201	11	analysis	analysis	NOUN
ajst-8009	201	12	of	of	ADP
ajst-8009	201	13	air	air	NOUN
ajst-8009	201	14	pollution	pollution	NOUN
ajst-8009	201	15	data	datum	NOUN
ajst-8009	201	16	shows	show	VERB
ajst-8009	201	17	that	that	SCONJ
ajst-8009	201	18	pm2.5	pm2.5	DET
ajst-8009	201	19	concentrations	concentration	NOUN
ajst-8009	201	20	have	have	VERB
ajst-8009	201	21	a	a	DET
ajst-8009	201	22	strong	strong	ADJ
ajst-8009	201	23	spatiotemporal	spatiotemporal	ADJ
ajst-8009	201	24	correlation	correlation	NOUN
ajst-8009	201	25	.	.	PUNCT
ajst-8009	202	1	due	due	ADP
ajst-8009	202	2	to	to	ADP
ajst-8009	202	3	the	the	DET
ajst-8009	202	4	propagation	propagation	NOUN
ajst-8009	202	5	of	of	ADP
ajst-8009	202	6	air	air	NOUN
ajst-8009	202	7	flow	flow	NOUN
ajst-8009	202	8	,	,	PUNCT
ajst-8009	202	9	pm2.5	pm2.5	DET
ajst-8009	202	10	concentrations	concentration	NOUN
ajst-8009	202	11	in	in	ADP
ajst-8009	202	12	the	the	DET
ajst-8009	202	13	prediction	prediction	NOUN
ajst-8009	202	14	range	range	NOUN
ajst-8009	202	15	are	be	AUX
ajst-8009	202	16	easily	easily	ADV
ajst-8009	202	17	affected	affect	VERB
ajst-8009	202	18	by	by	ADP
ajst-8009	202	19	pm2.5	pm2.5	DET
ajst-8009	202	20	concentrations	concentration	NOUN
ajst-8009	202	21	from	from	ADP
ajst-8009	202	22	neighboring	neighbor	VERB
ajst-8009	202	23	monitoring	monitoring	NOUN
ajst-8009	202	24	stations	station	NOUN
ajst-8009	202	25	.	.	PUNCT
ajst-8009	203	1	in	in	ADP
ajst-8009	203	2	addition	addition	NOUN
ajst-8009	203	3	,	,	PUNCT
ajst-8009	203	4	because	because	SCONJ
ajst-8009	203	5	pm2.5	pm2.5	ADV
ajst-8009	203	6	stays	stay	VERB
ajst-8009	203	7	in	in	ADP
ajst-8009	203	8	the	the	DET
ajst-8009	203	9	air	air	NOUN
ajst-8009	203	10	for	for	ADP
ajst-8009	203	11	a	a	DET
ajst-8009	203	12	long	long	ADJ
ajst-8009	203	13	time	time	NOUN
ajst-8009	203	14	,	,	PUNCT
ajst-8009	203	15	past	past	ADP
ajst-8009	203	16	feature	feature	NOUN
ajst-8009	203	17	states	state	NOUN
ajst-8009	203	18	also	also	ADV
ajst-8009	203	19	affect	affect	VERB
ajst-8009	203	20	future	future	ADJ
ajst-8009	203	21	pm2.5	pm2.5	ADJ
ajst-8009	203	22	concentrations	concentration	NOUN
ajst-8009	203	23	.	.	PUNCT
ajst-8009	204	1	furthermore	furthermore	ADV
ajst-8009	204	2	,	,	PUNCT
ajst-8009	204	3	compared	compare	VERB
ajst-8009	204	4	with	with	ADP
ajst-8009	204	5	a	a	DET
ajst-8009	204	6	single	single	ADJ
ajst-8009	204	7	model	model	NOUN
ajst-8009	204	8	,	,	PUNCT
ajst-8009	204	9	an	an	DET
ajst-8009	204	10	ensemble	ensemble	ADJ
ajst-8009	204	11	model	model	NOUN
ajst-8009	204	12	combines	combine	VERB
ajst-8009	204	13	the	the	DET
ajst-8009	204	14	feature	feature	NOUN
ajst-8009	204	15	extraction	extraction	NOUN
ajst-8009	204	16	capabilities	capability	NOUN
ajst-8009	204	17	of	of	ADP
ajst-8009	204	18	multiple	multiple	ADJ
ajst-8009	204	19	models	model	NOUN
ajst-8009	204	20	,	,	PUNCT
