A new cross-domain prediction model of air pollutant concentration based on secure federated learning and optimized LSTM neural network
As air pollution worsens, the fast prediction of air pollutant concentration becomes increasingly important for public health. This paper proposes a new cross-domain prediction model of air pollutant concentration based on federated learning (FL), differential privacy laplace mechanism (DPLA) and lo...
Saved in:
| Published in: | Environmental science and pollution research international Vol. 30; no. 2; pp. 5103 - 5125 |
|---|---|
| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Berlin/Heidelberg
Springer Berlin Heidelberg
01.01.2023
Springer Nature B.V |
| Subjects: | |
| ISSN: | 0944-1344, 1614-7499, 1614-7499 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | As air pollution worsens, the fast prediction of air pollutant concentration becomes increasingly important for public health. This paper proposes a new cross-domain prediction model of air pollutant concentration based on federated learning (FL), differential privacy laplace mechanism (DPLA) and long and short-term memory network optimized by sparrow search algorithm (SSA-LSTM), named FL-DPLA-SSA-LSTM. Firstly, with FL, SSA-LSTM is used as local training model for each city and predicts air pollutant concentration. Secondly, DPLA is used to add noise to the local model parameters, which can protect local data security. Then, the global model is updated by using the federated averaging algorithm (FedAvg). Lastly, FL is used to share global model for all cities, which can safely and quickly cross-domain predict air pollutant concentration. For data set, it is taken from hourly air pollutants and meteorological data from 12 cities in the Fenhe River and Weihe River Plains in China in 2020. The experimental results show that the prediction performance of the proposed model is significantly better than all comparison models. FedAvg updating with local model parameters with DPLA noise has little effect on the performance of the global model and even exceeds that of the global model. The calculation time of FL-DPLA-SSA-LSTM model is reduced by 99.95% compared with that of not using FL-DPLA machine learning model. It is proved that the model is high sharing and high safety, which greatly improves the training efficiency and has better generalization ability. It is significant for joint air pollution prevention and control and environmental protection. |
|---|---|
| AbstractList | As air pollution worsens, the fast prediction of air pollutant concentration becomes increasingly important for public health. This paper proposes a new cross-domain prediction model of air pollutant concentration based on federated learning (FL), differential privacy laplace mechanism (DPLA) and long and short-term memory network optimized by sparrow search algorithm (SSA-LSTM), named FL-DPLA-SSA-LSTM. Firstly, with FL, SSA-LSTM is used as local training model for each city and predicts air pollutant concentration. Secondly, DPLA is used to add noise to the local model parameters, which can protect local data security. Then, the global model is updated by using the federated averaging algorithm (FedAvg). Lastly, FL is used to share global model for all cities, which can safely and quickly cross-domain predict air pollutant concentration. For data set, it is taken from hourly air pollutants and meteorological data from 12 cities in the Fenhe River and Weihe River Plains in China in 2020. The experimental results show that the prediction performance of the proposed model is significantly better than all comparison models. FedAvg updating with local model parameters with DPLA noise has little effect on the performance of the global model and even exceeds that of the global model. The calculation time of FL-DPLA-SSA-LSTM model is reduced by 99.95% compared with that of not using FL-DPLA machine learning model. It is proved that the model is high sharing and high safety, which greatly improves the training efficiency and has better generalization ability. It is significant for joint air pollution prevention and control and environmental protection.As air pollution worsens, the fast prediction of air pollutant concentration becomes increasingly important for public health. This paper proposes a new cross-domain prediction model of air pollutant concentration based on federated learning (FL), differential privacy laplace mechanism (DPLA) and long and short-term memory network optimized by sparrow search algorithm (SSA-LSTM), named FL-DPLA-SSA-LSTM. Firstly, with FL, SSA-LSTM is used as local training model for each city and predicts air pollutant concentration. Secondly, DPLA is used to add noise to the local model parameters, which can protect local data security. Then, the global model is updated by using the federated averaging algorithm (FedAvg). Lastly, FL is used to share global model for all cities, which can safely and quickly cross-domain predict air pollutant concentration. For data set, it is taken from hourly air pollutants and meteorological data from 12 cities in the Fenhe River and Weihe River Plains in China in 2020. The experimental results show that the prediction performance of the proposed model is significantly better than all comparison models. FedAvg updating with local model parameters with DPLA noise has little effect on the performance of the global model and even exceeds that of the global model. The calculation time of FL-DPLA-SSA-LSTM model is reduced by 99.95% compared with that of not