High spatial resolution land-use regression model for urban ultrafine particle exposure assessment in Shanghai, China
Little is currently known about long-term health effects of ambient ultrafine particles (UFPs) due to the lack of exposure assessment metrics suitable for use in large population-based studies. Land use regression (LUR) models have been used increasingly for modeling small-scale spatial variation in...
Saved in:
Published in | The Science of the total environment Vol. 816; p. 151633 |
---|---|
Main Authors | , , , , , , , , , |
Format | Journal Article |
Language | English |
Published |
Netherlands
Elsevier B.V
10.04.2022
|
Subjects | |
Online Access | Get full text |
Cover
Loading…
Abstract | Little is currently known about long-term health effects of ambient ultrafine particles (UFPs) due to the lack of exposure assessment metrics suitable for use in large population-based studies. Land use regression (LUR) models have been used increasingly for modeling small-scale spatial variation in UFPs concentrations in European and American, but have never been applied in developing countries with heavy air pollution.
This study developed a land-use regression (LUR) model for UFP exposure assessment in Shanghai, a typic mega city of China, where dense population resides.
A 30-minute measurement of particle number concentrations of UFPs was collected at each visit at 144 fixed sites, and each was visited three times in each season of winter, spring, and summer. The annual adjusted average was calculated and regressed against pre-selected geographic information system-derived predictor variables using a stepwise variable selection method.
The final LUR model explained 69% of the spatial variability in UFP with a root mean square error of 6008 particles cm−3. The 10-fold cross validation R2 reached 0.68, revealing the robustness of the model. The final predictors included traffic-related NOx emissions, number of restaurants, building footprint area, and distance to the nearest national road. These predictors were within a relatively small buffer size, ranging from 50 m to 100 m, indicating great spatial variations of UFP particle number concentration and the need of high-resolution models for UFP exposure assessment in urban areas.
We concluded that based on a purpose-designed short-term monitoring network, LUR model can be applied to predict UFPs spatial surface in a mega city of China. Majority of the spatial variability in the annual mean of ambient UFP was explained in the model comprised primarily of traffic-, building-, and restaurant-related predictors.
[Display omitted]
•Lack of suitable exposure assessment metrics limits epidemiological studies of UFPs.•We built the first UFPs LUR model in China, upon specific-monitoring network.•The model comprised primarily of traffic-, building-&restaurant-related predictors.•The model explained majority of spatial variability in ambient UFP concentrations.•This study may support future population-based epidemiological studies of UFPs. |
---|---|
AbstractList | Little is currently known about long-term health effects of ambient ultrafine particles (UFPs) due to the lack of exposure assessment metrics suitable for use in large population-based studies. Land use regression (LUR) models have been used increasingly for modeling small-scale spatial variation in UFPs concentrations in European and American, but have never been applied in developing countries with heavy air pollution.
This study developed a land-use regression (LUR) model for UFP exposure assessment in Shanghai, a typic mega city of China, where dense population resides.
A 30-minute measurement of particle number concentrations of UFPs was collected at each visit at 144 fixed sites, and each was visited three times in each season of winter, spring, and summer. The annual adjusted average was calculated and regressed against pre-selected geographic information system-derived predictor variables using a stepwise variable selection method.
The final LUR model explained 69% of the spatial variability in UFP with a root mean square error of 6008 particles cm−3. The 10-fold cross validation R2 reached 0.68, revealing the robustness of the model. The final predictors included traffic-related NOx emissions, number of restaurants, building footprint area, and distance to the nearest national road. These predictors were within a relatively small buffer size, ranging from 50 m to 100 m, indicating great spatial variations of UFP particle number concentration and the need of high-resolution models for UFP exposure assessment in urban areas.
We concluded that based on a purpose-designed short-term monitoring network, LUR model can be applied to predict UFPs spatial surface in a mega city of China. Majority of the spatial variability in the annual mean of ambient UFP was explained in the model comprised primarily of traffic-, building-, and restaurant-related predictors.
[Display omitted]
•Lack of suitable exposure assessment metrics limits epidemiological studies of UFPs.•We built the first UFPs LUR model in China, upon specific-monitoring network.•The model comprised primarily of traffic-, building-&restaurant-related predictors.•The model explained majority of spatial variability in ambient UFP concentrations.