ajst-8009	204	21	resulting	result	VERB
ajst-8009	204	22	in	in	ADP
ajst-8009	204	23	more	more	ADV
ajst-8009	204	24	stable	stable	ADJ
ajst-8009	204	25	training	training	NOUN
ajst-8009	204	26	results	result	NOUN
ajst-8009	204	27	and	and	CCONJ
ajst-8009	204	28	stronger	strong	ADJ
ajst-8009	204	29	generalization	generalization	NOUN
ajst-8009	204	30	ability	ability	NOUN
ajst-8009	204	31	.	.	PUNCT
ajst-8009	205	1	the	the	DET
ajst-8009	205	2	tpa	tpa	PROPN
ajst-8009	205	3	-	-	PUNCT
ajst-8009	205	4	cnn	cnn	PROPN
ajst-8009	205	5	-	-	PUNCT
ajst-8009	205	6	lstm	lstm	PROPN
ajst-8009	205	7	prediction	prediction	NOUN
ajst-8009	205	8	model	model	NOUN
ajst-8009	205	9	can	can	AUX
ajst-8009	205	10	better	well	ADV
ajst-8009	205	11	capture	capture	VERB
ajst-8009	205	12	the	the	DET
ajst-8009	205	13	nonlinear	nonlinear	ADJ
ajst-8009	205	14	relationship	relationship	NOUN
ajst-8009	205	15	between	between	ADP
ajst-8009	205	16	each	each	DET
ajst-8009	205	17	input	input	NOUN
ajst-8009	205	18	variable	variable	NOUN
ajst-8009	205	19	and	and	CCONJ
ajst-8009	205	20	pm2.5	pm2.5	ADJ
ajst-8009	205	21	concentration	concentration	NOUN
ajst-8009	205	22	than	than	ADP
ajst-8009	205	23	other	other	ADJ
ajst-8009	205	24	models	model	NOUN
ajst-8009	205	25	,	,	PUNCT
ajst-8009	205	26	and	and	CCONJ
ajst-8009	205	27	has	have	VERB
ajst-8009	205	28	higher	high	ADJ
ajst-8009	205	29	prediction	prediction	NOUN
ajst-8009	205	30	accuracy	accuracy	NOUN
ajst-8009	205	31	.	.	PUNCT
ajst-8009	206	1	although	although	SCONJ
ajst-8009	206	2	the	the	DET
ajst-8009	206	3	model	model	NOUN
ajst-8009	206	4	achieved	achieve	VERB
ajst-8009	206	5	good	good	ADJ
ajst-8009	206	6	prediction	prediction	NOUN
ajst-8009	206	7	performance	performance	NOUN
ajst-8009	206	8	,	,	PUNCT
ajst-8009	206	9	there	there	PRON
ajst-8009	206	10	are	be	VERB
ajst-8009	206	11	still	still	ADV
ajst-8009	206	12	some	some	DET
ajst-8009	206	13	limitations	limitation	NOUN
ajst-8009	206	14	.	.	PUNCT
ajst-8009	207	1	the	the	DET
ajst-8009	207	2	data	datum	NOUN
ajst-8009	207	3	collected	collect	VERB
ajst-8009	207	4	in	in	ADP
ajst-8009	207	5	this	this	DET
ajst-8009	207	6	study	study	NOUN
ajst-8009	207	7	did	do	AUX
ajst-8009	207	8	not	not	PART
ajst-8009	207	9	consider	consider	VERB
ajst-8009	207	10	emissions	emission	NOUN
ajst-8009	207	11	from	from	ADP
ajst-8009	207	12	factories	factory	NOUN
ajst-8009	207	13	and	and	CCONJ
ajst-8009	207	14	vehicle	vehicle	NOUN
ajst-8009	207	15	exhaust	exhaust	NOUN
ajst-8009	207	16	,	,	PUNCT
ajst-8009	207	17	which	which	PRON
ajst-8009	207	18	are	be	AUX
ajst-8009	207	19	also	also	ADV
ajst-8009	207	20	important	important	ADJ
ajst-8009	207	21	factors	factor	NOUN
ajst-8009	207	22	in	in	ADP
ajst-8009	207	23	the	the	DET
ajst-8009	207	24	formation	formation	NOUN
ajst-8009	207	25	of	of	ADP
ajst-8009	207	26	air	air	NOUN