using FL-DPLA machine learning model. It is proved that the model is high sharing and high safety, which greatly improves the training efficiency and has better generalization ability. It is significant for joint air pollution prevention and control and environmental protection. As air pollution worsens, the fast prediction of air pollutant concentration becomes increasingly important for public health. This paper proposes a new cross-domain prediction model of air pollutant concentration based on federated learning (FL), differential privacy laplace mechanism (DPLA) and long and short-term memory network optimized by sparrow search algorithm (SSA-LSTM), named FL-DPLA-SSA-LSTM. Firstly, with FL, SSA-LSTM is used as local training model for each city and predicts air pollutant concentration. Secondly, DPLA is used to add noise to the local model parameters, which can protect local data security. Then, the global model is updated by using the federated averaging algorithm (FedAvg). Lastly, FL is used to share global model for all cities, which can safely and quickly cross-domain predict air pollutant concentration. For data set, it is taken from hourly air pollutants and meteorological data from 12 cities in the Fenhe River and Weihe River Plains in China in 2020. The experimental results show that the prediction performance of the proposed model is significantly better than all comparison models. FedAvg updating with local model parameters with DPLA noise has little effect on the performance of the global model and even exceeds that of the global model. The calculation time of FL-DPLA-SSA-LSTM model is reduced by 99.95% compared with that of not using FL-DPLA machine learning model. It is proved that the model is high sharing and high safety, which greatly improves the training efficiency and has better generalization ability. It is significant for joint air pollution prevention and control and environmental protection. |
| Author | Lu, Qiuqin Zhao, Xixuan Huang, Guangqiu |
| Author_xml | – sequence: 1 givenname: Guangqiu surname: Huang fullname: Huang, Guangqiu email: huangnan93@163.com organization: School of Management, Xi’an University of Architecture and Technology – sequence: 2 givenname: Xixuan orcidid: 0000-0003-3910-9014 surname: Zhao fullname: Zhao, Xixuan email: 2694155379@qq.com organization: School of Management, Xi’an University of Architecture and Technology – sequence: 3 givenname: Qiuqin surname: Lu fullname: Lu, Qiuqin organization: School of Management, Xi’an University of Architecture and Technology |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/35974279$$D View this record in MEDLINE/PubMed |
| BookMark | eNqFkc9uFSEUh4mpsbfVF3BhSNx0g_JvhmHZNFZNbuPCdk0Y5tBQZ2CEmTT6Ar629N5Wky7a1SGH78cBviN0EFMEhN4y-oFRqj4WxkTTEso54Vw2krQv0Ia1TBIltT5AG6qlJExIeYiOSrmhlFPN1St0KBqtJFd6g_6c4gi32OVUChnSZEPEc4YhuCWkiKc0wIiTxzZkPKdxXBcbF-xSdBCXbHdQbwsMuC4KuDUD9jBA3aq9EWyOIV5jGyswL2EKv2t7-_3yoo5dsx1rWW5T_vEavfR2LPDmvh6jq_NPl2dfyPbb569np1vihOgWojttNfVtw3QvOue5tD0IrxVYJrwHIZRqlReesYExJ7kVPbfSDpq7tul6cYxO9ufOOf1coSxmCsXBONoIaS2Gd51qRf1d9jyqqJBMU6Er-v4RepPWHOtDKqWY7LRs76h399TaTzCYOYfJ5l_mwUYF-B7Y6cjg_yGMmjvlZq_cVOVmp9y0NdQ9Crmw7MRUP2F8Oir20VLnxGvI_6_9ROovo8_AoQ |
| CitedBy_id | crossref_primary_10_1016_j_ecoenv_2024_116532 crossref_primary_10_1371_journal_pone_0305665 crossref_primary_10_1016_j_cie_2024_110477 crossref_primary_10_3390_ani14142021 crossref_primary_10_3390_computers14080321 crossref_primary_10_1038_s41598_025_00911_9 crossref_primary_10_3390_a15110434 crossref_primary_10_1080_10589759_2025_2512557 crossref_primary_10_3390_su15129713 crossref_primary_10_1016_j_jhazmat_2023_133099 crossref_primary_10_3390_su151813951 crossref_primary_10_1007_s10661_024_12939_x crossref_primary_10_1016_j_jclepro_2023_138676 |
| Cites_doi | 10.1016/j.scitotenv.2019.05.186 10.1016/j.apr.2018.02.006 10.1016/j.tej.2020.106883 10.1289/EHP4595 10.1007/s40747-021-00435-5 10.1088/1742-6596/1192/1/012010 10.23919/CJEE.2019.000025 10.3390/su132112071 10.1126/science.1127647 10.1016/j.envpol.2022.118932 10.3390/su122310090 10.1109/ACCESS.2020.3029828 10.1007/s12145-021-00618-1 10.1016/j.envpol.2017.08.114 10.1016/j.atmosenv.2011.08.066 10.4209/aaqr.2019.08.0408 10.1016/j.apr.2020.09.003 10.1371/journal.pone.0212320 10.3390/atmos12121626 10.1016/j.chemosphere.2021.133124 10.3390/s21092993 10.1016/j.physa.2019.123799 10.1016/j.rse.2016.07.015 10.1080/21642583.2019.1708830 10.1016/j.knosys.2020.106002 10.1016/j.atmosenv.2011.03.074 10.1016/j.chemosphere.2020.126735 10.3390/s21041235 10.1016/j.apr.2019.09.009 10.1109/TSG.2017.2753802 10.1088/1755-1315/651/4/042068 10.1080/0194262X.2020.1859046 10.5194/isprs-annals-IV-4-W2-15-2017 10.1109/IJCNN.1992.227046 10.1145/2971648.2971725 |
| ContentType | Journal Article |
| Copyright | The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature. |
| Copyright_xml | – notice: The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. – notice: 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature. |
| DBID | AAYXX CITATION CGR CUY CVF ECM EIF NPM 3V. 7QL 7SN 7T7 7TV 7U7 7WY 7WZ 7X7 7XB 87Z 88E 88I 8AO 8C1 8FD 8FI 8FJ 8FK 8FL ABUWG AEUYN AFKRA ATCPS AZQEC BENPR BEZIV BHPHI C1K CCPQU DWQXO FR3 FRNLG FYUFA F~G GHDGH GNUQQ HCIFZ K60 K6~ K9. L.- M0C M0S M1P M2P M7N P64 PATMY PHGZM PHGZT PJZUB PKEHL PPXIY PQBIZ PQBZA PQEST PQQKQ PQUKI PYCSY Q9U 7X8 7S9 L.6 |
| DOI | 10.1007/s11356-022-22454-6 |