•This study may support future population-based epidemiological studies of UFPs. Little is currently known about long-term health effects of ambient ultrafine particles (UFPs) due to the lack of exposure assessment metrics suitable for use in large population-based studies. Land use regression (LUR) models have been used increasingly for modeling small-scale spatial variation in UFPs concentrations in European and American, but have never been applied in developing countries with heavy air pollution. This study developed a land-use regression (LUR) model for UFP exposure assessment in Shanghai, a typic mega city of China, where dense population resides. A 30-minute measurement of particle number concentrations of UFPs was collected at each visit at 144 fixed sites, and each was visited three times in each season of winter, spring, and summer. The annual adjusted average was calculated and regressed against pre-selected geographic information system-derived predictor variables using a stepwise variable selection method. The final LUR model explained 69% of the spatial variability in UFP with a root mean square error of 6008 particles cm . The 10-fold cross validation R reached 0.68, revealing the robustness of the model. The final predictors included traffic-related NO emissions, number of restaurants, building footprint area, and distance to the nearest national road. These predictors were within a relatively small buffer size, ranging from 50 m to 100 m, indicating great spatial variations of UFP particle number concentration and the need of high-resolution models for UFP exposure assessment in urban areas. We concluded that based on a purpose-designed short-term monitoring network, LUR model can be applied to predict UFPs spatial surface in a mega city of China. Majority of the spatial variability in the annual mean of ambient UFP was explained in the model comprised primarily of traffic-, building-, and restaurant-related predictors. |
ArticleNumber | 151633 |
Author | Cai, Jing Fu, Qingyan Yang, Zhenchun Hu, Jianlin Lei, Xiaoning Ge, Yihui Xu, Xueyi Yi, Min Chao, Yuan Kan, Haidong |
Author_xml | – sequence: 1 givenname: Yihui surname: Ge fullname: Ge, Yihui organization: School of Public Health, Key Lab of Public Health Safety of the Ministry of Education and NHC Key Lab of Health Technology Assessment, Fudan University, Shanghai 200032, China – sequence: 2 givenname: Qingyan surname: Fu fullname: Fu, Qingyan organization: Shanghai Environmental Monitoring Center, Shanghai 200233, China – sequence: 3 givenname: Min surname: Yi fullname: Yi, Min organization: Shanghai Environmental Monitoring Center, Shanghai 200233, China – sequence: 4 givenname: Yuan surname: Chao fullname: Chao, Yuan organization: Shanghai Environmental Monitoring Center, Shanghai 200233, China – sequence: 5 givenname: Xiaoning surname: Lei fullname: Lei, Xiaoning organization: School of Public Health, Key Lab of Public Health Safety of the Ministry of Education and NHC Key Lab of Health Technology Assessment, Fudan University, Shanghai 200032, China – sequence: 6 givenname: Xueyi surname: Xu fullname: Xu, Xueyi organization: School of Public Health, Key Lab of Public Health Safety of the Ministry of Education and NHC Key Lab of Health Technology Assessment, Fudan University, Shanghai 200032, China – sequence: 7 givenname: Zhenchun surname: Yang fullname: Yang, Zhenchun organization: MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, St Mary's Campus, London, United Kingdom – sequence: 8 givenname: Jianlin surname: Hu fullname: Hu, Jianlin organization: Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science & Technology, Nanjing 210044, China – sequence: 9 givenname: Haidong surname: Kan fullname: Kan, Haidong email: kanh@fudan.edu.cn organization: School of Public Health, Key Lab of Public Health Safety of the Ministry of Education and NHC Key Lab of Health Technology Assessment, Fudan University, Shanghai 200032, China – sequence: 10 givenname: Jing surname: Cai fullname: Cai, Jing email: jingcai@fudan.edu.cn organization: School of Public Health, Key Lab of Public Health Safety of the Ministry of Education and NHC Key Lab of Health Technology Assessment, Fudan University, Shanghai 200032, China |
BackLink | https://www.ncbi.nlm.nih.gov/pubmed/34785221$$D View this record in MEDLINE/PubMed |
BookMark | eNqFkM1OwzAQhC1UBG3hFcAPQIqdP9tHVPEnVeIAnKONs2ldpXZkOxW8PakKvbKXlUYzs6tvRibWWSTklrMFZ7y83y6CNtFFtPtFylK-4AUvs-yMTLkUKuEsLSdkylguE1UqcUlmIWzZOELyC3KZ5UIWacqnZHgx6w0NPUQDHfUYXDdE4yztwDbJEHDU1qMcDtrONdjR1nk6-BosHbrooTUWaQ8-Gt0hxa_ehcEjhRDG1A5tpMbS9w3Y9QbMHV1ujIUrct5CF_D6d8_J59Pjx_IlWb09vy4fVonOBI9JWsiiUJzpOsdUsVK3UkCjdY6FwKIpgdV1DUq2jW5zBWmdC60ynbdSFqC4yOZEHHu1dyF4bKvemx3474qz6gCy2lYnkNUBZHUEOSZvjsl-qHfYnHJ_5EbDw9GA4_97g_5QhFZjYzzqWDXO_HvkB2NYjhk |
CitedBy_id | crossref_primary_10_1007_s11356_022_22151_4 crossref_primary_10_1016_j_envpol_2022_119470 crossref_primary_10_3390_su16135314 crossref_primary_10_1016_j_aeaoa_2023_100221 crossref_primary_10_1016_j_jtho_2023_05_024 crossref_primary_10_1016_j_scitotenv_2022_157524 crossref_primary_10_1016_j_envpol_2024_123664 crossref_primary_10_1016_j_envres_2022_115061 crossref_primary_10_3389_fenvs_2024_1399339 |
Cites_doi | 10.1016/j.envres.2015.12.016 10.1016/j.envpol.2016.02.041 10.1021/es505791g 10.1021/es100008x 10.1016/j.envres.2017.08.040 10.1021/acs.est.6b05920 10.1021/es0606780 10.1021/acs.est.7b05059 10.1016/j.envres.2005.12.013 10.1016/j.atmosenv.2017.02.028 10.1016/j.atmosenv.2020.117267 10.1016/j.scitotenv.2016.11.160 10.1177/0361198105193900118 10.1007/s11270-015-2726-6 10.1016/j.scitotenv.2008.01.038 10.1039/B615795E 10.3390/ijerph13111054 10.1021/acs.est.6b03476 10.1016/j.atmosenv.2010.03.035 10.1080/136588197242158 10.1016/j.scitotenv.2014.04.106 10.1016/j.envpol.2020.114027 10.1289/ehp.7939 10.1016/j.envpol.2015.04.011 10.1016/j.atmosenv.2012.01.058 10.1016/j.atmosenv.2015.02.018 10.1186/s12940-016-0137-9 10.1021/acs.est.9b02086 10.1021/es304495s 10.1016/j.scitotenv.2020.140059 10.1016/j.proeng.2017.10.062 10.1016/j.atmosenv.2014.05.070 10.1016/j.scitotenv.2019.134234 10.1016/j.atmosenv.2010.08.016 10.1021/es1023042 10.1021/es402156g 10.1021/es301948k 10.1016/j.scitotenv.2017.03.094 10.1016/j.scitotenv.2015.07.051 10.1016/j.atmosenv.2013.01.061 |
ContentType | Journal Article |
Copyright | 2021 Elsevier B.V. Copyright © 2021 Elsevier B.V. All rights reserved. |
Copyright_xml | – notice: 2021 Elsevier B.V. – notice: Copyright © 2021 Elsevier B.V. All rights reserved. |
DBID | CGR CUY CVF ECM EIF NPM AAYXX CITATION |
DOI | 10.1016/j.scitotenv.2021.151633 |
DatabaseName | Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed CrossRef |
DatabaseTitle | MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) CrossRef |
DatabaseTitleList | MEDLINE |
Database_xml | – sequence: 1 dbid: NPM name: PubMed url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: EIF name: MEDLINE url: https://proxy.k.utb.cz/login?url=https://www.webofscience.com/wos/medline/basic-search sourceTypes: Index Database |
DeliveryMethod | fulltext_linktorsrc |
Discipline | Public Health Biology Environmental Sciences |