ajst-8009	207	27	pollutants	pollutant	NOUN
ajst-8009	207	28	.	.	PUNCT
ajst-8009	208	1	additionally	additionally	ADV
ajst-8009	208	2	,	,	PUNCT
ajst-8009	208	3	in	in	ADP
ajst-8009	208	4	the	the	DET
ajst-8009	208	5	future	future	NOUN
ajst-8009	208	6	,	,	PUNCT
ajst-8009	208	7	we	we	PRON
ajst-8009	208	8	will	will	AUX
ajst-8009	208	9	explore	explore	VERB
ajst-8009	208	10	the	the	DET
ajst-8009	208	11	use	use	NOUN
ajst-8009	208	12	of	of	ADP
ajst-8009	208	13	this	this	DET
ajst-8009	208	14	model	model	NOUN
ajst-8009	208	15	for	for	ADP
ajst-8009	208	16	large	large	ADJ
ajst-8009	208	17	-	-	PUNCT
ajst-8009	208	18	scale	scale	NOUN
ajst-8009	208	19	prediction	prediction	NOUN
ajst-8009	208	20	of	of	ADP
ajst-8009	208	21	other	other	ADJ
ajst-8009	208	22	air	air	NOUN
ajst-8009	208	23	pollutants	pollutant	NOUN
ajst-8009	208	24	and	and	CCONJ
ajst-8009	208	25	incorporate	incorporate	VERB
ajst-8009	208	26	satellite	satellite	NOUN
ajst-8009	208	27	meteorological	meteorological	ADJ
ajst-8009	208	28	data	datum	NOUN
ajst-8009	208	29	into	into	ADP
ajst-8009	208	30	the	the	DET
ajst-8009	208	31	input	input	NOUN
ajst-8009	208	32	of	of	ADP
ajst-8009	208	33	the	the	DET
ajst-8009	208	34	prediction	prediction	NOUN
ajst-8009	208	35	model	model	NOUN
ajst-8009	208	36	.	.	PUNCT
ajst-8009	209	1	6	6	X
ajst-8009	209	2	.	.	X
ajst-8009	209	3	funding	funding	NOUN
ajst-8009	209	4	supported	support	VERB
ajst-8009	209	5	by	by	ADP
ajst-8009	209	6	the	the	DET
ajst-8009	209	7	innovation	innovation	NOUN
ajst-8009	209	8	fund	fund	NOUN
ajst-8009	209	9	of	of	ADP
ajst-8009	209	10	postgraduate	postgraduate	NOUN
ajst-8009	209	11	,	,	PUNCT
ajst-8009	209	12	sichuan	sichuan	PROPN
ajst-8009	209	13	university	university	PROPN
ajst-8009	209	14	of	of	ADP
ajst-8009	209	15	science	science	PROPN
ajst-8009	209	16	&	&	CCONJ
ajst-8009	209	17	engineering	engineering	NOUN
ajst-8009	209	18	under	under	ADP
ajst-8009	209	19	grant	grant	PROPN
ajst-8009	209	20	y2022184	y2022184	PROPN
ajst-8009	209	21	.	.	PUNCT
ajst-8009	210	1	references	reference	NOUN
ajst-8009	210	2	[	[	X
ajst-8009	210	3	1	1	X
ajst-8009	210	4	]	]	PUNCT
ajst-8009	210	5	liang	liang	PROPN
ajst-8009	210	6	b	b	PROPN
ajst-8009	210	7	,	,	PUNCT
ajst-8009	210	8	li	li	PROPN
ajst-8009	210	9	x	x	PROPN
ajst-8009	210	10	,	,	PUNCT
ajst-8009	210	11	ma	ma	PROPN
ajst-8009	210	12	k	k	PROPN
ajst-8009	210	13	,	,	PUNCT
ajst-8009	210	14	et	et	PROPN
ajst-8009	210	15	al	al	PROPN
ajst-8009	210	16	.	.	PUNCT
ajst-8009	210	17	pollution	pollution	NOUN
ajst-8009	210	18	characteristics	characteristic	NOUN
ajst-8009	210	19	of	of	ADP
ajst-8009	210	20	metal	metal	NOUN
ajst-8009	210	21	pollutants	pollutant	NOUN
ajst-8009	210	22	in	in	ADP
ajst-8009	210	23	pm2	pm2	NOUN
ajst-8009	210	24	.	.	PROPN
ajst-8009	211	1	5	5	NUM
ajst-8009	211	2	and	and	CCONJ
ajst-8009	211	3	comparison	comparison	NOUN
