Optimizing BenMAP health impact assessment with meteorological factor driven machine learning models
This study aims to address accuracy challenges in assessing air pollution health impacts using Environmental Benefits Mapping and Analysis Program (BenMap), caused by limited meteorological factor data and missing pollutant data. By employing data increment strategies and multiple machine learning m...
Gespeichert in:
| Veröffentlicht in: | The Science of the total environment Jg. 949; S. 175246 |
|---|---|
| Hauptverfasser: | , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Netherlands
Elsevier B.V
01.11.2024
|
| Schlagworte: | |
| ISSN: | 0048-9697, 1879-1026, 1879-1026 |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | This study aims to address accuracy challenges in assessing air pollution health impacts using Environmental Benefits Mapping and Analysis Program (BenMap), caused by limited meteorological factor data and missing pollutant data. By employing data increment strategies and multiple machine learning models, this research explores the effects of data volume, time steps, and meteorological factors on model prediction performance using several years of data from Tianjin City as an example. The findings indicate that increasing training data volume enhances the performance of Random Forest Regressor (RF) and Decision Tree Regressor (DT) models, especially for predicting CO, NO2, and PM2.5. The optimal prediction time step varies by pollutant, with the DT model achieving the highest R2 value (0.99) for CO and O3. Combining multiple meteorological factors, such as atmospheric pressure, relative humidity, and dew point temperature, significantly improves model accuracy. When using three meteorological factors, the model achieves an R2 of 0.99 for predicting CO, NO2, PM10, PM2.5, and SO2. Health impact assessments using BenMap demonstrated that the predicted all-cause mortality and specific disease mortalities were highly consistent with actual values, confirming the model's accuracy in assessing health impacts from air pollution. For instance, the predicted and actual all-cause mortality for PM2.5 were both 3120; for cardiovascular disease, both were 1560; and for respiratory disease, both were 780. To validate its generalizability, this method was applied to Chengdu, China, using several years of data for training and prediction of PM2.5, CO, NO2, O3, PM10, and SO2, incorporating atmospheric pressure, relative humidity, and dew point temperature. The model maintained excellent performance, confirming its broad applicability. Overall, we conclude that the machine learning and BenMap-based methods show high accuracy and reliability in predicting air pollutant concentrations and health impacts, providing a valuable reference for air pollution assessment.
[Display omitted]
•Utilize BenMAP and ML to improve air pollution assessment.•Find optimal prediction steps and key meteorological factors.•High accuracy in CO and O3 prediction with Decision Tree.•Demonstrated the generalizability of the method for application to different cities. |
|---|---|
| AbstractList | This study aims to address accuracy challenges in assessing air pollution health impacts using Environmental Benefits Mapping and Analysis Program (BenMap), caused by limited meteorological factor data and missing pollutant data. By employing data increment strategies and multiple machine learning models, this research explores the effects of data volume, time steps, and meteorological factors on model prediction performance using several years of data from Tianjin City as an example. The findings indicate that increasing training data volume enhances the performance of Random Forest Regressor (RF) and Decision Tree Regressor (DT) models, especially for predicting CO, NO
, and PM
. The optimal prediction time step varies by pollutant, with the DT model achieving the highest R
value (0.99) for CO and O
. Combining multiple meteorological factors, such as atmospheric pressure, relative humidity, and dew point temperature, significantly improves model accuracy. When using three meteorological factors, the model achieves an R
of 0.99 for predicting CO, NO
, PM
, PM
, and SO
. Health impact assessments using BenMap demonstrated that the predicted all-cause mortality and specific disease mortalities were highly consistent with actual values, confirming the model's accuracy in assessing health impacts from air pollution. For instance, the predicted and actual all-cause mortality for PM2.5 were both 3120; for cardiovascular disease, both were 1560; and for respiratory disease, both were 780. To validate its generalizability, this method was applied to Chengdu, China, using several years of data for training and prediction of PM
, CO, NO
, O
, PM
, and SO