EISSN | 1879-1026 |
ExternalDocumentID | 10_1016_j_scitotenv_2021_151633 34785221 S0048969721067097 |
Genre | Journal Article |
GeographicLocations | China |
GeographicLocations_xml | – name: China |
GrantInformation_xml | – fundername: Medical Research Council grantid: MR/S019669/1 |
GroupedDBID | --- --K --M .~1 0R~ 1B1 1RT 1~. 1~5 4.4 457 4G. 5VS 7-5 71M 8P~ 9JM AABNK AACTN AAEDT AAEDW AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AAXUO ABFNM ABFYP ABJNI ABLST ABMAC ABYKQ ACDAQ ACGFS ACRLP ADBBV ADEZE AEBSH AEKER AENEX AFKWA AFTJW AFXIZ AGUBO AGYEJ AHEUO AHHHB AIEXJ AIKHN AITUG AJOXV AKIFW ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AXJTR BKOJK BLECG BLXMC CS3 DU5 EBS EFJIC EFLBG 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 SPCBC SSJ SSZ T5K ~02 ~G- ~KM AAHBH AAXKI AKRWK CGR CUY CVF ECM EIF NPM RIG 53G AAQXK AAYJJ AAYXX ABEFU ABTAH ABXDB ADMUD AFJKZ AGHFR ASPBG AVWKF AZFZN CITATION EJD FEDTE FGOYB G-2 HMC HVGLF HZ~ R2- SEN SEW WUQ XPP ZXP ZY4 |
ID | FETCH-LOGICAL-c371t-25855910cb4e2906cf87adcc4e57e5d6a0bbba98fdcf49a2b47c93c4f885a9173 |
IEDL.DBID | .~1 |
ISSN | 0048-9697 |
IngestDate | Thu Sep 26 18:37:03 EDT 2024 Sat Sep 28 08:19:08 EDT 2024 Fri Feb 23 02:41:15 EST 2024 |
IsPeerReviewed | true |
IsScholarly | true |
Keywords | Ultrafine particles Purpose-designed monitoring network Spatial variation Exposure assessment Land use regression model |
Language | English |
License | Copyright © 2021 Elsevier B.V. All rights reserved. |
LinkModel | DirectLink |
MergedId | FETCHMERGED-LOGICAL-c371t-25855910cb4e2906cf87adcc4e57e5d6a0bbba98fdcf49a2b47c93c4f885a9173 |
PMID | 34785221 |
ParticipantIDs | crossref_primary_10_1016_j_scitotenv_2021_151633 pubmed_primary_34785221 elsevier_sciencedirect_doi_10_1016_j_scitotenv_2021_151633 |
PublicationCentury | 2000 |
PublicationDate | 2022-04-10 |
PublicationDateYYYYMMDD | 2022-04-10 |
PublicationDate_xml | – month: 04 year: 2022 text: 2022-04-10 day: 10 |
PublicationDecade | 2020 |
PublicationPlace | Netherlands |
PublicationPlace_xml | – name: Netherlands |
PublicationTitle | The Science of the total environment |
PublicationTitleAlternate | Sci Total Environ |
PublicationYear | 2022 |
Publisher | Elsevier B.V |
Publisher_xml | – name: Elsevier B.V |
References | Davis, Lents, Osses, Nikkila, Barth (bb0050) 2005; 1939 Saha, Li, Apte, Robinson, Presto (bb0175) 2019; 53 Pirhadi, Mousavi, Sowlat, Janssen, Cassee, Sioutas (bb0160) 2020; 260 Abernethy, Allen, McKendry, Brauer (bb0010) 2013; 47 Kumar, Robins, Vardoulakis, Britter (bb0120) 2010; 44 Sioutas, Delfino, Singh (bb0185) 2005; 113 Weichenthal, Van Ryswyk, Goldstein, Shekarrizfard, Hatzopoulou (bb0210) 2016; 208 Zhu, Li, Liao, Fan, Li (bb0240) 2014; 5 Abdullahi, Delgado-Saborit, Harrison (bb0005) 2013; 71 Amini, Taghavi-Shahri, Henderson, Naddafi, Nabizadeh, Yunesian (bb0015) 2014; 488 Masiol, Harrison (bb0135) 2014; 95 Sabaliauskas, Jeong, Yao, Reali, Sun, Evans (bb0170) 2015; 110 Yi (bb0230) 2020; 2 Chen, Zhao, Zhang, Zhao (bb0045) 2017; 205 Baldauf, Devlin, Gehr, Giannelli, Hassett-Sipple, Jung, Martini, McDonald, Sacks, Walker (bb0020) 2016; 13 Farrell, Weichenthal, Goldberg, Valois, Shekarrizfard, Hatzopoulou (bb0065) 2016; 212 Karner, Eisinger, Niemeier (bb0105) 2010; 44 Minet, Liu, Valois, Xu, Weichenthal, Hatzopoulou (bb0140) 2018; 52 Eeftens, Meier, Schindler, Aguilera, Phuleria, Ineichen, Davey, Ducret-Stich, Keidel, Probst-Hensch (bb0060) 2016; 15 Hoek, Beelen, Kos, Dijkema, Zee, Fischer, Brunekreef (bb0095) 2011; 45 Yu, Venecek, Hu, Tanrikulu, Soon, Tran, Fairley, Kleeman (bb0235) 2018; 1 See, Balasubramanian (bb0180) 2006; 102 Eeftens, Beelen, de Hoogh, Bellander, Cesaroni, Cirach, Declercq, Dėdelė, Dons, de Nazelle (bb0055) 2012; 46 van Nunen, Vermeulen, Tsai, Probst-Hensch, Ineichen, Davey, Imboden, Ducret-Stich, Naccarati, Raffaele (bb0155) 2017; 51 Rivera, Basagaña, Aguilera, Agis, Bouso, Foraster, Medina-Ramón, Pey, Künzli, Hoek (bb0165) 2012; 54 Montagne, Hoek, Klompmaker, Wang, Meliefste, Brunekreef (bb0145) 2015; 49 Moore, Jerrett, Mack, Künzli (bb0150) 2007; 9 HEI (bb0080) 2013; 3 Wolf, Cyrys, Harciníková, Gu, Kusch, Hampel, Schneider, Peters (bb0220) 2017; 579 Ghassoun, Ruths, Löwner, Weber (bb0075) 2015; 536 Kerckhoffs, Hoek, Messier, Brunekreef, Meliefste, Klompmaker, Vermeulen (bb0110) 2016; 50 Cassee, Morawska, Peters, Wierzbicka, Buonanno, Cyrys, SchnelleKreis, Kowalski, Riediker, Birmili (bb0035) 2019 Wang, Chen, Huang, Fu (bb0200) 2008; 398 Heinzerling, Hsu, Yip (bb0085) 2016; 227 Lee, Brauer, Wong, Tang, Tsui, Choi, Cheng, Lai, Tian, Thach (bb0125) 2017; 592 WHO (bb0215) 2013 Yang, Freni-Sterrantino, Fuller, Gulliver (bb0225) 2020; 740 Henderson, Beckerman, Jerrett, Brauer (bb0090) 2007; 41 Liu, Zhang, Cheng, Xing, Zhang, Streets, Jang, Wang, Hao (bb0130) 2010; 44 Tang, Blangiardo, Gulliver (bb0190) 2013; 47 Jones, Hoek, Fisher, Hasheminassab, Wang, Ward, Sioutas, Vermeulen, Silverman (bb0100) 2020; 699 Weichenthal, Van Ryswyk, Goldstein, Bagg, Shekkarizfard, Hatzopoulou (bb0205) 2016; 146 Cai, Ge, Li, Yang, Liu, Meng, Wang, Niu, Kan, Schikowski (bb0030) 2020; 223 Ghadiri, Rashidi, Broomandi (bb0070) 2017; 3 Kerckhoffs, Hoek, Vlaanderen, van Nunen, Messier, Brunekreef, Gulliver, Vermeulen (bb0115) 2017; 159 Briggs, Collins, Elliott, Fischer, Kingham, Lebret, Pryl, Van Reeuwijk, Smallbone, Van Der Veen (bb0025) 1997; 11 Cattani, Gaeta, di Bucchianico, De Santis, Gaddi, Cusano, Ancona, Badaloni, Forastiere, Gariazzo (bb0040) 2017; 156 Wang, Ding (bb0195) 2006; 23 Sioutas (10.1016/j.scitotenv.2021.151633_bb0185) 2005; 113 Ghassoun (10.1016/j.scitotenv.2021.151633_bb0075) 2015; 536 Montagne (10.1016/j.scitotenv.2021.151633_bb0145) 2015; 49 Yi (10.1016/j.scitotenv.2021.151633_bb0230) 2020; 2 Lee (10.1016/j.scitotenv.2021.151633_bb0125) 2017; 592 Eeftens (10.1016/j.scitotenv.2021.151633_bb0060) 2016; 15 Hoek (10.1016/j.scitotenv.2021.151633_bb0095) 2011; 45 Liu (10.1016/j.scitotenv.2021.151633_bb0130) 2010; 44 Tang (10.1016/j.scitotenv.2021.151633_bb0190) 2013; 47 Weichenthal (10.1016/j.scitotenv.2021.151633_bb0205) 2016; 146 See (10.1016/j.scitotenv.2021.151633_bb0180) 2006; 102 Kumar (10.1016/j.scitotenv.2021.151633_bb0120) 2010; 44 Heinzerling (10.1016/j.scitotenv.2021.151633_bb0085) 2016; 