ajst-8009	211	4	of	of	ADP
ajst-8009	211	5	risk	risk	NOUN
ajst-8009	211	6	on	on	ADP
ajst-8009	211	7	human	human	ADJ
ajst-8009	211	8	health	health	NOUN
ajst-8009	211	9	in	in	ADP
ajst-8009	211	10	heating	heating	NOUN
ajst-8009	211	11	and	and	CCONJ
ajst-8009	211	12	non	non	ADJ
ajst-8009	211	13	-	-	ADJ
ajst-8009	211	14	heating	heating	ADJ
ajst-8009	211	15	seasons	season	NOUN
ajst-8009	211	16	in	in	ADP
ajst-8009	211	17	baoding	baoding	PROPN
ajst-8009	211	18	,	,	PUNCT
ajst-8009	211	19	china[j	china[j	PROPN
ajst-8009	211	20	]	]	PUNCT
ajst-8009	211	21	.	.	PUNCT
ajst-8009	212	1	ecotoxicology	ecotoxicology	NOUN
ajst-8009	212	2	and	and	CCONJ
ajst-8009	212	3	environmental	environmental	ADJ
ajst-8009	212	4	safety	safety	NOUN
ajst-8009	212	5	,	,	PUNCT
ajst-8009	212	6	2019	2019	NUM
ajst-8009	212	7	,	,	PUNCT
ajst-8009	212	8	170	170	NUM
ajst-8009	212	9	:	:	SYM
ajst-8009	212	10	166	166	NUM
ajst-8009	212	11	-	-	SYM
ajst-8009	212	12	171	171	NUM
ajst-8009	212	13	.	.	PUNCT
ajst-8009	213	1	[	[	X
ajst-8009	213	2	2	2	NUM
ajst-8009	213	3	]	]	PUNCT
ajst-8009	213	4	kioumouｒtzoglou	kioumouｒtzoglou	NOUN
ajst-8009	213	5	m	m	PROPN
ajst-8009	213	6	a，schwaｒtz	a，schwaｒtz	NOUN
ajst-8009	213	7	j，james	j，jame	NOUN
ajst-8009	213	8	p	p	X
ajst-8009	213	9	，	，	PUNCT
ajst-8009	213	10	et	et	NOUN
ajst-8009	213	11	al．pm2	al．pm2	NOUN
ajst-8009	213	12	.	.	PUNCT
ajst-8009	214	1	5	5	NUM
ajst-8009	214	2	and	and	CCONJ
ajst-8009	214	3	mortality	mortality	NOUN
ajst-8009	214	4	in	in	ADP
ajst-8009	214	5	207	207	NUM
ajst-8009	214	6	us	us	PROPN
ajst-8009	214	7	cities	city	NOUN
ajst-8009	214	8	modification	modification	NOUN
ajst-8009	214	9	by	by	ADP
ajst-8009	214	10	temperature	temperature	NOUN
ajst-8009	214	11	and	and	CCONJ
ajst-8009	214	12	city	city	NOUN
ajst-8009	214	13	characteristics[j	characteristics[j	PROPN
ajst-8009	214	14	]	]	PUNCT
ajst-8009	214	15	.	.	PUNCT
ajst-8009	215	1	epidemiology	epidemiology	NOUN
ajst-8009	215	2	,	,	PUNCT
ajst-8009	215	3	2016	2016	NUM
ajst-8009	215	4	,	,	PUNCT
ajst-8009	215	5	27	27	NUM
ajst-8009	215	6	(	(	PUNCT
ajst-8009	215	7	2	2	NUM
ajst-8009	215	8	)	)	PUNCT
ajst-8009	215	9	:	:	PUNCT
ajst-8009	215	10	221	221	NUM
ajst-8009	215	11	-	-	SYM
ajst-8009	215	12	227．	227．	NUM
ajst-8009	215	13	[	[	X
ajst-8009	215	14	3	3	NUM
ajst-8009	215	15	]	]	X
ajst-8009	215	16	zhang	zhang	PROPN
ajst-8009	215	17	y	y	PROPN
ajst-8009	215	18	,	,	PUNCT
ajst-8009	215	19	guo	guo	PROPN
ajst-8009	215	20	a	a	X
ajst-8009	215	21	,	,	PUNCT
ajst-8009	215	22	wu	wu	PROPN
ajst-8009	215	23	h	h	PROPN
ajst-8009	215	24	,	,	PUNCT
ajst-8009	215	25	et	et	PROPN
ajst-8009	215	26	al	al	PROPN
ajst-8009	215	27	.	.	PROPN
ajst-8009	216	1	seasonal	seasonal	ADJ
ajst-8009	216	2	prediction	prediction	NOUN
ajst-8009	216	3	of	of	ADP
ajst-8009	216	4	pm2	pm2	NOUN
ajst-8009	216	5	.	.	PUNCT
ajst-8009	217	1	5	5	NUM