, incorporating atmospheric pressure, relative humidity, and dew point temperature. The model maintained excellent performance, confirming its broad applicability. Overall, we conclude that the machine learning and BenMap-based methods show high accuracy and reliability in predicting air pollutant concentrations and health impacts, providing a valuable reference for air pollution assessment. This study aims to address accuracy challenges in assessing air pollution health impacts using Environmental Benefits Mapping and Analysis Program (BenMap), caused by limited meteorological factor data and missing pollutant data. By employing data increment strategies and multiple machine learning models, this research explores the effects of data volume, time steps, and meteorological factors on model prediction performance using several years of data from Tianjin City as an example. The findings indicate that increasing training data volume enhances the performance of Random Forest Regressor (RF) and Decision Tree Regressor (DT) models, especially for predicting CO, NO₂, and PM₂.₅. The optimal prediction time step varies by pollutant, with the DT model achieving the highest R² value (0.99) for CO and O₃. Combining multiple meteorological factors, such as atmospheric pressure, relative humidity, and dew point temperature, significantly improves model accuracy. When using three meteorological factors, the model achieves an R² of 0.99 for predicting CO, NO₂, PM₁₀, PM₂.₅, and SO₂. Health impact assessments using BenMap demonstrated that the predicted all-cause mortality and specific disease mortalities were highly consistent with actual values, confirming the model's accuracy in assessing health impacts from air pollution. For instance, the predicted and actual all-cause mortality for PM2.5 were both 3120; for cardiovascular disease, both were 1560; and for respiratory disease, both were 780. To validate its generalizability, this method was applied to Chengdu, China, using several years of data for training and prediction of PM₂.₅, CO, NO₂, O₃, PM₁₀, and SO₂, incorporating atmospheric pressure, relative humidity, and dew point temperature. The model maintained excellent performance, confirming its broad applicability. Overall, we conclude that the machine learning and BenMap-based methods show high accuracy and reliability in predicting air pollutant concentrations and health impacts, providing a valuable reference for air pollution assessment. This study aims to address accuracy challenges in assessing air pollution health impacts using Environmental Benefits Mapping and Analysis Program (BenMap), caused by limited meteorological factor data and missing pollutant data. By employing data increment strategies and multiple machine learning models, this research explores the effects of data volume, time steps, and meteorological factors on model prediction performance using several years of data from Tianjin City as an example. The findings indicate that increasing training data volume enhances the performance of Random Forest Regressor (RF) and Decision Tree Regressor (DT) models, especially for predicting CO, NO2, and PM2.5. The optimal prediction time step varies by pollutant, with the DT model achieving the highest R2 value (0.99) for CO and O3. Combining multiple meteorological factors, such as atmospheric pressure, relative humidity, and dew point temperature, significantly improves model accuracy. When using three meteorological factors, the model achieves an R2 of 0.99 for predicting CO, NO2, PM10, PM2.5, and SO2. Health impact assessments using BenMap demonstrated that the predicted all-cause mortality and specific disease mortalities were highly consistent with actual values, confirming the model's accuracy in assessing health impacts from air pollution. For instance, the predicted and actual all-cause mortality for PM2.5 were both 3120; for cardiovascular disease, both were 1560; and for respiratory disease, both were 780. To validate its generalizability, this method was applied to Chengdu, China, using several years of data for training and prediction of PM2.5, CO, NO2, O3, PM10, and SO2, incorporating atmospheric pressure, relative humidity, and dew point temperature. The model maintained excellent performance, confirming its broad applicability. Overall, we conclude that the machine learning and BenMap-based methods show high accuracy and reliability in predicting air pollutant concentrations and health impacts, providing a valuable reference for air pollution assessment.This study aims to address accuracy challenges in assessing air pollution health impacts using Environmental Benefits Mapping and Analysis Program (BenMap), caused by limited meteorological factor data and missing pollutant data. By employing data increment strategies and multiple machine learning models, this research explores the effects of data volume, time steps, and meteorological factors on model prediction performance using several years of data from Tianjin City as an example. The findings indicate that increasing training data volume enhances the performance of Random Forest Regressor (RF) and Decision Tree Regressor (DT) models, especially for predicting CO, NO2, and PM2.5. The optimal prediction time step varies by pollutant, with the DT model achieving the highest R2 value (0.99) for CO and O3. Combining multiple meteorological factors, such as atmospheric pressure, relative humidity, and dew point temperature, significantly improves model accuracy. When using three meteorological factors, the model achieves an R2 of 0.99 for predicting CO, NO2, PM10, PM2.5, and SO2. Health impact assessments using BenMap demonstrated that the predicted all-cause mortality and specific disease mortalities were highly consistent with actual values, confirming the model's accuracy in assessing health impacts from air pollution. For instance, the predicted and actual all-cause mortality for PM2.5 were both 3120; for cardiovascular disease, both were 1560; and for respiratory disease, both were 780. To validate its generalizability, this method was applied to Chengdu, China, using several years of data for training and prediction of PM2.5, CO, NO2, O3, PM10, and SO2, incorporating atmospheric pressure, relative humidity, and dew point temperature. The model maintained excellent performance, confirming its broad applicability. Overall, we conclude that the machine learning and BenMap-based methods show high accuracy and reliability in predicting air pollutant concentrations and health impacts, providing a valuable reference for air pollution assessment. This study aims to address accuracy challenges in assessing air pollution health impacts using Environmental Benefits Mapping and Analysis Program (BenMap), caused by limited meteorological factor data and missing pollutant data. By employing data increment strategies and multiple machine learning models, this research explores the effects of data volume, time steps, and meteorological factors on model prediction performance using several years of data from Tianjin City as an example. The findings indicate that increasing training data volume enhances the performance of Random Forest Regressor (RF) and Decision Tree Regressor (DT) models, especially for predicting CO, NO2, and PM2.5. The optimal prediction time step varies by pollutant, with the DT model achieving the highest R2 value (0.99) for CO and O3. Combining multiple meteorological factors, such as atmospheric pressure, relative humidity, and dew point temperature, significantly improves model accuracy. When using three meteorological factors, the model achieves an R2 of 0.99 for predicting CO, NO2, PM10, PM2.5, and SO2. Health impact assessments using BenMap demonstrated that the predicted all-cause mortality and specific disease mortalities were highly consistent with actual values, confirming the model's accuracy in assessing health impacts from air pollution. For instance, the predicted and actual all-cause mortality for PM2.5 were both 3120; for cardiovascular disease, both were 1560; and for respiratory disease, both were 780. To validate its generalizability, this method was applied to Chengdu, China, using several years of data for training and prediction of PM2.5, CO, NO2, O3, PM10, and SO2, incorporating atmospheric pressure, relative humidity, and dew point temperature. The model maintained excellent performance, confirming its broad applicability. Overall, we conclude that the machine learning and BenMap-based methods show high accuracy and reliability in predicting air pollutant concentrations and health impacts, providing a valuable reference for air pollution assessment. [Display omitted] •Utilize BenMAP and ML to improve air pollution assessment.•Find optimal prediction steps and key meteorological factors.•High accuracy in CO and O3 prediction with Decision Tree.•Demonstrated the generalizability of the method for application to different cities. |
| ArticleNumber | 175246 |
| Author | Dai, Qili Song, Shaojie Wu, Juncheng |
| Author_xml | – sequence: 1 givenname: Juncheng surname: Wu fullname: Wu, Juncheng – sequence: 2 givenname: Qili surname: Dai fullname: Dai, Qili – sequence: 3 givenname: Shaojie surname: Song fullname: Song, Shaojie email: songs@nankai.edu.cn |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/39098427$$D View this record in MEDLINE/PubMed |
| BookMark | eNqFkU1vEzEQhi1URNPCX4A9ctkw9tpr74FDqPiSisoBzpZjzzaO1nawnVTw69koLQcu8WUk63nfkea5IhcxRSTkDYUlBdq_2y6L9TVVjIclA8aXVArG-2dkQZUcWgqsvyALAK7aoR_kJbkqZQvzk4q-IJfdAIPiTC6Iu9tVH_wfH--bDxi_rb43GzRT3TQ-7IytjSkFSwkYa_Pg5--AFVNOU7r31kzNODMpNy77A8YmGLvxEZsJTY7HypAcTuUleT6aqeCrx3lNfn76-OPmS3t79_nrzeq2tRx4be3o1Fo6AIPMraEfjaEOzECVch23aAVjXPFOMm5hECAocmNY33cK1iOI7pq8PfXucvq1x1J18MXiNJmIaV90R0Unqeq5PI-CUkIIKtWMvn5E9-uATu-yDyb_1k9HnIH3J8DmVErGUc9uTPUp1mz8pCnoozS91f-k6aM0fZI25-V_-acV55OrU3I-Mh485iOH0aLzGW3VLvmzHX8BAki3sw |
| CitedBy_id | crossref_primary_10_1186_s12889_025_23111_6 |