227 Wang (10.1016/j.scitotenv.2021.151633_bb0195) 2006; 23 Wolf (10.1016/j.scitotenv.2021.151633_bb0220) 2017; 579 Pirhadi (10.1016/j.scitotenv.2021.151633_bb0160) 2020; 260 Jones (10.1016/j.scitotenv.2021.151633_bb0100) 2020; 699 Briggs (10.1016/j.scitotenv.2021.151633_bb0025) 1997; 11 Eeftens (10.1016/j.scitotenv.2021.151633_bb0055) 2012; 46 Kerckhoffs (10.1016/j.scitotenv.2021.151633_bb0110) 2016; 50 Abernethy (10.1016/j.scitotenv.2021.151633_bb0010) 2013; 47 Amini (10.1016/j.scitotenv.2021.151633_bb0015) 2014; 488 Cattani (10.1016/j.scitotenv.2021.151633_bb0040) 2017; 156 Yu (10.1016/j.scitotenv.2021.151633_bb0235) 2018; 1 Kerckhoffs (10.1016/j.scitotenv.2021.151633_bb0115) 2017; 159 Yang (10.1016/j.scitotenv.2021.151633_bb0225) 2020; 740 Moore (10.1016/j.scitotenv.2021.151633_bb0150) 2007; 9 van Nunen (10.1016/j.scitotenv.2021.151633_bb0155) 2017; 51 Sabaliauskas (10.1016/j.scitotenv.2021.151633_bb0170) 2015; 110 Cassee (10.1016/j.scitotenv.2021.151633_bb0035) 2019 Saha (10.1016/j.scitotenv.2021.151633_bb0175) 2019; 53 Weichenthal (10.1016/j.scitotenv.2021.151633_bb0210) 2016; 208 Abdullahi (10.1016/j.scitotenv.2021.151633_bb0005) 2013; 71 Ghadiri (10.1016/j.scitotenv.2021.151633_bb0070) 2017; 3 Cai (10.1016/j.scitotenv.2021.151633_bb0030) 2020; 223 Wang (10.1016/j.scitotenv.2021.151633_bb0200) 2008; 398 Baldauf (10.1016/j.scitotenv.2021.151633_bb0020) 2016; 13 Henderson (10.1016/j.scitotenv.2021.151633_bb0090) 2007; 41 Farrell (10.1016/j.scitotenv.2021.151633_bb0065) 2016; 212 Karner (10.1016/j.scitotenv.2021.151633_bb0105) 2010; 44 Masiol (10.1016/j.scitotenv.2021.151633_bb0135) 2014; 95 Minet (10.1016/j.scitotenv.2021.151633_bb0140) 2018; 52 Chen (10.1016/j.scitotenv.2021.151633_bb0045) 2017; 205 WHO (10.1016/j.scitotenv.2021.151633_bb0215) 2013 Rivera (10.1016/j.scitotenv.2021.151633_bb0165) 2012; 54 Davis (10.1016/j.scitotenv.2021.151633_bb0050) 2005; 1939 HEI (10.1016/j.scitotenv.2021.151633_bb0080) 2013; 3 Zhu (10.1016/j.scitotenv.2021.151633_bb0240) 2014; 5 |
References_xml | – volume: 9 start-page: 246 year: 2007 end-page: 252 ident: bb0150 article-title: A land use regression model for predicting ambient fine particulate matter across Los Angeles, CA publication-title: J. Environ. Monit. contributor: fullname: Künzli – volume: 223 year: 2020 ident: bb0030 article-title: Application of land use regression to assess exposure and identify potential sources in PM2. 5, BC, NO2 concentrations publication-title: Atmos. Environ. contributor: fullname: Schikowski – volume: 45 start-page: 622 year: 2011 end-page: 628 ident: bb0095 article-title: Land use regression model for ultrafine particles in Amsterdam publication-title: Environ. Sci. Technol. contributor: fullname: Brunekreef – volume: 113 start-page: 947 year: 2005 end-page: 955 ident: bb0185 article-title: Exposure assessment for atmospheric ultrafine particles (UFPs) and implications in epidemiologic research publication-title: Environ. Health Perspect. contributor: fullname: Singh – volume: 15 start-page: 1 year: 2016 end-page: 14 ident: bb0060 article-title: Development of land use regression models for nitrogen dioxide, ultrafine particles, lung deposited surface area, and four other markers of particulate matter pollution in the swiss SAPALDIA regions publication-title: Environ. Health contributor: fullname: Probst-Hensch – volume: 592 start-page: 306 year: 2017 end-page: 315 ident: bb0125 article-title: Land use regression modelling of air pollution in high density high rise cities: a case study in Hong Kong publication-title: Sci. Total Environ. contributor: fullname: Thach – volume: 3 year: 2013 ident: bb0080 article-title: Understanding the Health Effects of Ambient Ultrafine Particles. HEI Perspectives contributor: fullname: HEI – volume: 488 start-page: 343 year: 2014 end-page: 353 ident: bb0015 article-title: Land use regression models to estimate the annual and seasonal spatial variability of sulfur dioxide and particulate matter in Tehran, Iran publication-title: Sci. Total Environ. contributor: fullname: Yunesian – volume: 536 start-page: 150 year: 2015 end-page: 160 ident: bb0075 article-title: Intra-urban variation of ultrafine particles as evaluated by process related land use and pollutant driven regression modelling publication-title: Sci. Total Environ. contributor: fullname: Weber – volume: 146 start-page: 65 year: 2016 end-page: 72 ident: bb0205 article-title: A land use regression model for ambient ultrafine particles in Montreal, Canada: a comparison of linear regression and a machine learning approach publication-title: Environ. Res. contributor: fullname: Hatzopoulou – volume: 41 start-page: 2422 year: 2007 end-page: 2428 ident: bb0090 article-title: Application of land use regression to estimate long-term concentrations of traffic-related nitrogen oxides and fine particulate matter publication-title: Environ. Sci. Technol. contributor: fullname: Brauer – volume: 44 start-page: 5334 year: 2010 end-page: 5344 ident: bb0105 article-title: Near-roadway air quality: synthesizing the findings from real-world data publication-title: Environ. Sci. Technol. contributor: fullname: Niemeier – volume: 54 start-page: 657 year: 2012 end-page: 666 ident: bb0165 article-title: Spatial distribution of ultrafine particles in urban settings: a land use regression model publication-title: Atmos. Environ. contributor: fullname: Hoek – volume: 23 start-page: 44 year: 2006 end-page: 47 ident: bb0195 article-title: Study on oil smoke pollution in the catering trade and treating situation (in Chinese) publication-title: J. Chongqing Technol. Bus. Univ. contributor: fullname: Ding – volume: 95 start-page: 409 year: 2014 end-page: 455 ident: bb0135 article-title: Aircraft engine exhaust emissions and other airport-related contributions to ambient air pollution: a review publication-title: Atmos. Environ. contributor: fullname: Harrison – volume: 44 start-page: 2415 year: 2010 end-page: 2426 ident: bb0130 article-title: Understanding of regional air pollution over China using CMAQ, part I performance evaluation and seasonal variation publication-title: Atmos. Environ. contributor: fullname: Hao – volume: 47 start-page: 11643 year: 2013 end-page: 11650 ident: bb0190 article-title: Using building heights and street configuration to enhance intraurban PM10, NOx, and NO2 land use regression models publication-title: Environ. Sci. Technol. contributor: fullname: Gulliver – volume: 227 start-page: 32 year: 2016 ident: bb0085 article-title: Respiratory health effects of ultrafine particles in children: a literature review publication-title: Water Air Soil Pollut. contributor: fullname: Yip – volume: 47 start-page: 5217 year: 2013 end-page: 5225 ident: bb0010 article-title: A land use regression model for ultrafine particles in Vancouver, Canada publication-title: Environ. Sci. Technol. contributor: fullname: Brauer – volume: 398 start-page: 60 year: 2008 end-page: 67 ident: bb0200 article-title: On-road vehicle emission inventory and its uncertainty analysis for Shanghai, China publication-title: Sci. Total Environ. contributor: fullname: Fu – volume: 50 start-page: 12894 year: 2016 end-page: 12902 ident: bb0110 article-title: Comparison of ultrafine particle and black carbon concentration predictions from a mobile and short-term stationary land-use regression model publication-title: Environ. Sci. Technol. contributor: fullname: Vermeulen – volume: 2 start-page: 225 year: 2020 end-page: 234 ident: bb0230 article-title: Design and application of real-time vehicle emission measurement information system in Shanghai publication-title: Environ. Monit. China contributor: fullname: Yi – volume: 159 start-page: 500 year: 2017 end-page: 508 ident: bb0115 article-title: Robustness of intra urban land-use regression models for ultrafine particles and black carbon based on mobile monitoring publication-title: Environ. Res. contributor: fullname: Vermeulen – volume: 52 start-page: 3512 year: 2018 end-page: 3519 ident: bb0140 article-title: Development and comparison of air pollution exposure surfaces derived from on-road mobile monitoring and short-term stationary sidewalk measurements publication-title: Environ. Sci. Technol. contributor: fullname: Hatzopoulou – volume: 212 start-page: 498 year: 2016 end-page: 507 ident: bb0065 article-title: Near roadway air pollution across a spatially extensive road and cycling network publication-title: Environ. Pollut. contributor: fullname: Hatzopoulou – volume: 13 start-page: 1054 year: 2016 ident: bb0020 article-title: Ultrafine particle metrics and research considerations: review of the 2015 UFP workshop publication-title: Int. J. Environ. Res. Public Health contributor: fullname: Walker – year: 2019 ident: bb0035 article-title: White Paper: Ambient Ultrafine Particles: Evidence for Policy Makers contributor: fullname: Birmili – volume: 53 start-page: 7326 year: 2019 end-page: 7336 ident: bb0175 article-title: Urban ultrafine particle exposure assessment with land-use regression: influence of sampling strategy publication-title: Environ. Sci. Technol. contributor: fullname: Presto – year: 2013 ident: bb0215 article-title: Review of Evidence on Health Aspects of Air Pollution–REVIHAAP Project: Technical Report [Internet] contributor: fullname: WHO – volume: 205 start-page: 2231 year: 2017 end-page: 2237 ident: bb0045 article-title: Source strength of ultrafine and fine particle due to chinese cooking publication-title: Procedia Eng. contributor: fullname: Zhao – volume: 740 year: 2020 ident: bb0225 article-title: Development and transferability of ultrafine particle land use regression models in London publication-title: Sci. Total Environ. contributor: fullname: Gulliver – volume: 102 start-page: 197 year: 2006 end-page: 204 ident: bb0180 article-title: Risk assessment of exposure to indoor aerosols associated with chinese cooking publication-title: Environ. Res. contributor: fullname: Balasubramanian – volume: 51 start-page: 3336 year: 2017 end-page: 3345 ident: bb0155 article-title: Land use regression models for ultrafine particles in six european areas publication-title: Environ. Sci. Technol. contributor: fullname: Raffaele – volume: 5 start-page: 57 year: 2014 end-page: 60 ident: bb0240 article-title: Analysis of characters of particulate emissions generated from urban cooking fume publication-title: Green Building contributor: fullname: Li – volume: 11 start-page: 699 year: 1997 end-page: 718 ident: bb0025 article-title: Mapping urban air pollution using GIS: a regression-based approach publication-title: Int. J. Geogr. Inf. Sci. contributor: fullname: Van Der Veen – volume: 3 start-page: 639 year: 2017 end-page: 653 ident: bb0070 article-title: Evaluation euro IV of effectiveness in transportation systems of Tehran on air quality: application of IVE model publication-title: Pollution contributor: fullname: Broomandi – volume: 208 start-page: 241 year: 2016 end-page: 248 ident: bb0210 article-title: Characterizing the spatial distribution of ambient ultrafine particles in Toronto, Canada: a land use regression model publication-title: Environ. Pollut. contributor: fullname: Hatzopoulou – volume: 156 start-page: 52 year: 2017 end-page: 60 ident: bb0040 article-title: Development of land-use regression models for exposure assessment to ultrafine particles in Rome, Italy publication-title: Atmos. Environ. contributor: fullname: Gariazzo – volume: 1 year: 2018 ident: bb0235 article-title: Sources of airborne ultrafine particle number and mass concentrations in California publication-title: Atmos. Chem. Phys. Discuss contributor: fullname: Kleeman – volume: 699 year: 2020 ident: bb0100 article-title: Land use regression models for ultrafine particles, fine particles, and black carbon in Southern California publication-title: Sci. Total Environ. contributor: fullname: Silverman – volume: 1939 start-page: 156 year: 2005 end-page: 165 ident: bb0050 article-title: Development and application of an international vehicle emissions model publication-title: Transp. Res. Rec. contributor: fullname: Barth – volume: 49 start-page: 8712 year: 2015 end-page: 8720 ident: bb0145 article-title: Land use regression models for ultrafine particles and black carbon based on short-term monitoring predict past spatial variation publication-title: Environ. Sci. Technol. contributor: fullname: Brunekreef – volume: 110 start-page: 84 year: 2015 end-page: 92 ident: bb0170 article-title: Development of a land-use regression model for ultrafine particles in Toronto, Canada publication-title: Atmos. Environ. contributor: fullname: Evans – volume: 44 start-page: 5035 year: 2010 end-page: 5052 ident: bb0120 article-title: A review of the characteristics of nanoparticles in the urban atmosphere and the prospects for developing regulatory controls publication-title: Atmos. Environ. contributor: fullname: Britter – volume: 260 year: 2020 ident: bb0160 article-title: Relative contributions of a major international airport activities and other urban sources to the particle number concentrations (PNCs) at a nearby monitoring site publication-title: Environ. Pollut. contributor: fullname: Sioutas – volume: 46 start-page: 11195 year: 2012 end-page: 11205 ident: bb0055 article-title: Development of land use regression models for PM2. 