ajst-8009	217	2	based	base	VERB
ajst-8009	217	3	on	on	ADP
ajst-8009	217	4	the	the	DET
ajst-8009	217	5	pca	pca	PROPN
ajst-8009	217	6	-	-	PUNCT
ajst-8009	217	7	bp	bp	PROPN
ajst-8009	217	8	neural	neural	ADJ
ajst-8009	217	9	network[j	network[j	PROPN
ajst-8009	217	10	]	]	PUNCT
ajst-8009	217	11	.	.	PUNCT
ajst-8009	218	1	journal	journal	PROPN
ajst-8009	218	2	of	of	ADP
ajst-8009	218	3	nanjing	nanjing	PROPN
ajst-8009	218	4	forestry	forestry	PROPN
ajst-8009	218	5	university	university	PROPN
ajst-8009	218	6	,	,	PUNCT
ajst-8009	218	7	2020	2020	NUM
ajst-8009	218	8	,	,	PUNCT
ajst-8009	218	9	44(5	44(5	NUM
ajst-8009	218	10	):	):	PUNCT
ajst-8009	218	11	231	231	NUM
ajst-8009	218	12	-	-	SYM
ajst-8009	218	13	241	241	NUM
ajst-8009	218	14	.	.	PUNCT
ajst-8009	219	1	[	[	X
ajst-8009	219	2	4	4	NUM
ajst-8009	219	3	]	]	X
ajst-8009	219	4	samal	samal	PROPN
ajst-8009	219	5	k	k	PROPN
ajst-8009	219	6	,	,	PUNCT
ajst-8009	219	7	babu	babu	PROPN
ajst-8009	219	8	k	k	PROPN
ajst-8009	219	9	,	,	PUNCT
ajst-8009	219	10	das	das	PROPN
ajst-8009	219	11	s.	s.	PROPN
ajst-8009	219	12	multi	multi	PROPN
ajst-8009	219	13	-	-	ADJ
ajst-8009	219	14	directional	directional	ADJ
ajst-8009	219	15	temporal	temporal	ADJ
ajst-8009	219	16	convolutional	convolutional	ADJ
ajst-8009	219	17	artificial	artificial	ADJ
ajst-8009	219	18	neural	neural	ADJ
ajst-8009	219	19	network	network	NOUN
ajst-8009	219	20	for	for	ADP
ajst-8009	219	21	pm2.5	pm2.5	NOUN
ajst-8009	219	22	forecasting	forecasting	NOUN
ajst-8009	219	23	with	with	ADP
ajst-8009	219	24	missing	miss	VERB
ajst-8009	219	25	values	value	NOUN
ajst-8009	219	26	:	:	PUNCT
ajst-8009	219	27	a	a	DET
ajst-8009	219	28	deep	deep	ADJ
ajst-8009	219	29	learning	learning	NOUN
ajst-8009	219	30	approach[j	approach[j	PROPN
ajst-8009	219	31	]	]	PUNCT
ajst-8009	219	32	.	.	PUNCT
ajst-8009	220	1	urban	urban	ADJ
ajst-8009	220	2	climate	climate	NOUN
ajst-8009	220	3	,	,	PUNCT
ajst-8009	220	4	2021	2021	NUM
ajst-8009	220	5	,	,	PUNCT
ajst-8009	220	6	36	36	NUM
ajst-8009	220	7	,	,	PUNCT
ajst-8009	220	8	100800:1	100800:1	NUM
ajst-8009	220	9	-	-	SYM
ajst-8009	220	10	10	10	NUM
ajst-8009	220	11	.	.	PUNCT
ajst-8009	221	1	[	[	X
ajst-8009	221	2	5	5	X
ajst-8009	221	3	]	]	X
ajst-8009	221	4	liu	liu	PROPN
ajst-8009	221	5	linbo	linbo	PROPN
ajst-8009	221	6	,	,	PUNCT
ajst-8009	221	7	liu	liu	PROPN
ajst-8009	221	8	lilong	lilong	PROPN
ajst-8009	221	9	,	,	PUNCT
ajst-8009	221	10	li	li	PROPN
ajst-8009	221	11	junyu	junyu	PROPN
ajst-8009	221	12	,	,	PUNCT
ajst-8009	221	13	et	et	PROPN
ajst-8009	221	14	al	al	PROPN
ajst-8009	221	15	.	.	PROPN
ajst-8009	221	16	predition	predition	NOUN
ajst-8009	221	17	of	of	ADP
ajst-8009	221	18	pm2	pm2	NOUN
ajst-8009	221	19	.	.	PUNCT
ajst-8009	222	1	5	5	NUM
ajst-8009	222	2	mass	mass	NOUN
ajst-8009	222	3	concentration	concentration	NOUN
ajst-8009	222	4	based	base	VERB