| Cites_doi | 10.1016/j.scitotenv.2014.03.113 10.1016/j.atmosenv.2024.120393 10.1016/j.oceaneng.2024.117236 10.1016/j.chemosphere.2020.129369 10.1016/j.rineng.2024.101834 10.1016/j.chemosphere.2021.131285 10.1016/j.envpol.2016.11.080 10.1016/j.envsoft.2018.02.009 10.1016/j.jenvman.2017.08.018 10.1016/j.atmosenv.2020.117343 10.1016/j.envres.2023.117491 10.1016/j.renene.2023.119883 10.1016/j.envint.2022.107241 10.1016/j.apr.2024.102123 10.1016/j.jhazmat.2024.133944 10.1016/j.scitotenv.2024.170099 10.1016/j.envint.2023.107740 10.1016/j.envres.2023.116704 10.1016/j.atmosenv.2021.118209 10.1016/j.apr.2023.101932 10.1016/j.xinn.2023.100517 10.1016/j.agwat.2023.108416 10.1016/j.jhazmat.2023.133281 10.1016/j.envres.2022.113322 10.1016/j.heliyon.2023.e18450 10.1016/j.apr.2023.101777 10.1016/j.ecolind.2023.111233 10.1016/j.energy.2023.128446 |
| ContentType | Journal Article |
| Copyright | 2024 Elsevier B.V. Copyright © 2024 Elsevier B.V. All rights reserved. |
| Copyright_xml | – notice: 2024 Elsevier B.V. – notice: Copyright © 2024 Elsevier B.V. All rights reserved. |
| DBID | AAYXX CITATION NPM 7X8 7S9 L.6 |
| DOI | 10.1016/j.scitotenv.2024.175246 |
| DatabaseName | CrossRef PubMed MEDLINE - Academic AGRICOLA AGRICOLA - Academic |
| DatabaseTitle | CrossRef PubMed MEDLINE - Academic AGRICOLA AGRICOLA - Academic |
| DatabaseTitleList | PubMed AGRICOLA MEDLINE - Academic |
| 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: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Public Health Biology Environmental Sciences |
| EISSN | 1879-1026 |
| ExternalDocumentID | 39098427 10_1016_j_scitotenv_2024_175246 S0048969724053968 |
| Genre | Journal Article |
| GeographicLocations | China |
| GeographicLocations_xml | – name: China |
| GroupedDBID | --- --K --M .~1 0R~ 1B1 1RT 1~. 1~5 4.4 457 4G. 5VS 7-5 71M 8P~ 9JM AABNK AACTN AAEDT AAEDW AAHBH AAIKJ AAKOC AALRI AAOAW AAQFI AATTM AAXKI AAXUO ABFNM ABFYP ABJNI ABLST ABMAC ACDAQ ACGFS ACRLP ADBBV ADEZE AEBSH AEIPS AEKER AENEX AFJKZ AFTJW AFXIZ AGUBO AGYEJ AHEUO AHHHB AIEXJ AIKHN AITUG AKIFW AKRWK ALMA_UNASSIGNED_HOLDINGS AMRAJ ANKPU AXJTR BKOJK BLECG BLXMC BNPGV CS3 DU5 EBS EFJIC EO8 EO9 EP2 EP3 F5P FDB FIRID FNPLU FYGXN G-Q GBLVA IHE J1W K-O KCYFY KOM LY9 M41 MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 RNS ROL RPZ SCU SDF SDG SDP SES SEW SPCBC SSH SSJ SSZ T5K ~02 ~G- ~KM 53G 9DU AAQXK AAYJJ AAYWO AAYXX ABEFU ABWVN ABXDB ACLOT ACRPL ACVFH ADCNI ADMUD ADNMO ADXHL AEGFY AEUPX AFPUW AGHFR AGQPQ AIGII AIIUN AKBMS AKYEP APXCP ASPBG AVWKF AZFZN CITATION EFKBS EFLBG EJD FEDTE FGOYB G-2 HMC HVGLF HZ~ R2- SEN WUQ XPP ZXP ZY4 ~HD NPM 7X8 7S9 L.6 |
| ID | FETCH-LOGICAL-c404t-cfd8b7d00ae2db06faa1d0a9188d34cec5224843724c095051e4aa266380bf053 |
| ISICitedReferencesCount | 0 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001290384700001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0048-9697 1879-1026 |
| IngestDate | Fri Oct 03 00:13:10 EDT 2025 Sun Sep 28 10:00:45 EDT 2025 Thu Apr 03 07:06:12 EDT 2025 Sat Nov 29 05:04:39 EST 2025 Tue Nov 18 22:32:07 EST 2025 Sun Apr 06 06:54:22 EDT 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Prediction accuracy Air pollution Health impact assessment BenMap Meteorological factors Machine learning |
| Language | English |
| License | Copyright © 2024 Elsevier B.V. All rights reserved. |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c404t-cfd8b7d00ae2db06faa1d0a9188d34cec5224843724c095051e4aa266380bf053 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| PMID | 39098427 |
| PQID | 3088555178 |
| PQPubID | 23479 |
| ParticipantIDs | proquest_miscellaneous_3153718647 proquest_miscellaneous_3088555178 pubmed_primary_39098427 crossref_citationtrail_10_1016_j_scitotenv_2024_175246 crossref_primary_10_1016_j_scitotenv_2024_175246 elsevier_sciencedirect_doi_10_1016_j_scitotenv_2024_175246 |
| PublicationCentury | 2000 |
| PublicationDate | 2024-11-01 |
| PublicationDateYYYYMMDD | 2024-11-01 |
| PublicationDate_xml | – month: 11 year: 2024 text: 2024-11-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | Netherlands |
| PublicationPlace_xml | – name: Netherlands |
| PublicationTitle | The Science of the total environment |
| PublicationTitleAlternate | Sci Total Environ |
| PublicationYear | 2024 |
| Publisher | Elsevier B.V |
| Publisher_xml | – name: Elsevier B.V |
| References | Chen, Shi, Gao, Li, Mao, Zhang (bb0025) 2017; 221 Sadiq, Sarkar, Raisa (bb0095) 2023; 157 Voorhees, Wang, Wang, Zhao, Wang, Kan (bb0125) 2014; 485-486 Sacks, Lloyd, Zhu, Anderton, Jang, Hubbell (bb0090) 2018; 104 Zhong, Hodgson, James Bloss, Shi (bb0145) 2023; 236 Wang, Sun, Liu, Wang, Liu, Feng (bb0130) 2023; 287 Verma, Verma, Mallet, Sisodiya, Zare, Dwivedi (bb0120) 2024; 15 Aguiar-Gil, Gómez-Peláez, Álvarez-Jaramillo, Correa-Ochoa, Saldarriaga-Molina (bb0010) 2020; 224 Alomari, Andó (bb0020) 2024; 21 Kalisa, Clark, Ntakirutimana, Amani, Volckens (bb0060) 2023; 9 Li, Wu, Wang, Cheng, Sun, Li (bb0065) 2024; 323 Nguyen, Nagashima, Doan, Khan, Niyogi (bb0075) 2023; 14 Xu, Kong, Cai (bb0135) 2024; 299 Zhang, Xu, Xu, Wang, Gao, Li (bb0140) 2022; 212 