5, PM2. 5 absorbance, PM10 and PMcoarse in 20 European study areas; results of the ESCAPE project publication-title: Environ. Sci. Technol. contributor: fullname: de Nazelle – volume: 579 start-page: 1531 year: 2017 end-page: 1540 ident: bb0220 article-title: Land use regression modeling of ultrafine particles, ozone, nitrogen oxides and markers of particulate matter pollution in Augsburg, Germany publication-title: Sci. Total Environ. contributor: fullname: Peters – volume: 71 start-page: 260 year: 2013 end-page: 294 ident: bb0005 article-title: Emissions and indoor concentrations of particulate matter and its specific chemical components from cooking: a review publication-title: Atmos. Environ. contributor: fullname: Harrison – volume: 146 start-page: 65 year: 2016 ident: 10.1016/j.scitotenv.2021.151633_bb0205 article-title: A land use regression model for ambient ultrafine particles in Montreal, Canada: a comparison of linear regression and a machine learning approach publication-title: Environ. Res. doi: 10.1016/j.envres.2015.12.016 contributor: fullname: Weichenthal – volume: 212 start-page: 498 year: 2016 ident: 10.1016/j.scitotenv.2021.151633_bb0065 article-title: Near roadway air pollution across a spatially extensive road and cycling network publication-title: Environ. Pollut. doi: 10.1016/j.envpol.2016.02.041 contributor: fullname: Farrell – volume: 49 start-page: 8712 issue: 14 year: 2015 ident: 10.1016/j.scitotenv.2021.151633_bb0145 article-title: Land use regression models for ultrafine particles and black carbon based on short-term monitoring predict past spatial variation publication-title: Environ. Sci. Technol. doi: 10.1021/es505791g contributor: fullname: Montagne – volume: 1 year: 2018 ident: 10.1016/j.scitotenv.2021.151633_bb0235 article-title: Sources of airborne ultrafine particle number and mass concentrations in California publication-title: Atmos. Chem. Phys. Discuss contributor: fullname: Yu – volume: 44 start-page: 5334 issue: 14 year: 2010 ident: 10.1016/j.scitotenv.2021.151633_bb0105 article-title: Near-roadway air quality: synthesizing the findings from real-world data publication-title: Environ. Sci. Technol. doi: 10.1021/es100008x contributor: fullname: Karner – volume: 159 start-page: 500 year: 2017 ident: 10.1016/j.scitotenv.2021.151633_bb0115 article-title: Robustness of intra urban land-use regression models for ultrafine particles and black carbon based on mobile monitoring publication-title: Environ. Res. doi: 10.1016/j.envres.2017.08.040 contributor: fullname: Kerckhoffs – volume: 51 start-page: 3336 issue: 6 year: 2017 ident: 10.1016/j.scitotenv.2021.151633_bb0155 article-title: Land use regression models for ultrafine particles in six european areas publication-title: Environ. Sci. Technol. doi: 10.1021/acs.est.6b05920 contributor: fullname: van Nunen – volume: 41 start-page: 2422 issue: 7 year: 2007 ident: 10.1016/j.scitotenv.2021.151633_bb0090 article-title: Application of land use regression to estimate long-term concentrations of traffic-related nitrogen oxides and fine particulate matter publication-title: Environ. Sci. Technol. doi: 10.1021/es0606780 contributor: fullname: Henderson – volume: 52 start-page: 3512 issue: 6 year: 2018 ident: 10.1016/j.scitotenv.2021.151633_bb0140 article-title: Development and comparison of air pollution exposure surfaces derived from on-road mobile monitoring and short-term stationary sidewalk measurements publication-title: Environ. Sci. Technol. doi: 10.1021/acs.est.7b05059 contributor: fullname: Minet – volume: 102 start-page: 197 issue: 2 year: 2006 ident: 10.1016/j.scitotenv.2021.151633_bb0180 article-title: Risk assessment of exposure to indoor aerosols associated with chinese cooking publication-title: Environ. Res. doi: 10.1016/j.envres.2005.12.013 contributor: fullname: See – volume: 156 start-page: 52 year: 2017 ident: 10.1016/j.scitotenv.2021.151633_bb0040 article-title: Development of land-use regression models for exposure assessment to ultrafine particles in Rome, Italy publication-title: Atmos. Environ. doi: 10.1016/j.atmosenv.2017.02.028 contributor: fullname: Cattani – volume: 223 year: 2020 ident: 10.1016/j.scitotenv.2021.151633_bb0030 article-title: Application of land use regression to assess exposure and identify potential sources in PM2. 5, BC, NO2 concentrations publication-title: Atmos. Environ. doi: 10.1016/j.atmosenv.2020.117267 contributor: fullname: Cai – volume: 5 start-page: 57 year: 2014 ident: 10.1016/j.scitotenv.2021.151633_bb0240 article-title: Analysis of characters of particulate emissions generated from urban cooking fume publication-title: Green Building contributor: fullname: Zhu – year: 2019 ident: 10.1016/j.scitotenv.2021.151633_bb0035 contributor: fullname: Cassee – year: 2013 ident: 10.1016/j.scitotenv.2021.151633_bb0215 contributor: fullname: WHO – volume: 579 start-page: 1531 year: 2017 ident: 10.1016/j.scitotenv.2021.151633_bb0220 article-title: Land use regression modeling of ultrafine particles, ozone, nitrogen oxides and markers of particulate matter pollution in Augsburg, Germany publication-title: Sci. Total Environ. doi: 10.1016/j.scitotenv.2016.11.160 contributor: fullname: Wolf – volume: 1939 start-page: 156 issue: 1 year: 2005 ident: 10.1016/j.scitotenv.2021.151633_bb0050 article-title: Development and application of an international vehicle