ajst-8009	222	5	on	on	ADP
ajst-8009	222	6	ga	ga	PROPN
ajst-8009	222	7	-	-	PUNCT
ajst-8009	222	8	bp	bp	PROPN
ajst-8009	222	9	neural	neural	ADJ
ajst-8009	222	10	network	network	NOUN
ajst-8009	222	11	with	with	ADP
ajst-8009	222	12	water	water	NOUN
ajst-8009	222	13	vapor[j	vapor[j	NOUN
ajst-8009	222	14	]	]	PUNCT
ajst-8009	222	15	.	.	PUNCT
ajst-8009	223	1	journal	journal	PROPN
ajst-8009	223	2	of	of	ADP
ajst-8009	223	3	guilin	guilin	PROPN
ajst-8009	223	4	university	university	PROPN
ajst-8009	223	5	of	of	ADP
ajst-8009	223	6	technology	technology	NOUN
ajst-8009	223	7	,	,	PUNCT
ajst-8009	223	8	2019	2019	NUM
ajst-8009	223	9	,	,	PUNCT
ajst-8009	223	10	39(02	39(02	NUM
ajst-8009	223	11	):	):	PUNCT
ajst-8009	223	12	420	420	NUM
ajst-8009	223	13	-	-	SYM
ajst-8009	223	14	426	426	NUM
ajst-8009	223	15	.	.	PUNCT
ajst-8009	224	1	[	[	X
ajst-8009	224	2	6	6	NUM
ajst-8009	224	3	]	]	X
ajst-8009	224	4	li	li	PROPN
ajst-8009	224	5	s	s	PROPN
ajst-8009	224	6	,	,	PUNCT
ajst-8009	224	7	xie	xie	PROPN
ajst-8009	224	8	g	g	PROPN
ajst-8009	224	9	,	,	PUNCT
ajst-8009	224	10	ren	ren	PROPN
ajst-8009	224	11	j	j	PROPN
ajst-8009	224	12	,	,	PUNCT
ajst-8009	224	13	et	et	PROPN
ajst-8009	224	14	al	al	PROPN
ajst-8009	224	15	.	.	PUNCT
ajst-8009	225	1	urban	urban	ADJ
ajst-8009	225	2	pm2.5	pm2.5	PROPN
ajst-8009	225	3	concentration	concentration	NOUN
ajst-8009	225	4	prediction	prediction	NOUN
ajst-8009	225	5	via	via	ADP
ajst-8009	225	6	attention	attention	NOUN
ajst-8009	225	7	-	-	PUNCT
ajst-8009	225	8	based	base	VERB
ajst-8009	225	9	cnn	cnn	PROPN
ajst-8009	225	10	–	–	PUNCT
ajst-8009	225	11	lstm[j	lstm[j	PROPN
ajst-8009	225	12	]	]	PUNCT
ajst-8009	225	13	.	.	PUNCT
ajst-8009	226	1	applied	apply	VERB
ajst-8009	226	2	sciences	science	NOUN
ajst-8009	226	3	,	,	PUNCT
ajst-8009	226	4	2020	2020	NUM
ajst-8009	226	5	,	,	PUNCT
ajst-8009	226	6	10(6),1953	10(6),1953	NUM
ajst-8009	226	7	:	:	PUNCT
ajst-8009	226	8	1	1	NUM
ajst-8009	226	9	-	-	SYM
ajst-8009	226	10	17	17	NUM
ajst-8009	226	11	.	.	PUNCT
ajst-8009	227	1	[	[	X
ajst-8009	227	2	7	7	X
ajst-8009	227	3	]	]	X
ajst-8009	227	4	liu	liu	PROPN
ajst-8009	227	5	x	x	PROPN
ajst-8009	227	6	,	,	PUNCT
ajst-8009	227	7	liu	liu	PROPN
ajst-8009	227	8	q	q	PROPN
ajst-8009	227	9	,	,	PUNCT
ajst-8009	227	10	zou	zou	PROPN
ajst-8009	227	11	y	y	PROPN
ajst-8009	227	12	,	,	PUNCT
ajst-8009	227	13	et	et	PROPN
ajst-8009	227	14	al	al	PROPN
ajst-8009	227	15	.	.	PUNCT
ajst-8009	228	1	a	a	DET
ajst-8009	228	2	self	self	NOUN
ajst-8009	228	3	-	-	PUNCT
ajst-8009	228	4	organizing	organize	VERB
ajst-8009	228	5	lstm	lstm	NOUN
ajst-8009	228	6	-	-	PUNCT
ajst-8009	228	7	based	base	VERB
ajst-8009	228	8	approach	approach	NOUN
ajst-8009	228	9	to	to	ADP
ajst-8009	228	10	pm2	pm2	NOUN
ajst-8009	228	11	.	.	PUNCT
ajst-8009	229	1	5	5	NUM
ajst-8009	229	2	forecast[c]//cloud	forecast[c]//cloud	PROPN