| DatabaseName | CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed ProQuest Central (Corporate) Bacteriology Abstracts (Microbiology B) Ecology Abstracts Industrial and Applied Microbiology Abstracts (Microbiology A) Pollution Abstracts Toxicology Abstracts ABI/INFORM Collection ABI/INFORM Global (PDF only) Health & Medical Collection ProQuest Central (purchase pre-March 2016) ABI/INFORM Collection Medical Database (Alumni Edition) Science Database (Alumni Edition) ProQuest Pharma Collection Public Health Database Technology Research Database Hospital Premium Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ABI/INFORM Collection (Alumni) ProQuest Central (Alumni) ProQuest One Sustainability ProQuest Central UK/Ireland Agricultural & Environmental Science Collection ProQuest Central Essentials - QC ProQuest Central Business Premium Collection Natural Science Collection Environmental Sciences and Pollution Management ProQuest One ProQuest Central Korea Engineering Research Database Business Premium Collection (Alumni) Proquest Health Research Premium Collection ABI/INFORM Global (Corporate) Health Research Premium Collection (Alumni) ProQuest Central Student SciTech Premium Collection ProQuest Business Collection (Alumni Edition) ProQuest Business Collection ProQuest Health & Medical Complete (Alumni) ABI/INFORM Professional Advanced ABI/INFORM Global ProQuest Health & Medical Collection Medical Database Science Database Algology Mycology and Protozoology Abstracts (Microbiology C) Biotechnology and BioEngineering Abstracts Environmental Science Database ProQuest Central Premium ProQuest One Academic (New) ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) ProQuest One Health & Nursing ProQuest One Business ProQuest One Business (Alumni) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Academic (retired) ProQuest One Academic UKI Edition Environmental Science Collection ProQuest Central Basic MEDLINE - Academic AGRICOLA AGRICOLA - Academic |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) ProQuest Business Collection (Alumni Edition) ProQuest Central Student ProQuest Central Essentials SciTech Premium Collection ABI/INFORM Complete Environmental Sciences and Pollution Management ProQuest One Sustainability Health Research Premium Collection Natural Science Collection Health & Medical Research Collection Industrial and Applied Microbiology Abstracts (Microbiology A) ProQuest Central (New) ProQuest Medical Library (Alumni) Business Premium Collection ABI/INFORM Global ProQuest Science Journals (Alumni Edition) ProQuest One Academic Eastern Edition ProQuest Hospital Collection Health Research Premium Collection (Alumni) ProQuest Business Collection Ecology Abstracts ProQuest Hospital Collection (Alumni) Biotechnology and BioEngineering Abstracts Environmental Science Collection ProQuest Health & Medical Complete ProQuest One Academic UKI Edition Environmental Science Database Engineering Research Database ProQuest One Academic ProQuest One Academic (New) ABI/INFORM Global (Corporate) ProQuest One Business Technology Research Database ProQuest One Academic Middle East (New) ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) ProQuest One Community College ProQuest One Health & Nursing Pollution Abstracts ProQuest Pharma Collection ProQuest Central ABI/INFORM Professional Advanced ProQuest Health & Medical Research Collection Health and Medicine Complete (Alumni Edition) ProQuest Central Korea Bacteriology Abstracts (Microbiology B) Algology Mycology and Protozoology Abstracts (Microbiology C) Agricultural & Environmental Science Collection ABI/INFORM Complete (Alumni Edition) ProQuest Public Health ABI/INFORM Global (Alumni Edition) ProQuest Central Basic Toxicology Abstracts ProQuest Science Journals ProQuest Medical Library ProQuest One Business (Alumni) ProQuest Central (Alumni) Business Premium Collection (Alumni) MEDLINE - Academic AGRICOLA AGRICOLA - Academic |
| DatabaseTitleList | MEDLINE - Academic AGRICOLA MEDLINE ProQuest Business Collection (Alumni Edition) |
| Database_xml | – sequence: 1 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: BENPR name: ProQuest Central url: https://www.proquest.com/central sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Environmental Sciences Public Health |
| EISSN | 1614-7499 |
| EndPage | 5125 |
| ExternalDocumentID | 35974279 10_1007_s11356_022_22454_6 |
| Genre | Journal Article |
| GeographicLocations | China |
| GeographicLocations_xml | – name: China |
| GrantInformation_xml | – fundername: National Natural Science Foundation of China grantid: No. 71874134 funderid: http://dx.doi.org/10.13039/501100001809 – fundername: National Natural Science Foundation of China grantid: No. 71874134 |
| GroupedDBID | --- -5A -5G -5~ -BR -EM -Y2 -~C .VR 06D 0R~ 0VY 199 1N0 2.D 203 29G 2J2 2JN 2JY 2KG 2KM 2LR 2P1 2VQ 2~H 30V 3V. 4.4 406 408 409 40D 40E 4P2 53G 5GY 5VS 67M 67Z 6NX 78A 7WY 7X7 7XC 88E 88I 8AO 8C1 8FE 8FH 8FI 8FJ 8FL 8TC 8UJ 95- 95. 