Chen, Dou, Zhao, Xiao, Lu, Qiu (bb0030) 2024; 465 Devasahayam, Albijanic (bb0035) 2024; 222 Ghahremanloo, Choi, Sayeed, Salman, Pan, Amani (bb0045) 2021; 247 Ni, Zhao, Li, Liu, Zhou, Zhang (bb0080) 2023; 14 Sun, Zhao, Liu, Qiu, Shen, Guillas (bb0115) 2023; 4 Goudarzi, Hopke, Yazdani (bb0050) 2021; 283 Ducruet, Polo Martin, Sene, Lo Prete, Sun, Itoh (bb0040) 2024; 915 Parthum, Pindilli, Hogan (bb0085) 2017; 203 So, Andersen, Chen, Stafoggia, de Hoogh, Katsouyanni (bb0100) 2022; 164 Afzal, Ziapour, Shokri, Shakibi, Sobhani (bb0005) 2023; 282 Ai, Zhang, Zhang, Chen, Tian, Chen (bb0015) 2024; 469 de Souza Fernandes Duarte, Lucio, Costa, Salgueiro, Salgado, Potes (bb0105) 2024; 240 Su, Chen, Wang, Zhang, Gao, Sun (bb0110) 2024 Han, Song, Shon, Kang, Bang, Oh (bb0055) 2021; 268 Meng, Hang, Lin, Li, Wang, Cao (bb0070) 2023; 171 Meng (10.1016/j.scitotenv.2024.175246_bb0070) 2023; 171 Han (10.1016/j.scitotenv.2024.175246_bb0055) 2021; 268 So (10.1016/j.scitotenv.2024.175246_bb0100) 2022; 164 Voorhees (10.1016/j.scitotenv.2024.175246_bb0125) 2014; 485-486 Alomari (10.1016/j.scitotenv.2024.175246_bb0020) 2024; 21 Goudarzi (10.1016/j.scitotenv.2024.175246_bb0050) 2021; 283 Parthum (10.1016/j.scitotenv.2024.175246_bb0085) 2017; 203 Sacks (10.1016/j.scitotenv.2024.175246_bb0090) 2018; 104 Sun (10.1016/j.scitotenv.2024.175246_bb0115) 2023; 4 Nguyen (10.1016/j.scitotenv.2024.175246_bb0075) 2023; 14 Sadiq (10.1016/j.scitotenv.2024.175246_bb0095) 2023; 157 Kalisa (10.1016/j.scitotenv.2024.175246_bb0060) 2023; 9 Zhang (10.1016/j.scitotenv.2024.175246_bb0140) 2022; 212 Devasahayam (10.1016/j.scitotenv.2024.175246_bb0035) 2024; 222 Verma (10.1016/j.scitotenv.2024.175246_bb0120) 2024; 15 Ghahremanloo (10.1016/j.scitotenv.2024.175246_bb0045) 2021; 247 Wang (10.1016/j.scitotenv.2024.175246_bb0130) 2023; 287 Chen (10.1016/j.scitotenv.2024.175246_bb0025) 2017; 221 Zhong (10.1016/j.scitotenv.2024.175246_bb0145) 2023; 236 Xu (10.1016/j.scitotenv.2024.175246_bb0135) 2024; 299 Afzal (10.1016/j.scitotenv.2024.175246_bb0005) 2023; 282 Chen (10.1016/j.scitotenv.2024.175246_bb0030) 2024; 465 Su (10.1016/j.scitotenv.2024.175246_bb0110) 2024 Li (10.1016/j.scitotenv.2024.175246_bb0065) 2024; 323 Ai (10.1016/j.scitotenv.2024.175246_bb0015) 2024; 469 Aguiar-Gil (10.1016/j.scitotenv.2024.175246_bb0010) 2020; 224 Ducruet (10.1016/j.scitotenv.2024.175246_bb0040) 2024; 915 de Souza Fernandes Duarte (10.1016/j.scitotenv.2024.175246_bb0105) 2024; 240 Ni (10.1016/j.scitotenv.2024.175246_bb0080) 2023; 14 |
| References_xml | – volume: 21 year: 2024 ident: bb0020 article-title: SHAP-based insights for aerospace PHM: temporal feature importance, dependencies, robustness, and interaction analysis publication-title: Results Eng. – volume: 915 year: 2024 ident: bb0040 article-title: Ports and their influence on local air pollution and public health: a global analysis publication-title: Sci. Total Environ. – year: 2024 ident: bb0110 article-title: Long- and short-term health benefits attributable to PM2.5 constituents reductions from 2013 to 2021: a spatiotemporal analysis in China publication-title: Sci. Total Environ. – volume: 212 year: 2022 ident: bb0140 article-title: Machine learning combined with the PMF model reveal the synergistic effects of sources and meteorological factors on PM2.5 pollution publication-title: Environ. Res. – volume: 222 year: 2024 ident: bb0035 article-title: Predicting hydrogen production from co-gasification of biomass and plastics using tree based machine learning algorithms publication-title: Renew. Energy – volume: 465 year: 2024 ident: bb0030 article-title: Machine learning-assisted assessment of key meteorological and crop factors affecting historical mulch pollution in China publication-title: J. Hazard. Mater. – volume: 104 start-page: 118 year: 2018 end-page: 129 ident: bb0090 article-title: The environmental Benefits Mapping and Analysis Program – Community Edition (BenMap–CE): a tool to estimate the health and economic benefits of reducing air pollution publication-title: Environ. Model Softw. – volume: 299 year: 2024 ident: bb0135 article-title: Cross-validation strategy for performance evaluation of machine learning algorithms in underwater acoustic target recognition publication-title: Ocean Eng. – volume: 171 year: 2023 ident: bb0070 article-title: A satellite-driven model to estimate long-term particulate sulfate levels and attributable mortality burden in China publication-title: Environ. Int. – volume: 485-486 start-page: 396 year: 2014 end-page: 405 ident: bb0125 article-title: Public health benefits of reducing air pollution in Shanghai: a proof-of-concept methodology with application to BenMap publication-title: Sci. Total Environ. – volume: 283 year: 2021 ident: bb0050 article-title: Forecasting PM2.5 concentration using artificial neural network and its health effects in Ahvaz, Iran publication-title: Chemosphere – volume: 14 year: 2023 ident: bb0075 article-title: Source apportionment of