ajst-8009	229	3	computing	computing	NOUN
ajst-8009	229	4	and	and	CCONJ
ajst-8009	229	5	security	security	NOUN
ajst-8009	229	6	:	:	PUNCT
ajst-8009	229	7	4th	4th	ADJ
ajst-8009	229	8	international	international	ADJ
ajst-8009	229	9	conference	conference	NOUN
ajst-8009	229	10	,	,	PUNCT
ajst-8009	229	11	icccs	icccs	NOUN
ajst-8009	229	12	2018	2018	NUM
ajst-8009	229	13	,	,	PUNCT
ajst-8009	229	14	haikou	haikou	PROPN
ajst-8009	229	15	,	,	PUNCT
ajst-8009	229	16	china	china	PROPN
ajst-8009	229	17	,	,	PUNCT
ajst-8009	229	18	june	june	PROPN
ajst-8009	229	19	8–10	8–10	PROPN
ajst-8009	229	20	,	,	PUNCT
ajst-8009	229	21	2018	2018	NUM
ajst-8009	229	22	,	,	PUNCT
ajst-8009	229	23	revised	revise	VERB
ajst-8009	229	24	selected	selected	ADJ
ajst-8009	229	25	papers	paper	NOUN
ajst-8009	229	26	,	,	PUNCT
ajst-8009	229	27	part	part	NOUN
ajst-8009	229	28	iv	iv	NUM
ajst-8009	229	29	4	4	NUM
ajst-8009	229	30	.	.	PUNCT
ajst-8009	229	31	springer	springer	NOUN
ajst-8009	229	32	international	international	ADJ
ajst-8009	229	33	publishing	publishing	NOUN
ajst-8009	229	34	,	,	PUNCT
ajst-8009	229	35	2018	2018	NUM
ajst-8009	229	36	:	:	PUNCT
ajst-8009	229	37	683	683	NUM
ajst-8009	229	38	-	-	SYM
ajst-8009	229	39	693	693	NUM
ajst-8009	229	40	.	.	PUNCT
ajst-8009	230	1	[	[	X
ajst-8009	230	2	8	8	NUM
ajst-8009	230	3	]	]	X
ajst-8009	230	4	chaloulakou	chaloulakou	PROPN
ajst-8009	230	5	,	,	PUNCT
ajst-8009	230	6	a.	a.	NOUN
ajst-8009	230	7	kassomenos	kassomenos	PROPN
ajst-8009	230	8	,	,	PUNCT
ajst-8009	230	9	p.	p.	PROPN
ajst-8009	230	10	spyrellis	spyrellis	PROPN
ajst-8009	230	11	,	,	PUNCT
ajst-8009	230	12	n.	n.	PROPN
ajst-8009	230	13	demokritou	demokritou	NOUN
ajst-8009	230	14	,	,	PUNCT
ajst-8009	230	15	p.	p.	PROPN
ajst-8009	230	16	koutrakis	koutrakis	PROPN
ajst-8009	230	17	,	,	PUNCT
ajst-8009	230	18	p.	p.	NOUN
ajst-8009	230	19	measurements	measurement	NOUN
ajst-8009	230	20	of	of	ADP
ajst-8009	230	21	pm10	pm10	PROPN
ajst-8009	230	22	and	and	CCONJ
ajst-8009	230	23	pm2.5	pm2.5	DET
ajst-8009	230	24	particle	particle	NOUN
ajst-8009	230	25	concentrations	concentration	NOUN
ajst-8009	230	26	in	in	ADP
ajst-8009	230	27	athens	athens	PROPN
ajst-8009	230	28	,	,	PUNCT
ajst-8009	230	29	greece	greece	PROPN
ajst-8009	230	30	.	.	PUNCT
ajst-8009	230	31	atmos	atmos	PROPN
ajst-8009	230	32	.	.	PUNCT
ajst-8009	231	1	environ	environ	PROPN
ajst-8009	231	2	.	.	PROPN
ajst-8009	231	3	2003	2003	NUM
ajst-8009	231	4	,	,	PUNCT
ajst-8009	231	5	37	37	NUM
ajst-8009	231	6	,	,	PUNCT
ajst-8009	231	7	649–660	649–660	NUM
ajst-8009	231	8	.	.	PUNCT
ajst-8009	232	1	[	[	X
ajst-8009	232	2	9	9	NUM
ajst-8009	232	3	]	]	SYM
ajst-8009	232	4	hussein	hussein	NOUN
ajst-8009	232	5	,	,	PUNCT
ajst-8009	232	6	t.	t.	PROPN
ajst-8009	232	7	karppinen	karppinen	PROPN
ajst-8009	232	8	,	,	PUNCT
ajst-8009	232	9	a.	a.	PROPN
ajst-8009	232	10	kukkonen	kukkonen	PROPN