95~ 96X AAAVM AABHQ AACDK AAHBH AAHNG AAIAL AAJBT AAJKR AANZL AARHV AARTL AASML AATNV AATVU AAUYE AAWCG AAYIU AAYQN AAYTO AAYZH ABAKF ABBBX ABBXA ABDZT ABECU ABFTV ABHLI ABHQN ABJNI ABJOX ABKCH ABMNI ABMQK ABNWP ABQBU ABQSL ABSXP ABTEG ABTHY ABTKH ABTMW ABULA ABUWG ABWNU ABXPI ACAOD ACBXY ACDTI ACGFO ACGFS ACGOD ACHSB ACHXU ACKNC ACMDZ ACMLO ACOKC ACOMO ACPIV ACPRK ACREN ACSNA ACSVP ACZOJ ADBBV ADHHG ADHIR ADINQ ADKNI ADKPE ADRFC ADTPH ADURQ ADYFF ADYOE ADZKW AEBTG AEFQL AEGAL AEGNC AEJHL AEJRE AEKMD AEMSY AENEX AEOHA AEPYU AESKC AETLH AEUYN AEVLU AEXYK AFBBN AFGCZ AFKRA AFLOW AFQWF AFRAH AFWTZ AFYQB AFZKB AGAYW AGDGC AGGDS AGJBK AGMZJ AGQEE AGQMX AGRTI AGWIL AGWZB AGYKE AHAVH AHBYD AHKAY AHMBA AHSBF AHYZX AIAKS AIGIU AIIXL AILAN AITGF AJBLW AJRNO AJZVZ ALMA_UNASSIGNED_HOLDINGS ALWAN AMKLP AMTXH AMXSW AMYLF AMYQR AOCGG ARMRJ ASPBG ATCPS AVWKF AXYYD AYJHY AZFZN AZQEC B-. BA0 BBWZM BDATZ BENPR BEZIV BGNMA BHPHI BPHCQ BSONS BVXVI CAG CCPQU COF CS3 CSCUP DDRTE DL5 DNIVK DPUIP DU5 DWQXO EBD EBLON EBS EDH EIOEI EJD ESBYG F5P FEDTE FERAY FFXSO FIGPU FINBP FNLPD FRNLG FRRFC FSGXE FWDCC FYUFA GGCAI GGRSB GJIRD GNUQQ GNWQR GQ6 GQ7 GQ8 GROUPED_ABI_INFORM_COMPLETE GXS H13 HCIFZ HF~ HG5 HG6 HMCUK HMJXF HQYDN HRMNR HVGLF HZ~ IJ- IKXTQ IWAJR IXC IXD IXE IZIGR IZQ I~X I~Y I~Z J-C J0Z JBSCW JCJTX JZLTJ K60 K6~ KDC KOV L8X LAS LLZTM M0C M1P M2P M4Y MA- ML. N2Q N9A NB0 NDZJH NF0 NPVJJ NQJWS NU0 O9- O93 O9G O9I O9J OAM P19 P2P PATMY PF0 PQBIZ PQBZA PQQKQ PROAC PSQYO PT4 PT5 PYCSY Q2X QOK QOS R89 R9I RHV RNI RNS ROL RSV RZK S16 S1Z S26 S27 S28 S3B SAP SCK SCLPG SDH SEV SHX SISQX SJYHP SNE SNPRN SNX SOHCF SOJ SPISZ SRMVM SSLCW STPWE SZN T13 T16 TSG TSK TSV TUC TUS U2A U9L UG4 UKHRP UOJIU UTJUX UZXMN VC2 VFIZW W23 W48 WK6 WK8 Y6R YLTOR Z45 Z5O Z7R Z7U Z7V Z7W Z7X Z7Y Z7Z Z81 Z83 Z85 Z86 Z87 Z8P Z8Q Z8S ZMTXR ~02 ~KM AAPKM AAYXX ABBRH ABDBE ABFSG ABRTQ ACSTC ADHKG AEZWR AFDZB AFFHD AFHIU AFOHR AGQPQ AHPBZ AHWEU AIXLP ATHPR AYFIA BANNL CITATION PHGZM PHGZT PJZUB PPXIY CGR CUY CVF ECM EIF NPM 7QL 7SN 7T7 7TV 7U7 7XB 8FD 8FK C1K FR3 K9. L.- M7N P64 PKEHL PQEST PQUKI Q9U 7X8 PUEGO 7S9 L.6 |
| ID | FETCH-LOGICAL-c338t-989a90f6519b38cf24abe3f97ea13ffe337767f3f11d11c42a3b2a4ad92c658b3 |
| IEDL.DBID | M2P |
| ISICitedReferencesCount | 16 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000841108800003&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0944-1344 1614-7499 |
| IngestDate | Thu Oct 02 05:26:59 EDT 2025 Fri Sep 05 08:21:02 EDT 2025 Tue Dec 02 16:06:59 EST 2025 Mon Jul 21 06:03:11 EDT 2025 Tue Nov 18 21:42:58 EST 2025 Sat Nov 29 03:49:40 EST 2025 Fri Feb 21 02:44:14 EST 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 2 |
| Keywords | Sparrow search algorithm (SSA) Differential privacy laplace mechanism (DPLA) Air pollutant concentration prediction; Federated learning (FL) LSTM neural network Joint prevention and control |
| Language | English |
| License | 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c338t-989a90f6519b38cf24abe3f97ea13ffe337767f3f11d11c42a3b2a4ad92c658b3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| ORCID | 0000-0003-3910-9014 |
| PMID | 35974279 |
| PQID | 2771489469 |
| PQPubID | 54208 |
| PageCount | 23 |
| ParticipantIDs | proquest_miscellaneous_2887630071 proquest_miscellaneous_2703419039 proquest_journals_2771489469 pubmed_primary_35974279 crossref_primary_10_1007_s11356_022_22454_6 crossref_citationtrail_10_1007_s11356_022_22454_6 springer_journals_10_1007_s11356_022_22454_6 |
| PublicationCentury | 2000 |
| PublicationDate | 20230100 2023-01-00 2023-Jan 20230101 |
| PublicationDateYYYYMMDD | 2023-01-01 |
| PublicationDate_xml | – month: 1 year: 2023 text: 20230100 |
| PublicationDecade | 2020 |
| PublicationPlace | Berlin/Heidelberg |
| PublicationPlace_xml | – name: Berlin/Heidelberg – name: Germany – name: Heidelberg |
| PublicationTitle | Environmental science and pollution research international |
| PublicationTitleAbbrev | Environ Sci Pollut Res |
| PublicationTitleAlternate | Environ Sci Pollut Res Int |
| PublicationYear | 2023 |
| Publisher | Springer Berlin Heidelberg Springer Nature B.V |
| Publisher_xml | – name: Springer Berlin Heidelberg – name: Springer Nature B.V |
| References | Li, Peng, Yao (CR24) 2017; 231 Deng, Guo, Liu (CR11) 2019; 5 Maharani, Murfi (CR28) 2019; 1192 Wu, Yao, Li (CR40) 2016; 184 Shi, Zhang, Xu (CR35) 2022; 291 CR37 CR13 Ketu, Mishra (CR18) 2021; 7 CR12 Hinton, Salakhutdinov (CR16) 2006; 313 CR32 CR31 Kong, Dong, Jia (CR21) 2019; 10 CR30 Kloog, Koutrakis, Coull (CR20) 2011; 45 Cheng, Liu, Xu (CR8) 2019; 682 Arsov, Zdravevski, Lameski (CR2) 2021; 21 Kong, Zhang, Lv (CR22) 2020; 8 Govender, Sivakumar (CR15) 2020; 11 Liu, Yue, Xie (CR25) 2022; 300 Sethi, Mittal (CR34) 2021; 14 McMahan, Moore, Ramage (CR29) 2017; 54 Chen, Jin, Chai (CR5) 2021; 41 CR6 Braithwaite, Zhang, Kirkbride (CR4) 2019; 127 Wang, Li, Ho (CR38) 2021; 34 Liu, Yu, Sun (CR26) 2020; 201 Kim, Kim (CR19) 2019; 14 Zhang, Liu, Zhao (CR42) 2021; 12 Franceschi, Cobo, Figueredo (CR14) 2018; 9 Baker, Foley (CR3) 2011; 45 Hiwale, Phanasalkar, Kotecha (CR17) 2021; 40 Alomari, Katib, Albeshri (CR1) 2021; 21 Dai, Huang, Zeng (CR10) 2021; 13 Li, Shao, Sun (CR23) 2019; 2019 Ma, Yu, Qu (CR27) 2020; 20 Wang, Qiao, Zhang (CR39) 2020; 254 Xue, Shen (CR41) 2020; 8 Zhang, Zheng, Zhang (CR43) 2020; 36 Stan, Marmureanu, Marin (CR36) 2020; 545 Ragab, Abdulkadir, Aziz (CR33) 2020; 12 Chen (CR7) 2021; 651 Dai, Huang, Wang (CR9) 2021; 12 GE Hinton (22454_CR16) 2006; 313 JH Ma (22454_CR27) 2020; 20 D Maharani (22454_CR28) 2019; 1192 JK Sethi (22454_CR34) 2021; 14 22454_CR32 XH Cheng (22454_CR8) 2019; 682 X Li (22454_CR24) 2017; 231 22454_CR31 BH Chen (22454_CR5) 2021; 41 HB Dai (22454_CR9) 2021; 12 22454_CR30 22454_CR37 N Wang (22454_CR38) 2021; 34 PF Wang (22454_CR39) 2020; 254 22454_CR13 M Arsov (22454_CR2) 2021; 21 22454_CR12 ZM Kong (22454_CR22) 2020; 8 I Kloog (22454_CR20) 2011; 45 LK Shi (22454_CR35) 2022; 291 L Zhang (22454_CR42) 2021; 12 P Govender (22454_CR15) 2020; 11 Z Zhang (22454_CR43) 2020; 36 T Kim (22454_CR19) 2019; 14 F Franceschi (22454_CR14) 2018; 9 HW Liu (22454_CR25) 2022; 300 C Stan (22454_CR36) 2020; 545 S Ketu (22454_CR18) 2021; 7 JG Li (22454_CR23) 2019; 2019 MG Ragab (22454_CR33) 2020; 12 JS Wu (22454_CR40) 2016; 184 L Liu (22454_CR26) 2020; 201 22454_CR6 M Hiwale (22454_CR17) 2021; 40 WC Kong (22454_CR21) 2019; 10 I Braithwaite (22454_CR4) 2019; 127 HB Dai (22454_CR10) 2021; 13 JK Xue (22454_CR41) 2020; 8 KR Baker (22454_CR3) 2011; 45 Y Chen (22454_CR7) 2021; 651 W Deng (22454_CR11) 2019; 5 E Alomari (22454_CR1) 2021; 21 B McMahan (22454_CR29) 2017; 54 |