PM2.5 and the impact of future PM2.5 changes on human health in the monsoon-influenced humid subtropical climate publication-title: Atmos. Pollut. Res. – volume: 236 year: 2023 ident: bb0145 article-title: Impacts of net zero policies on air quality in a metropolitan area of the United Kingdom: towards world health organization air quality guidelines publication-title: Environ. Res. – volume: 4 year: 2023 ident: bb0115 article-title: Antagonism between ambient ozone increase and urbanization-oriented population migration on Chinese cardiopulmonary mortality publication-title: The Innovation – volume: 247 year: 2021 ident: bb0045 article-title: Estimating daily high-resolution PM2.5 concentrations over Texas: Machine Learning approach publication-title: Atmos. Environ. – volume: 157 year: 2023 ident: bb0095 article-title: Meteorological drought assessment in northern Bangladesh: a machine learning-based approach considering remote sensing indices publication-title: Ecol. Indic. – volume: 287 year: 2023 ident: bb0130 article-title: Reconstruction of the pan evaporation based on meteorological factors with machine learning method over China publication-title: Agric. Water Manag. – volume: 469 year: 2024 ident: bb0015 article-title: Causal association between long-term exposure to air pollution and incident Parkinson’s disease publication-title: J. Hazard. Mater. – volume: 268 year: 2021 ident: bb0055 article-title: Comprehensive study of a long-lasting severe haze in Seoul megacity and its impacts on fine particulate matter and health publication-title: Chemosphere – volume: 323 year: 2024 ident: bb0065 article-title: Effects of chemical mechanism and meteorological factors on the concentration of atmospheric pollutants in the megacity Beijing, China publication-title: Atmos. Environ. – volume: 164 year: 2022 ident: bb0100 article-title: Long-term exposure to air pollution and mortality in a Danish nationwide administrative cohort study: beyond mortality from cardiopulmonary disease and lung cancer publication-title: Environ. Int. – volume: 224 year: 2020 ident: bb0010 article-title: Evaluating the impact of PM2.5 atmospheric pollution on population mortality in an urbanized valley in the American tropics publication-title: Atmos. Environ. – volume: 221 start-page: 311 year: 2017 end-page: 317 ident: bb0025 article-title: Assessment of population exposure to PM2.5 for mortality in China and its public health benefit based on BenMap publication-title: Environ. Pollut. – volume: 15 year: 2024 ident: bb0120 article-title: Assessment of human and meteorological influences on PM10 concentrations: insights from machine learning algorithms publication-title: Atmos. Pollut. Res. – volume: 282 year: 2023 ident: bb0005 article-title: Building energy consumption prediction using multilayer perceptron neural network-assisted models; comparison of different optimization algorithms publication-title: Energy – volume: 203 start-page: 375 year: 2017 end-page: 382 ident: bb0085 article-title: Benefits of the fire mitigation ecosystem service in The Great Dismal Swamp National Wildlife Refuge, Virginia, USA publication-title: J. Environ. Manag. – volume: 240 year: 2024 ident: bb0105 article-title: Pollutant-meteorological factors and cardio-respiratory mortality in Portugal: seasonal variability and associations publication-title: Environ. Res. – volume: 9 year: 2023 ident: bb0060 article-title: Exposure to indoor and outdoor air pollution in schools in Africa: current status, knowledge gaps, and a call to action publication-title: Heliyon – volume: 14 year: 2023 ident: bb0080 article-title: Investigation of the impact mechanisms and patterns of meteorological factors on air quality and atmospheric pollutant concentrations during extreme weather events in Zhengzhou city, Henan Province publication-title: Atmos. Pollut. Res. – volume: 485-486 start-page: 396 year: 2014 ident: 10.1016/j.scitotenv.2024.175246_bb0125 article-title: Public health benefits of reducing air pollution in Shanghai: a proof-of-concept methodology with application to BenMap publication-title: Sci. Total Environ. doi: 10.1016/j.scitotenv.2014.03.113 – volume: 323 year: 2024 ident: 10.1016/j.scitotenv.2024.175246_bb0065 article-title: Effects of chemical mechanism and meteorological factors on the concentration of atmospheric pollutants in the megacity Beijing, China publication-title: Atmos. Environ. doi: 10.1016/j.atmosenv.2024.120393 – volume: 299 year: 2024 ident: 10.1016/j.scitotenv.2024.175246_bb0135 article-title: Cross-validation strategy for performance evaluation of machine learning algorithms in underwater acoustic target recognition publication-title: Ocean Eng. doi: 10.1016/j.oceaneng.2024.117236 – volume: 268 year: 2021 ident: 10.1016/j.scitotenv.2024.175246_bb0055 article-title: Comprehensive study of a long-lasting severe haze in Seoul megacity and its impacts on fine particulate matter and health publication-title: Chemosphere