emissions model publication-title: Transp. Res. Rec. doi: 10.1177/0361198105193900118 contributor: fullname: Davis – volume: 227 start-page: 32 issue: 1 year: 2016 ident: 10.1016/j.scitotenv.2021.151633_bb0085 article-title: Respiratory health effects of ultrafine particles in children: a literature review publication-title: Water Air Soil Pollut. doi: 10.1007/s11270-015-2726-6 contributor: fullname: Heinzerling – volume: 398 start-page: 60 issue: 1–3 year: 2008 ident: 10.1016/j.scitotenv.2021.151633_bb0200 article-title: On-road vehicle emission inventory and its uncertainty analysis for Shanghai, China publication-title: Sci. Total Environ. doi: 10.1016/j.scitotenv.2008.01.038 contributor: fullname: Wang – volume: 9 start-page: 246 issue: 3 year: 2007 ident: 10.1016/j.scitotenv.2021.151633_bb0150 article-title: A land use regression model for predicting ambient fine particulate matter across Los Angeles, CA publication-title: J. Environ. Monit. doi: 10.1039/B615795E contributor: fullname: Moore – volume: 13 start-page: 1054 issue: 11 year: 2016 ident: 10.1016/j.scitotenv.2021.151633_bb0020 article-title: Ultrafine particle metrics and research considerations: review of the 2015 UFP workshop publication-title: Int. J. Environ. Res. Public Health doi: 10.3390/ijerph13111054 contributor: fullname: Baldauf – volume: 50 start-page: 12894 issue: 23 year: 2016 ident: 10.1016/j.scitotenv.2021.151633_bb0110 article-title: Comparison of ultrafine particle and black carbon concentration predictions from a mobile and short-term stationary land-use regression model publication-title: Environ. Sci. Technol. doi: 10.1021/acs.est.6b03476 contributor: fullname: Kerckhoffs – volume: 44 start-page: 2415 issue: 20 year: 2010 ident: 10.1016/j.scitotenv.2021.151633_bb0130 article-title: Understanding of regional air pollution over China using CMAQ, part I performance evaluation and seasonal variation publication-title: Atmos. Environ. doi: 10.1016/j.atmosenv.2010.03.035 contributor: fullname: Liu – volume: 11 start-page: 699 issue: 7 year: 1997 ident: 10.1016/j.scitotenv.2021.151633_bb0025 article-title: Mapping urban air pollution using GIS: a regression-based approach publication-title: Int. J. Geogr. Inf. Sci. doi: 10.1080/136588197242158 contributor: fullname: Briggs – volume: 488 start-page: 343 year: 2014 ident: 10.1016/j.scitotenv.2021.151633_bb0015 article-title: Land use regression models to estimate the annual and seasonal spatial variability of sulfur dioxide and particulate matter in Tehran, Iran publication-title: Sci. Total Environ. doi: 10.1016/j.scitotenv.2014.04.106 contributor: fullname: Amini – volume: 260 year: 2020 ident: 10.1016/j.scitotenv.2021.151633_bb0160 article-title: Relative contributions of a major international airport activities and other urban sources to the particle number concentrations (PNCs) at a nearby monitoring site publication-title: Environ. Pollut. doi: 10.1016/j.envpol.2020.114027 contributor: fullname: Pirhadi – volume: 113 start-page: 947 issue: 8 year: 2005 ident: 10.1016/j.scitotenv.2021.151633_bb0185 article-title: Exposure assessment for atmospheric ultrafine particles (UFPs) and implications in epidemiologic research publication-title: Environ. Health Perspect. doi: 10.1289/ehp.7939 contributor: fullname: Sioutas – volume: 208 start-page: 241 year: 2016 ident: 10.1016/j.scitotenv.2021.151633_bb0210 article-title: Characterizing the spatial distribution of ambient ultrafine particles in Toronto, Canada: a land use regression model publication-title: Environ. Pollut. doi: 10.1016/j.envpol.2015.04.011 contributor: fullname: Weichenthal – volume: 54 start-page: 657 year: 2012 ident: 10.1016/j.scitotenv.2021.151633_bb0165 article-title: Spatial distribution of ultrafine particles in urban settings: a land use regression model publication-title: Atmos. Environ. doi: 10.1016/j.atmosenv.2012.01.058 contributor: fullname: Rivera – volume: 3 year: 2013 ident: 10.1016/j.scitotenv.2021.151633_bb0080 contributor: fullname: HEI – volume: 3 start-page: 639 issue: 4 year: 2017 ident: 10.1016/j.scitotenv.2021.151633_bb0070 article-title: Evaluation euro IV of effectiveness in transportation systems of Tehran on air quality: application of IVE model publication-title: Pollution contributor: fullname: Ghadiri – volume: 110 start-page: 84 year: 2015 ident: 10.1016/j.scitotenv.2021.151633_bb0170 article-title: Development of a land-use regression model for ultrafine particles in Toronto, Canada publication-title: Atmos. Environ. doi: 10.1016/j.atmosenv.2015.02.018 contributor: fullname: Sabaliauskas – volume: 15 start-page: 1 issue: 1 year: 2016 ident: 10.1016/j.scitotenv.2021.151633_bb0060 article-title: Development of land use regression models for nitrogen dioxide, ultrafine particles, lung deposited surface area, and four other markers of particulate matter pollution in the swiss SAPALDIA regions publication-title: Environ. Health doi: 10.1186/s12940-016-0137-9 contributor: fullname: Eeftens – volume: 23 start-page: 44 year: 2006 ident: 10.1016/j.scitotenv.2021.151633_bb0195 article-title: Study on oil smoke pollution in the catering trade and treating situation (in Chinese) publication-title: J. Chongqing Technol. Bus. Univ. contributor: fullname: Wang – volume: 53 start-page: 7326 issue: 13 year: 2019 ident: 10.1016/j.scitotenv.2021.151633_bb0175 article-title: Urban ultrafine particle exposure assessment with land-use regression: influence of sampling strategy publication-title: Environ. Sci. Technol. doi: 10.1021/acs.est.9b02086 contributor: fullname: Saha – volume: 47 start-page: 5217 issue: 10 year: 2013 ident: 10.1016/j.scitotenv.2021.151633_bb0010 article-title: A land use regression model for ultrafine particles in Vancouver, Canada publication-title: Environ. Sci. Technol. doi: 10.1021/es304495s contributor: fullname: Abernethy – volume: 740 year: 2020 ident: 10.1016/j.scitotenv.2021.151633_bb0225 article-title: Development and transferability of ultrafine particle land use regression models in London publication-title: Sci. Total Environ. doi: 10.1016/j.scitotenv.2020.140059 contributor: fullname: Yang – volume: 205 start-page: 2231 year: 2017 ident: 10.1016/j.scitotenv.2021.151633_bb0045 