| References_xml | – volume: 682 start-page: 541 year: 2019 end-page: 552 ident: CR8 article-title: Lidar data assimilation method based on CRTM and WRF-Chem models and its application in PM forecasts in Beijing publication-title: Sci Total Environ doi: 10.1016/j.scitotenv.2019.05.186 – volume: 9 start-page: 912 issue: 5 year: 2018 end-page: 922 ident: CR14 article-title: Discovering relationships and forecasting PM and PM concentrations in Bogotá, Colombia, using artificial neural networks, principal component analysis, and k-means clustering publication-title: Atmos Pollut Res doi: 10.1016/j.apr.2018.02.006 – volume: 34 start-page: 106883 issue: 1 year: 2021 ident: CR38 article-title: Distributed machine learning for energy trading in electric distribution system of the future publication-title: Electr J doi: 10.1016/j.tej.2020.106883 – volume: 127 start-page: 126002 issue: 12 year: 2019 ident: CR4 article-title: Air pollution (particulate matter) exposure and associations with depression, anxiety, bipolar, psychosis and suicide risk: a systematic review and meta-analysis publication-title: Environ Health Persp doi: 10.1289/EHP4595 – volume: 7 start-page: 2597 issue: 5 year: 2021 end-page: 2615 ident: CR18 article-title: Scalable kernel-based SVM classification algorithm on imbalance air quality data for proficient healthcare publication-title: Complex Intell Syst doi: 10.1007/s40747-021-00435-5 – volume: 1192 start-page: 012010 issue: 1 year: 2019 ident: CR28 article-title: Deep neural network for structured data—a case study of mortality rate prediction caused by air quality publication-title: J Phys Conf Seri doi: 10.1088/1742-6596/1192/1/012010 – volume: 5 start-page: 33 issue: 4 year: 2019 end-page: 39 ident: CR11 article-title: A missing power data filling method based on improved random forest algorithm publication-title: Chin J Electric Eng doi: 10.23919/CJEE.2019.000025 – volume: 41 start-page: 817 year: 2021 end-page: 829 ident: CR5 article-title: Spatiotemporal distribution and correlation factors of PM concentrations in Zhejiang Province publication-title: Acta Sci Circumst – volume: 13 start-page: 12071 issue: 21 year: 2021 ident: CR10 article-title: PM Concentration prediction based on spatiotemporal feature selection using XGBoost-MSCNN-GA-LSTM publication-title: Sustainability doi: 10.3390/su132112071 – ident: CR37 – ident: CR12 – ident: CR30 – volume: 313 start-page: 504 issue: 5786 year: 2006 end-page: 507 ident: CR16 article-title: Reducing the dimensionality of data with neural networks publication-title: Science doi: 10.1126/science.1127647 – volume: 300 start-page: 118932 year: 2022 ident: CR25 article-title: Quantify the role of anthropogenic emission and meteorology on air pollution using machine learning approach: a case study of PM during the COVID-19 outbreak in Hubei Province, China publication-title: Environ Pollut doi: 10.1016/j.envpol.2022.118932 – volume: 12 start-page: 10090 issue: 23 year: 2020 ident: CR33 article-title: A novel one-dimensional CNN with exponential adaptive gradients for air pollution index prediction publication-title: Sustainability doi: 10.3390/su122310090 – volume: 8 start-page: 185373 year: 2020 end-page: 185383 ident: CR22 article-title: Multimodal feature extraction and fusion deep neural networks for short-term load forecasting publication-title: IEEE Access doi: 10.1109/ACCESS.2020.3029828 – volume: 14 start-page: 1777 issue: 4 year: 2021 end-page: 1786 ident: CR34 article-title: An efficient correlation based adaptive LASSO regression method for air quality index prediction publication-title: Earth Sci Inform doi: 10.1007/s12145-021-00618-1 – volume: 231 start-page: 997 year: 2017 end-page: 1004 ident: CR24 article-title: Long short-term memory neural network for air pollutant concentration predictions: Method development and evaluation publication-title: Environ Pollut doi: 10.1016/j.envpol.2017.08.114 – ident: CR6 – volume: 45 start-page: 6267 issue: 35 year: 2011 end-page: 6275 ident: CR20 article-title: Assessing temporally and spatially resolved PM exposures for epidemiological studies using satellite aerosol optical depth measurements publication-title: Atmos Environ doi: 10.1016/j.atmosenv.2011.08.066 – volume: 20 start-page: 128 issue: 1 year: 2020 end-page: 138 ident: CR27 article-title: Application of the XGBoost machine learning method in PM prediction: a case study of Shanghai publication-title: Aerosol Air Qual Res doi: 10.4209/aaqr.2019.08.0408 – volume: 12 start-page: 328 issue: 1 year: 2021 end-page: 339 ident: CR42 article-title: Air quality predictions with a semi-supervised bidirectional LSTM neural network publication-title: Atmos Pollut Res doi: 10.1016/j.apr.2020.09.003 – volume: 14 start-page: e0212320 issue: 2 year: 2019 ident: CR19 article-title: Forecasting stock prices with a feature fusion LSTM-CNN model using different representations of the same data publication-title: PLoS One doi: 10.1371/journal.pone.0212320 – volume: 12 start-page: 1626 issue: 12 year: 2021 ident: CR9 article-title: Prediction of air pollutant concentration based on one-dimensional multi-scale CNN-LSTM considering spatial-temporal characteristics: a case study of Xi’an, China publication-title: Atmosphere doi: 10.3390/atmos12121626 – volume: 291 