ajst-8009	232	11	,	,	PUNCT
ajst-8009	232	12	j.	j.	PROPN
ajst-8009	232	13	härkönen	härkönen	PROPN
ajst-8009	232	14	,	,	PUNCT
ajst-8009	232	15	j.	j.	PROPN
ajst-8009	232	16	aalto	aalto	PROPN
ajst-8009	232	17	,	,	PUNCT
ajst-8009	232	18	p.p	p.p	PROPN
ajst-8009	232	19	.	.	PROPN
ajst-8009	232	20	hämeri	hämeri	PROPN
ajst-8009	232	21	,	,	PUNCT
ajst-8009	232	22	k.	k.	PROPN
ajst-8009	232	23	;	;	PUNCT
ajst-8009	232	24	kerminen	kerminen	PROPN
ajst-8009	232	25	,	,	PUNCT
ajst-8009	232	26	v.m.kulmala	v.m.kulmala	NOUN
ajst-8009	232	27	,	,	PUNCT
ajst-8009	232	28	m.	m.	NOUN
ajst-8009	232	29	meteorological	meteorological	ADJ
ajst-8009	232	30	dependence	dependence	NOUN
ajst-8009	232	31	of	of	ADP
ajst-8009	232	32	size	size	NOUN
ajst-8009	232	33	-	-	PUNCT
ajst-8009	232	34	fractionated	fractionate	VERB
ajst-8009	232	35	number	number	NOUN
ajst-8009	232	36	concentrations	concentration	NOUN
ajst-8009	232	37	of	of	ADP
ajst-8009	232	38	urban	urban	ADJ
ajst-8009	232	39	aerosol	aerosol	NOUN
ajst-8009	232	40	particles	particle	NOUN
ajst-8009	232	41	.	.	PUNCT
ajst-8009	233	1	atmos	atmos	PROPN
ajst-8009	233	2	.	.	PUNCT
ajst-8009	234	1	environ	environ	PROPN
ajst-8009	234	2	.	.	PUNCT
ajst-8009	235	1	2006	2006	NUM
ajst-8009	235	2	,	,	PUNCT
ajst-8009	235	3	40	40	NUM
ajst-8009	235	4	,	,	PUNCT
ajst-8009	235	5	1427–1440	1427–1440	NUM
ajst-8009	235	6	.	.	PUNCT
ajst-8009	236	1	[	[	X
ajst-8009	236	2	10	10	NUM
ajst-8009	236	3	]	]	X
ajst-8009	236	4	chen	chen	PROPN
ajst-8009	236	5	,	,	PUNCT
ajst-8009	236	6	j	j	PROPN
ajst-8009	236	7	,	,	PUNCT
ajst-8009	236	8	lu	lu	PROPN
ajst-8009	236	9	,	,	PUNCT
ajst-8009	236	10	j.	j.	PROPN
ajst-8009	236	11	avise	avise	PROPN
ajst-8009	236	12	,	,	PUNCT
ajst-8009	236	13	j.c	j.c	PROPN
ajst-8009	236	14	.	.	PROPN
ajst-8009	236	15	damassa	damassa	PROPN
ajst-8009	236	16	,	,	PUNCT
ajst-8009	236	17	j.a	j.a	PROPN
ajst-8009	236	18	.	.	PROPN
ajst-8009	236	19	kleeman	kleeman	PROPN
ajst-8009	236	20	,	,	PUNCT
ajst-8009	236	21	m.j	m.j	PROPN
ajst-8009	236	22	.	.	PROPN
ajst-8009	236	23	kaduwela	kaduwela	PROPN
ajst-8009	236	24	,	,	PUNCT
ajst-8009	236	25	a.p	a.p	PROPN
ajst-8009	236	26	.	.	PROPN
ajst-8009	236	27	seasonal	seasonal	ADJ
ajst-8009	236	28	modeling	modeling	NOUN
ajst-8009	236	29	of	of	ADP
ajst-8009	236	30	pm2.5	pm2.5	PROPN
ajst-8009	236	31	in	in	ADP
ajst-8009	236	32	california	california	PROPN
ajst-8009	236	33	’s	’s	PART
ajst-8009	236	34	san	san	PROPN
ajst-8009	236	35	joaquin	joaquin	PROPN
ajst-8009	236	36	valley	valley	PROPN
ajst-8009	236	37	.	.	PUNCT
ajst-8009	237	1	atmos	atmos	PROPN
ajst-8009	237	2	.	.	PUNCT
ajst-8009	238	1	environ	environ	PROPN
ajst-8009	238	2	.	.	PROPN
ajst-8009	238	3	2014	2014	NUM
ajst-8009	238	4	,	,	PUNCT
ajst-8009	238	5	92	92	NUM
ajst-8009	238	6	,	,	PUNCT
ajst-8009	238	7	182–190	182–190	NUM
ajst-8009	238	8	.	.	PUNCT