article-title: Source strength of ultrafine and fine particle due to chinese cooking publication-title: Procedia Eng. doi: 10.1016/j.proeng.2017.10.062 contributor: fullname: Chen – volume: 2 start-page: 225 issue: 36 year: 2020 ident: 10.1016/j.scitotenv.2021.151633_bb0230 article-title: Design and application of real-time vehicle emission measurement information system in Shanghai publication-title: Environ. Monit. China contributor: fullname: Yi – volume: 95 start-page: 409 year: 2014 ident: 10.1016/j.scitotenv.2021.151633_bb0135 article-title: Aircraft engine exhaust emissions and other airport-related contributions to ambient air pollution: a review publication-title: Atmos. Environ. doi: 10.1016/j.atmosenv.2014.05.070 contributor: fullname: Masiol – volume: 699 year: 2020 ident: 10.1016/j.scitotenv.2021.151633_bb0100 article-title: Land use regression models for ultrafine particles, fine particles, and black carbon in Southern California publication-title: Sci. Total Environ. doi: 10.1016/j.scitotenv.2019.134234 contributor: fullname: Jones – volume: 44 start-page: 5035 issue: 39 year: 2010 ident: 10.1016/j.scitotenv.2021.151633_bb0120 article-title: A review of the characteristics of nanoparticles in the urban atmosphere and the prospects for developing regulatory controls publication-title: Atmos. Environ. doi: 10.1016/j.atmosenv.2010.08.016 contributor: fullname: Kumar – volume: 45 start-page: 622 issue: 2 year: 2011 ident: 10.1016/j.scitotenv.2021.151633_bb0095 article-title: Land use regression model for ultrafine particles in Amsterdam publication-title: Environ. Sci. Technol. doi: 10.1021/es1023042 contributor: fullname: Hoek – volume: 47 start-page: 11643 issue: 20 year: 2013 ident: 10.1016/j.scitotenv.2021.151633_bb0190 article-title: Using building heights and street configuration to enhance intraurban PM10, NOx, and NO2 land use regression models publication-title: Environ. Sci. Technol. doi: 10.1021/es402156g contributor: fullname: Tang – volume: 46 start-page: 11195 issue: 20 year: 2012 ident: 10.1016/j.scitotenv.2021.151633_bb0055 article-title: Development of land use regression models for PM2. 5, PM2. 5 absorbance, PM10 and PMcoarse in 20 European study areas; results of the ESCAPE project publication-title: Environ. Sci. Technol. doi: 10.1021/es301948k contributor: fullname: Eeftens – volume: 592 start-page: 306 year: 2017 ident: 10.1016/j.scitotenv.2021.151633_bb0125 article-title: Land use regression modelling of air pollution in high density high rise cities: a case study in Hong Kong publication-title: Sci. Total Environ. doi: 10.1016/j.scitotenv.2017.03.094 contributor: fullname: Lee – volume: 536 start-page: 150 year: 2015 ident: 10.1016/j.scitotenv.2021.151633_bb0075 article-title: Intra-urban variation of ultrafine particles as evaluated by process related land use and pollutant driven regression modelling publication-title: Sci. Total Environ. doi: 10.1016/j.scitotenv.2015.07.051 contributor: fullname: Ghassoun – volume: 71 start-page: 260 year: 2013 ident: 10.1016/j.scitotenv.2021.151633_bb0005 article-title: Emissions and indoor concentrations of particulate matter and its specific chemical components from cooking: a review publication-title: Atmos. Environ. doi: 10.1016/j.atmosenv.2013.01.061 contributor: fullname: Abdullahi |
SSID | ssj0000781 |
Score | 2.503096 |
Snippet | Little is currently known about long-term health effects of ambient ultrafine particles (UFPs) due to the lack of exposure assessment metrics suitable for use... |
SourceID | crossref pubmed elsevier |
SourceType | Aggregation Database Index Database Publisher |
StartPage | 151633 |
SubjectTerms | Air Pollutants - analysis Air Pollution - analysis China Environmental Monitoring Exposure assessment Land use regression model Particle Size Particulate Matter - analysis Purpose-designed monitoring network Spatial variation Ultrafine particles |
Title | High spatial resolution land-use regression model for urban ultrafine particle exposure assessment in Shanghai, China |
URI | https://dx.doi.org/10.1016/j.scitotenv.2021.151633 https://www.ncbi.nlm.nih.gov/pubmed/34785221 |
Volume | 816 |
hasFullText | 1 |
inHoldings | 1 |
isFullTextHit | |
isPrint | |
link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1LT-MwEB4h0EpICEF5v-TDHgkkjZ3Y3BACdbdaDgsIbpFfgSKUVm2C2Mv-dmbqtCwnDnuKZDlO5Bl7ZuzvmwH4LjiPjXQuUqXMIq58Gimf2YhMfSJ11xhLROFf11nvjv98EA8LcDHjwhCsst37w54-3a3bltN2Nk9HgwFxfLlUGWWfIa6JIkY5JdtCnT75-wHzoGQ24ZYZFzb2_oTxwnHrIfqmrxgodpMTtH5Zmn5hof4xP1drsNr6jew8_No6LPiqA99CJck_Hdi6_CCsYbd2xU46sBLO5VigG21AQ8AONiEcNfbDWLtVPUYIx6iZeGx7DNjYik3L5DB0a1kzNrpizUs91iX6pWzUzhHzb6MhHTIyPU_xyQYVu6Fz6Cc9OGbTAt2bcHd1eXvRi9rSC5FN86SOuhhFCPQkrOGeEsLbUubaWcu9yL1wmY6NMVrJ0tmSKxQpz61KLS-lFBojwHQLFqth5XeAOSeNQa9EOs85L0utROySTDsltPCZ24V4Nt3FKGTYKGbQs-diLqGCJFQECe3C2UwsxSdlKdAOfP3ydhDk_GspzyXqZrL3P8Puw3KXmBGUBjI-gMV63PhD9FdqczRVyCNYOv_R713Ts__7vv8OwlnvuA |
link.rule.ids | 315,783,787,4511,24130,27938,27939,45599,45693 |
linkProvider | Elsevier |
linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1LT9wwEB4tVAikqmqXZwvUB44Eko2d2L1VCLQtjwsgcYv8CmxVZVe7CYJLf3tn1tldOHHgak2cyDP2fON8MwNwIDiPjXQuUqXMIq58Gimf2YhcfSJ1zxhLicKXV1n_lv--E3cdOJnlwhCtsj37w5k-Pa3bkeN2NY9HgwHl-HKpMqo-Q7kmKl-CD5zqZ6FRH_1b8Dyomk34zYw7G8Vfkbxw4nqI4PQRI8VecoTuL0vTN1zUC_9z9hk-tcCR_Qzf9gU6vurCSmgl-dyFzdNFxhqKtVt20oWP4WKOhXyjdWiI2cEmRKRGOQy2W9tjRHGMmonHsftAjq3YtE8OQ1zLmrHRFWv-1mNdIjBlo3aRmH8aDemWkel5jU82qNg1XUQ_6MEhm3bo3oDbs9Obk37U9l6IbJonddTDMEIglLCGe6oIb0uZa2ct9yL3wmU6NsZoJUtnS65Qpzy3KrW8lFJoDAHTTViuhpXfBuacNAZhiXSec16WWonYJZl2SmjhM7cD8Wy5i1EosVHMuGd_irmGCtJQETS0Az9maileWUuBjuDth7eCIudvS3ku0TiTr--Z9jus9m8uL4qLX1fn32CtR2kSMdEFd2G5Hjd-D8FLbfanxvkf9rvvtA |
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=High+spatial+resolution+land-use+regression+model+for+urban+ultrafine+particle+exposure+assessment+in+Shanghai%2C+China&rft.jtitle=The+Science+of+the+total+environment&rft.au=Ge%2C+Yihui&rft.au=Fu%2C+Qingyan&rft.au=Yi%2C+Min&rft.au=Chao%2C+Yuan&rft.date=2022-04-10&rft.issn=0048-9697&rft.volume=816&rft.spage=151633&rft_id=info:doi/10.1016%2Fj.scitotenv.2021.151633&rft.externalDBID=n%2Fa&rft.externalDocID=10_1016_j_scitotenv_2021_151633 |
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 |