start-page: 133124 year: 2022 ident: CR35 article-title: A balanced social LSTM for PM concentration prediction based on local spatiotemporal correlation publication-title: Chemosphere doi: 10.1016/j.chemosphere.2021.133124 – volume: 21 start-page: 2993 issue: 9 year: 2021 ident: CR1 article-title: Iktishaf+: a big data tool with automatic labeling for road traffic social sensing and event detection using distributed machine learning publication-title: Sensors doi: 10.3390/s21092993 – volume: 545 start-page: 123799 year: 2020 ident: CR36 article-title: Investigation of multifractal cross-correlation surfaces of Hurst exponents for some atmospheric pollutants publication-title: Physica A doi: 10.1016/j.physa.2019.123799 – volume: 184 start-page: 316 year: 2016 end-page: 328 ident: CR40 article-title: VIIRS-based remote sensing estimation of ground-level PM concentrations in Beijing–Tianjin–Hebei: a spatiotemporal statistical model publication-title: Remote Sens Environ doi: 10.1016/j.rse.2016.07.015 – volume: 54 start-page: 1273 year: 2017 end-page: 1282 ident: CR29 article-title: Communication-efficient learning of deep networks from decentralized data publication-title: Proc Int Conf Artif Intell Stat AISTATS – volume: 8 start-page: 22 issue: 1 year: 2020 end-page: 34 ident: CR41 article-title: A novel swarm intelligence optimization approach: sparrow search algorithm publication-title: Syst Sci Control Eng doi: 10.1080/21642583.2019.1708830 – volume: 201 start-page: 106002 year: 2020 ident: CR26 article-title: Online job scheduling for distributed machine learning in optical circuit switch networks publication-title: Knowl-Based Syst doi: 10.1016/j.knosys.2020.106002 – volume: 45 start-page: 3758 issue: 22 year: 2011 end-page: 3767 ident: CR3 article-title: A nonlinear regression model estimating single source concentrations of primary and secondarily formed PM publication-title: Atmos Environ doi: 10.1016/j.atmosenv.2011.03.074 – volume: 2019 start-page: 1 year: 2019 end-page: 9 ident: CR23 article-title: A DBN-based deep neural network model with multitask learning for online air quality prediction publication-title: J Control Sci Eng – volume: 254 start-page: 126735 year: 2020 ident: CR39 article-title: Modeling PM and O3 with aerosol feedbacks using WRF/Chem over the Sichuan Basin, southwestern China publication-title: Chemosphere doi: 10.1016/j.chemosphere.2020.126735 – ident: CR31 – ident: CR13 – volume: 21 start-page: 1235 issue: 4 year: 2021 ident: CR2 article-title: Multi-horizon air pollution forecasting with deep neural networks publication-title: Sensors doi: 10.3390/s21041235 – ident: CR32 – volume: 36 start-page: 96 year: 2020 end-page: 103 ident: CR43 article-title: The survey and influence factors of air pollution in Ningbo publication-title: Environ Monit China – volume: 11 start-page: 40 issue: 1 year: 2020 end-page: 56 ident: CR15 article-title: Application of k-means and hierarchical clustering techniques for analysis of air pollution: a review (1980–2019) publication-title: Atmos Pollut Res doi: 10.1016/j.apr.2019.09.009 – volume: 10 start-page: 841 issue: 1 year: 2019 end-page: 851 ident: CR21 article-title: Short-term residential load forecasting based on LSTM recurrent neural network publication-title: IEEE T Smart Grid doi: 10.1109/TSG.2017.2753802 – volume: 651 start-page: 042068 issue: 4 year: 2021 ident: CR7 article-title: Air pollution analysis based on PCA and entropy weight method publication-title: IOP Conf Ser Earth Environ Sci doi: 10.1088/1755-1315/651/4/042068 – volume: 40 start-page: 190 issue: 2 year: 2021 end-page: 213 ident: CR17 article-title: Using blockchain and distributed machine learning to manage decentralized but trustworthy disease data publication-title: Sci Technol Libr doi: 10.1080/0194262X.2020.1859046 – volume: 10 start-page: 841 issue: 1 year: 2019 ident: 22454_CR21 publication-title: IEEE T Smart Grid doi: 10.1109/TSG.2017.2753802 – volume: 254 start-page: 126735 year: 2020 ident: 22454_CR39 publication-title: Chemosphere doi: 10.1016/j.chemosphere.2020.126735 – volume: 21 start-page: 2993 issue: 9 year: 2021 ident: 22454_CR1 publication-title: Sensors doi: 10.3390/s21092993 – volume: 21 start-page: 1235 issue: 4 year: 2021 ident: 22454_CR2 publication-title: Sensors doi: 10.3390/s21041235 – volume: 11 start-page: 40 issue: 1 year: 2020 ident: 22454_CR15 publication-title: Atmos Pollut Res doi: 10.1016/j.apr.2019.09.009 – volume: 7 start-page: 2597 issue: 5 year: 2021 ident: 22454_CR18 publication-title: Complex Intell Syst doi: 10.1007/s40747-021-00435-5 – volume: 14 start-page: 1777 issue: 4 year: 2021 ident: 22454_CR34 publication-title: Earth Sci Inform doi: 10.1007/s12145-021-00618-1 – volume: 5 start-page: 33 issue: 4 year: 2019 ident: 22454_CR11 publication-title: Chin J Electric Eng doi: 10.23919/CJEE.2019.000025 – ident: 22454_CR31 – ident: 22454_CR12 – volume: 9 start-page: 912 issue: 5 year: 2018 ident: 22454_CR14 publication-title: Atmos Pollut Res doi: 10.1016/j.apr.2018.02.006 – volume: 14 start-page: e0212320 issue: 2 year: 2019 ident: 22454_CR19 publication-title: PLoS One doi: 10.1371/journal.pone.0212320 – volume: 45 start-page: 3758 issue: 22 year: 2011 ident: 22454_CR3 publication-title: Atmos Environ doi: 10.1016/j.atmosenv.2011.03.074 – ident: 22454_CR37 – volume: 34 start-page: 106883 issue: 1 year: 2021 ident: 22454_CR38 publication-title: Electr J doi: 10.1016/j.tej.2020.106883 – volume: 8 start-page: 185373 year: 2020 ident: 22454_CR22 publication-title: IEEE Access doi: 10.1109/ACCESS.2020.3029828 – ident: 22454_CR13 doi: 10.5194/isprs-annals-IV-4-W2-15-2017 – volume: 184 start-page: 316 year: 2016 ident: 22454_CR40 publication-title: Remote Sens Environ doi: 10.1016/j.rse.2016.07.015 – ident: 22454_CR30 doi: 10.1109/IJCNN.1992.227046 – volume: 2019 start-page: 1 year: 2019 ident: 22454_CR23 publication-title: J Control Sci Eng – volume: 13 start-page: 12071 issue: 21 year: 2021 ident: 22454_CR10 publication-title: Sustainability doi: 10.3390/su132112071 – volume: 231 start-page: 997 year: 2017 ident: 22454_CR24 publication-title: Environ Pollut doi: 10.1016/j.envpol.2017.08.114 – volume: 12 start-page: 328 issue: 1 year: 2021 ident: 22454_CR42 publication-title: Atmos Pollut Res doi: 10.1016/j.apr.2020.09.003 – ident: 22454_CR32 – volume: 682 start-page: 541 year: 2019 ident: 22454_CR8 publication-title: Sci Total Environ doi: 10.1016/j.scitotenv.2019.05.186 – volume: 40 start-page: 190 issue: 2 year: 2021 ident: 22454_CR17 publication-title: Sci Technol Libr doi: 10.1080/0194262X.2020.1859046 – volume: 651 start-page: 042068 issue: 4 year: 2021 ident: 22454_CR7 publication-title: IOP Conf Ser Earth Environ Sci doi: 10.1088/1755-1315/651/4/042068 – volume: 12 start-page: 10090 issue: 23 year: 2020 ident: 22454_CR33 publication-title: Sustainability doi: 10.3390/su122310090 – volume: 45 start-page: 6267 issue: 35 year: 2011 ident: 22454_CR20 publication-title: Atmos Environ doi: 10.1016/j.atmosenv.2011.08.066 – volume: 545 start-page: 123799 year: 2020 ident: 22454_CR36 publication-title: Physica A doi: 10.1016/j.physa.2019.123799 – volume: 8 start-page: 22 issue: 1 year: 2020 ident: 22454_CR41 publication-title: Syst Sci Control Eng doi: 10.1080/21642583.2019.1708830 – volume: 300 start-page: 118932 year: 2022 ident: 22454_CR25 publication-title: Environ Pollut doi: 10.1016/j.envpol.2022.118932 – volume: 201 start-page: 106002 year: 2020 ident: 22454_CR26 publication-title: Knowl-Based Syst doi: 10.1016/j.knosys.2020.106002 – volume: 41 start-page: 817 year: 2021 ident: 22454_CR5 publication-title: Acta Sci Circumst – volume: 20 start-page: 128 issue: 1 year: 2020 ident: 22454_CR27 publication-title: Aerosol Air Qual Res doi: 10.4209/aaqr.2019.08.0408 – ident: 22454_CR6 doi: 10.1145/2971648.2971725 – volume: 54 start-page: 1273 year: 2017 ident: 22454_CR29 publication-title: Proc Int Conf Artif Intell Stat AISTATS – volume: 12 start-page: 1626 issue: 12 year: 2021 ident: 22454_CR9 publication-title: Atmosphere doi: 10.3390/atmos12121626 – volume: 127 start-page: 126002 issue: 12 year: 2019 ident: 22454_CR4 publication-title: Environ Health Persp doi: 10.1289/EHP4595 – volume: 313 start-page: 504 issue: 5786 year: 2006 ident: 22454_CR16 publication-title: Science doi: 10.1126/science.1127647 – volume: 36 start-page: 96 year: 2020 ident: 22454_CR43 publication-title: Environ Monit China – volume: 1192 start-page: 012010 issue: 1 year: 2019 ident: 22454_CR28 publication-title: J Phys Conf Seri doi: 10.1088/1742-6596/1192/1/012010 – volume: 291 start-page: 133124 year: 2022 ident: 22454_CR35 publication-title: Chemosphere doi: 10.1016/j.chemosphere.2021.133124 |
| SSID | ssj0020927 |
| Score | 2.42991 |
| Snippet | As air pollution worsens, the fast prediction of air pollutant concentration becomes increasingly important for public health. This paper proposes a new... |
| SourceID | proquest pubmed crossref springer |
| SourceType | Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 5103 |
| SubjectTerms | air air pollutants Air Pollutants - analysis Air pollution Air Pollution - analysis Algorithms Aquatic Pollution Atmospheric Protection/Air Quality Control/Air Pollution China data collection Domains Earth and Environmental Science Ecotoxicology Environment Environmental Chemistry Environmental Health Environmental Monitoring - methods Environmental protection Environmental science Federated learning Learning Machine learning Mathematical models memory Meteorological data Neural networks Neural Networks, Computer Outdoor air quality Parameters Passeriformes Pollutants Pollution abatement pollution control Pollution prevention prediction Prediction models Public health Research Article Rivers Search algorithms Short term memory Waste Water Technology Water Management Water Pollution Control |
| SummonAdditionalLinks | – databaseName: SpringerLINK Contemporary 1997-Present dbid: RSV link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1LT9wwEB4VyoFLXzy6La2M1BtYimPn4SOqQD1QVAGtuEWOH2glNrvaLBz4A_3bnUmcXVUUJHqLkkk08mu-iT3fB_CF4orLXODBu5wrbxNuSoeLoTXS5FmGgK5TLTktzs7Kqyv9IxaFtcNp92FLslupV8VuQmZ0YDblGHYyxfM1eInhriTBhvOLX8s0K9G9UKtWigupVCyV-fc3_g5HDzDmg_3RLuycvP4_h9_Aqwgz2VE_Lt7CC9-8g53jVVUbPozTut2C30cM0TXrXORuOjHjhs3mtIVD3cY6tRw2DcyM52xG2sgkPcwsVTw2kXaXUTh0DC9a-oPvWSCWCgSyjkVhimtmGjTAJWoyvsfbpxeX3xnRaaIrTX8YfRt-nhxffv3Go0IDt5jaLrgutdFJyBEG1rK0IVWm9jLowhshQ_BSEllQkEEIJ4RVqZF1apRxOrUIfWq5A-vNtPHvgVmEqoIKzw1maLag0j_MFX0ZkqSWWV2PQAwdVdlIX04qGjfViniZ2rvC9q669q7yERws35n15B1PWu8N_V_FidxWaVFgwqhVrkewv3yMU5D2VUzjp7dkkxArXiKfsimJ-o8A3Qh2-7G1dElSUpcW-PbhMJBWDjzu74fnmX-EzRQBWv_7aA_WF_Nb_wk27N1i3M4_d1PoD73CFYM priority: 102 providerName: Springer Nature |
| Title | A new cross-domain prediction model of air pollutant concentration based on secure federated learning and optimized LSTM neural network |
| URI | https://link.springer.com/article/10.1007/s11356-022-22454-6 https://www.ncbi.nlm.nih.gov/pubmed/35974279 https://www.proquest.com/docview/2771489469 https://www.proquest.com/docview/2703419039 https://www.proquest.com/docview/2887630071 |
| Volume | 30 |