doi: 10.1016/j.chemosphere.2020.129369 – volume: 21 year: 2024 ident: 10.1016/j.scitotenv.2024.175246_bb0020 article-title: SHAP-based insights for aerospace PHM: temporal feature importance, dependencies, robustness, and interaction analysis publication-title: Results Eng. doi: 10.1016/j.rineng.2024.101834 – volume: 283 year: 2021 ident: 10.1016/j.scitotenv.2024.175246_bb0050 article-title: Forecasting PM2.5 concentration using artificial neural network and its health effects in Ahvaz, Iran publication-title: Chemosphere doi: 10.1016/j.chemosphere.2021.131285 – volume: 221 start-page: 311 year: 2017 ident: 10.1016/j.scitotenv.2024.175246_bb0025 article-title: Assessment of population exposure to PM2.5 for mortality in China and its public health benefit based on BenMap publication-title: Environ. Pollut. doi: 10.1016/j.envpol.2016.11.080 – volume: 104 start-page: 118 year: 2018 ident: 10.1016/j.scitotenv.2024.175246_bb0090 article-title: The environmental Benefits Mapping and Analysis Program – Community Edition (BenMap–CE): a tool to estimate the health and economic benefits of reducing air pollution publication-title: Environ. Model Softw. doi: 10.1016/j.envsoft.2018.02.009 – issue: 907 year: 2024 ident: 10.1016/j.scitotenv.2024.175246_bb0110 article-title: Long- and short-term health benefits attributable to PM2.5 constituents reductions from 2013 to 2021: a spatiotemporal analysis in China publication-title: Sci. Total Environ. – volume: 203 start-page: 375 year: 2017 ident: 10.1016/j.scitotenv.2024.175246_bb0085 article-title: Benefits of the fire mitigation ecosystem service in The Great Dismal Swamp National Wildlife Refuge, Virginia, USA publication-title: J. Environ. Manag. doi: 10.1016/j.jenvman.2017.08.018 – volume: 224 year: 2020 ident: 10.1016/j.scitotenv.2024.175246_bb0010 article-title: Evaluating the impact of PM2.5 atmospheric pollution on population mortality in an urbanized valley in the American tropics publication-title: Atmos. Environ. doi: 10.1016/j.atmosenv.2020.117343 – volume: 240 year: 2024 ident: 10.1016/j.scitotenv.2024.175246_bb0105 article-title: Pollutant-meteorological factors and cardio-respiratory mortality in Portugal: seasonal variability and associations publication-title: Environ. Res. doi: 10.1016/j.envres.2023.117491 – volume: 222 year: 2024 ident: 10.1016/j.scitotenv.2024.175246_bb0035 article-title: Predicting hydrogen production from co-gasification of biomass and plastics using tree based machine learning algorithms publication-title: Renew. Energy doi: 10.1016/j.renene.2023.119883 – volume: 164 year: 2022 ident: 10.1016/j.scitotenv.2024.175246_bb0100 article-title: Long-term exposure to air pollution and mortality in a Danish nationwide administrative cohort study: beyond mortality from cardiopulmonary disease and lung cancer publication-title: Environ. Int. doi: 10.1016/j.envint.2022.107241 – volume: 15 year: 2024 ident: 10.1016/j.scitotenv.2024.175246_bb0120 article-title: Assessment of human and meteorological influences on PM10 concentrations: insights from machine learning algorithms publication-title: Atmos. Pollut. Res. doi: 10.1016/j.apr.2024.102123 – volume: 469 year: 2024 ident: 10.1016/j.scitotenv.2024.175246_bb0015 article-title: Causal association between long-term exposure to air pollution and incident Parkinson’s disease publication-title: J. Hazard. Mater. doi: 10.1016/j.jhazmat.2024.133944 – volume: 915 year: 2024 ident: 10.1016/j.scitotenv.2024.175246_bb0040 article-title: Ports and their influence on local air pollution and public health: a global analysis publication-title: Sci. Total Environ. doi: 10.1016/j.scitotenv.2024.170099 – volume: 171 year: 2023 ident: 10.1016/j.scitotenv.2024.175246_bb0070 article-title: A satellite-driven model to estimate long-term particulate sulfate levels and attributable mortality burden in China publication-title: Environ. Int. doi: 10.1016/j.envint.2023.107740 – volume: 236 year: 2023 ident: 10.1016/j.scitotenv.2024.175246_bb0145 article-title: Impacts of net zero policies on air quality in a metropolitan area of the United Kingdom: towards world health organization air quality guidelines publication-title: Environ. Res. doi: 10.1016/j.envres.2023.116704 – volume: 247 year: 2021 ident: 10.1016/j.scitotenv.2024.175246_bb0045 article-title: Estimating daily high-resolution PM2.5 concentrations over Texas: Machine Learning approach publication-title: Atmos. Environ. doi: 10.1016/j.atmosenv.2021.118209 – volume: 14 year: 2023 ident: 10.1016/j.scitotenv.2024.175246_bb0080 article-title: Investigation of the impact mechanisms and patterns of meteorological factors on air quality and atmospheric pollutant concentrations during extreme weather events in Zhengzhou city, Henan Province publication-title: Atmos. Pollut. Res. doi: 10.1016/j.apr.2023.101932 – volume: 4 year: 2023 ident: 10.1016/j.scitotenv.2024.175246_bb0115 article-title: Antagonism between ambient ozone increase and urbanization-oriented