| WOSCitedRecordID | wos000841108800003&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVPQU databaseName: ABI/INFORM Collection customDbUrl: eissn: 1614-7499 dateEnd: 20241212 omitProxy: false ssIdentifier: ssj0020927 issn: 0944-1344 databaseCode: 7WY dateStart: 20190101 isFulltext: true titleUrlDefault: https://www.proquest.com/abicomplete providerName: ProQuest – providerCode: PRVPQU databaseName: ABI/INFORM Global customDbUrl: eissn: 1614-7499 dateEnd: 20241212 omitProxy: false ssIdentifier: ssj0020927 issn: 0944-1344 databaseCode: M0C dateStart: 20190101 isFulltext: true titleUrlDefault: https://search.proquest.com/abiglobal providerName: ProQuest – providerCode: PRVPQU databaseName: Environmental Science Database customDbUrl: eissn: 1614-7499 dateEnd: 20241212 omitProxy: false ssIdentifier: ssj0020927 issn: 0944-1344 databaseCode: PATMY dateStart: 20190101 isFulltext: true titleUrlDefault: http://search.proquest.com/environmentalscience providerName: ProQuest – providerCode: PRVPQU databaseName: Health & Medical Collection customDbUrl: eissn: 1614-7499 dateEnd: 20241212 omitProxy: false ssIdentifier: ssj0020927 issn: 0944-1344 databaseCode: 7X7 dateStart: 20190101 isFulltext: true titleUrlDefault: https://search.proquest.com/healthcomplete providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 1614-7499 dateEnd: 20241212 omitProxy: false ssIdentifier: ssj0020927 issn: 0944-1344 databaseCode: BENPR dateStart: 20190101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Public Health Database customDbUrl: eissn: 1614-7499 dateEnd: 20241212 omitProxy: false ssIdentifier: ssj0020927 issn: 0944-1344 databaseCode: 8C1 dateStart: 20190101 isFulltext: true titleUrlDefault: https://search.proquest.com/publichealth providerName: ProQuest – providerCode: PRVPQU databaseName: Science Database customDbUrl: eissn: 1614-7499 dateEnd: 20241212 omitProxy: false ssIdentifier: ssj0020927 issn: 0944-1344 databaseCode: M2P dateStart: 20190101 isFulltext: true titleUrlDefault: https://search.proquest.com/sciencejournals providerName: ProQuest – providerCode: PRVAVX databaseName: SpringerLINK Contemporary 1997-Present customDbUrl: eissn: 1614-7499 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0020927 issn: 0944-1344 databaseCode: RSV dateStart: 19970101 isFulltext: true titleUrlDefault: https://link.springer.com/search?facet-content-type=%22Journal%22 providerName: Springer Nature |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1LbxMxEB7RlgMS4lEopJTISNzAYr3e56lqo1QcmihqC4TTyutHFYnuhmzKgT_A32Zm10lUVeTCxdpde6WRxvZ8M_bMB_Ce7IqJjePOmoRHVgdcZQY3Q62kSuIYAV3LWnKejsfZdJpPfMCt8dcqV3tiu1GbWlOM_FOYpojcc_Tmjuc_ObFG0emqp9DYgT1ENoKudI3CydrhCvKOsjWPIi5kFPmkmS51TsiYrt-GHI1YHPHkrmG6hzbvnZS2Bujs6f-K_gyeeOjJTrq58hwe2GofDoabTDfs9Eu92YfHXUCPdXlKL-DPCUMEzlrhualv1Kxi8wUd85BqWcuow2rH1GzB5sSfTPTETFNWZOVL8zIymYbhQ0NRfsscVbJAsGuYJ6-4ZqrCAbiN3cx-4-fzy6sRo5KbKFrVXVh_CV_OhleDz9yzOHCN7u-S51mu8sAlCBVLmWkXRqq00uWpVUI6Z6WkgkJOOiGMEDoKlSxDFSmThxrhUSkPYLeqK_samEY4Kyg5XaEXp1NKD0R_0mYuCEoZl2UPxEqFhfYlzolp40exKc5Mai9Q7UWr9iLpwYf1P_OuwMfW0UcrFRd-sTfFRr89eLfuxmVKZy-qsvUtjQmocl4gt43JqDwggb4evOpm3VokSY5fmOLfH1fTcCPAv-U93C7vG3gUImjrQkpHsLtc3Nq38FD_Ws6aRR920m_fqZ2mbZthmw1EH_ZOh-PJBb6NgkG_XXLYXlx-_Qtjayvj |
| linkProvider | ProQuest |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1baxNBFD7UKlgQL9VqtOoI-qSDuzN7fRAp2tLSNBSM0Lft7Fwk0O7GbKroH_Df-Bs9Z2c3QYp564NvITsbhsk35zJnzvcBvCS_YmLjuLMm4ZHVAVeZQWOolVRJHGNA16qWDNPRKDs5yY_X4HffC0PXKnub2BpqU2s6I38r0hQj9xyzuffTr5xUo6i62ktoeFgc2h_fMWVr3h18xP_3lRB7u-MP-7xTFeAa07E5z7Nc5YFLMHQpZaadiFRppctTq0LpnJWSCG6cdGFowlBHQslSqEiZXGh016XE370G1yNiFqOrguJ4keAFuZeIzaOIhzKKuiYd36oXypiu-wqOTjOOePK3I7wU3V6qzLYOb-_O_7ZUd-F2F1qzHb8X7sGarTZha3fZyYcPO1PWbMItf2DJfB_Wffi1wzDDYO1icVOfq0nFpjMqYxF0WasYxGrH1GTGpqQPTfLLTFPXZ9VRDzMKCQzDDw1VMSxzxNSBwbxhnTjHF6YqHIBm-nzyE78efhofMaIUxalV_kL-A_h8JYu0BetVXdlHwDSG6yE13yvMUnVK7Y-YL9vMBUEp47IcQNhDptAdhTspiZwVS_JpglmBMCtamBXJAF4v3pl6ApOVo7d7SBWdMWuKJZ4G8GLxGM0Q1ZZUZesLGhMQM2AgV43JiP6QgtoBPPQoX0xJUmIrUnz7TQ_75QT-Pd_Hq-f7HG7uj4-GxfBgdPgENgQGqP74bBvW57ML-xRu6G_zSTN71m5kBqdXvR3-AD9RgfA |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1baxNBFD7UKiKIl2prtOoI-qRDd2f2-iBS2gZLYyhYoW_r7FwkYHfTbKroH_A_-es8Z2c3QYp564NvITsbhsm5fGfO5QN4SX7FxMZxZ03CI6sDrjKDxlArqZI4RkDXspaM0vE4Oz3Nj9fgd98LQ2WVvU1sDbWpNd2R74g0ReSeYzS347qyiOP94bvpOScGKcq09nQaXkSO7I_vGL41bw_38b9-JcTw4GTvPe8YBrjG0GzO8yxXeeAShDGlzLQTkSqtdHlqVSids1LSsBsnXRiaMNSRULIUKlImFxpddynxd6_B9TRCt0llg8HeItgLck8Xm0cRD2UUdQ07vm0vlDGV_gqODjSOePK3U7yEdC9laVvnN7z7Px_bPbjTQW6263XkPqzZagM2D5YdfviwM3HNBtz2F5nM92c9gF-7DCMP1h4cN_WZmlRsOqP0Fok0a5mEWO2YmszYlHijiZaZaeoGrbqRxIyggmH4oaHshmWOJnggyDesI-34wlSFC9B8n01-4tejjycfGI0axa1VvlD_IXy6kkPahPWqruwjYBphfEhN-QqjV51SWyTG0TZzQVDKuCwHEPbiU-hutDsxjHwtlkOpSeQKFLmiFbkiGcDrxTtTP9hk5ertXryKzsg1xVK2BvBi8RjNE-WcVGXrC1oT0MTAQK5ak9FYRAK7A9jyEr_YkqSAV6T49pteBZYb-Pd-H6_e73O4iVpQjA7HR0_glkDc6m_VtmF9PruwT-GG_jafNLNnrU4z-HzV2vAHHKeKkg |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=A+new+cross-domain+prediction+model+of+air+pollutant+concentration+based+on+secure+federated+learning+and+optimized+LSTM+neural+network&rft.jtitle=Environmental+science+and+pollution+research+international&rft.au=Huang%2C+Guangqiu&rft.au=Zhao%2C+Xixuan&rft.au=Lu%2C+Qiuqin&rft.date=2023-01-01&rft.issn=0944-1344&rft.volume=30&rft.issue=2+p.5103-5125&rft.spage=5103&rft.epage=5125&rft_id=info:doi/10.1007%2Fs11356-022-22454-6&rft.externalDBID=NO_FULL_TEXT |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0944-1344&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0944-1344&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0944-1344&client=summon |