population migration on Chinese cardiopulmonary mortality publication-title: The Innovation doi: 10.1016/j.xinn.2023.100517 – volume: 287 year: 2023 ident: 10.1016/j.scitotenv.2024.175246_bb0130 article-title: Reconstruction of the pan evaporation based on meteorological factors with machine learning method over China publication-title: Agric. Water Manag. doi: 10.1016/j.agwat.2023.108416 – volume: 465 year: 2024 ident: 10.1016/j.scitotenv.2024.175246_bb0030 article-title: Machine learning-assisted assessment of key meteorological and crop factors affecting historical mulch pollution in China publication-title: J. Hazard. Mater. doi: 10.1016/j.jhazmat.2023.133281 – volume: 212 year: 2022 ident: 10.1016/j.scitotenv.2024.175246_bb0140 article-title: Machine learning combined with the PMF model reveal the synergistic effects of sources and meteorological factors on PM2.5 pollution publication-title: Environ. Res. doi: 10.1016/j.envres.2022.113322 – volume: 9 year: 2023 ident: 10.1016/j.scitotenv.2024.175246_bb0060 article-title: Exposure to indoor and outdoor air pollution in schools in Africa: current status, knowledge gaps, and a call to action publication-title: Heliyon doi: 10.1016/j.heliyon.2023.e18450 – volume: 14 year: 2023 ident: 10.1016/j.scitotenv.2024.175246_bb0075 article-title: Source apportionment of PM2.5 and the impact of future PM2.5 changes on human health in the monsoon-influenced humid subtropical climate publication-title: Atmos. Pollut. Res. doi: 10.1016/j.apr.2023.101777 – volume: 157 year: 2023 ident: 10.1016/j.scitotenv.2024.175246_bb0095 article-title: Meteorological drought assessment in northern Bangladesh: a machine learning-based approach considering remote sensing indices publication-title: Ecol. Indic. doi: 10.1016/j.ecolind.2023.111233 – volume: 282 year: 2023 ident: 10.1016/j.scitotenv.2024.175246_bb0005 article-title: Building energy consumption prediction using multilayer perceptron neural network-assisted models; comparison of different optimization algorithms publication-title: Energy doi: 10.1016/j.energy.2023.128446 |
| SSID | ssj0000781 |
| Score | 2.4601033 |
| Snippet | This study aims to address accuracy challenges in assessing air pollution health impacts using Environmental Benefits Mapping and Analysis Program (BenMap),... |
| SourceID | proquest pubmed crossref elsevier |
| SourceType | Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 175246 |
| SubjectTerms | air pollutants Air pollution atmospheric pressure BenMap cardiovascular diseases China decision support systems dewpoint environment health effects assessments Health impact assessment Machine learning Meteorological factors mortality prediction Prediction accuracy relative humidity respiratory tract diseases |
| Title | Optimizing BenMAP health impact assessment with meteorological factor driven machine learning models |
| URI | https://dx.doi.org/10.1016/j.scitotenv.2024.175246 https://www.ncbi.nlm.nih.gov/pubmed/39098427 https://www.proquest.com/docview/3088555178 https://www.proquest.com/docview/3153718647 |
| Volume | 949 |
| WOSCitedRecordID | wos001290384700001&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: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 customDbUrl: eissn: 1879-1026 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000781 issn: 0048-9697 databaseCode: AIEXJ dateStart: 19950106 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1db9MwFLW6DaRJCEFhrHxMRkK8TJnc1Klt3grqBKh0Q3RS3yzHSUarNin90uDXcx07SSs2Nh54qaooTqKe0-PjG997EXoTBoEOm9oE3QnzqA6Ep1SUeAlloVAwx7Z1Xme2x_p9PhyK81ptXeTCrCcsTfnVlZj9V6jhGIBtUmf_Ae7yonAAvgPo8Amww-edgD8DEZiOfpkQwPs4_dI5d7mORUKkKmtx2iDsFGxzNi810DbgOY7mRgaPp_ley7hoLnFpO-csNi2tIVqhEG7DwTIzKZYbOXSl9K9sJkgKVHFTZh4lz7cUfB1NRmXAx-0U_vZdZeNRvBmb8KlL0qvklDMBQu-7YtfXHHMaLGzdUqeiYGl8G5j8Q-BtrGEMS38QPFhVrE_MfU-qEdsltftn8vSi15OD7nDwdvbDM93GzFt513plB-35LBCgj3udT93h52oOZ3l72_JZt3YGXnvvm3zNTeuW3L8MHqGHbuGBO5Ywj1EtTuvovm1F-rOODroVWnCaA3RRRw9sYBfbfLUnKKr4hS2_sOUXtvzCFb-w4Rfe5he2_MKWX9jxCxf8wpZfT9HFaXfw4aPnWnV4mhK69HQS8ZBFhKjYj0LSTpRqRkSJJudRi-pYg82n3LwjphpMPcwEMVUKzGGLkzCBieAA7aZZGh8iTJKE8yAkmgpFEwaOEjwmbfMoTOAiAWmgdvFDS-3q2Jt2KhNZbFgcyxIhaRCSFqEGIuXAmS3lcvuQdwWS0jlS6zQl8PH2wa8L7CVotnkRp9I4Wy1kC6b2AJYqjP_lHLAi4BvblDXQM0uc8qlbgghOffb8Dnd4gfarf-ZLtLucr-JX6J5eL0eL-RHaYUN-5Nj_G9yp0qc |
| linkProvider | Elsevier |
| 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=Optimizing+BenMAP+health+impact+assessment+with+meteorological+factor+driven+machine+learning+models&rft.jtitle=The+Science+of+the+total+environment&rft.au=Wu%2C+Juncheng&rft.au=Dai%2C+Qili&rft.au=Song%2C+Shaojie&rft.date=2024-11-01&rft.issn=1879-1026&rft.eissn=1879-1026&rft.volume=949&rft.spage=175246&rft_id=info:doi/10.1016%2Fj.scitotenv.2024.175246&rft.externalDBID=NO_FULL_TEXT |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0048-9697&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0048-9697&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0048-9697&client=summon |