A new method for interpolation of missing air quality data at monitor stations
Studies in environmental fields often suffer from air quality datasets incomplete at certain places and times. Here, a Spatial-Temporal Point Interpolation based on Biased Sentinel Hospitals Areal Disease Estimation (STPI-BSHADE) interpolation method was introduced to address this issue. The method...
Saved in:
| Published in | Environment international Vol. 169; p. 107538 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Ltd
01.11.2022
Elsevier |
| Subjects | |
| Online Access | Get full text |
Cover
Loading…
| Abstract | Studies in environmental fields often suffer from air quality datasets incomplete at certain places and times. Here, a Spatial-Temporal Point Interpolation based on Biased Sentinel Hospitals Areal Disease Estimation (STPI-BSHADE) interpolation method was introduced to address this issue. The method was based on the spatial statistic trinity theory, where the statistical error is determined by the population properties, the condition of the sample, and the method of estimation. In our study, the spatial association of the variables was quantified by the covariance and the ratio of air quality data between stations, resulting in linear unbiased estimates of the missing data. STPI-BSHADE was compared with two widely used statistical methods, inverse distance weighting (IDW) and Kriging. Theoretically, IDW and Kriging are short of the capacity of using the heterogeneous characteristics of the population and remedying the sample bias. Empirically, the accuracy of the STPI-BSHADE method was assessed using hourly particulate matter 2.5 data, collected from May 13 to December 31, 2014, in the Beijing-Tianjin-Hebei areas, where air quality presents spatial heterogeneity. The experimental results also demonstrated that STPI-BSHADE significantly outperformed the traditional methods. |
|---|---|
| AbstractList | Studies in environmental fields often suffer from air quality datasets incomplete at certain places and times. Here, a Spatial-Temporal Point Interpolation based on Biased Sentinel Hospitals Areal Disease Estimation (STPI-BSHADE) interpolation method was introduced to address this issue. The method was based on the spatial statistic trinity theory, where the statistical error is determined by the population properties, the condition of the sample, and the method of estimation. In our study, the spatial association of the variables was quantified by the covariance and the ratio of air quality data between stations, resulting in linear unbiased estimates of the missing data. STPI-BSHADE was compared with two widely used statistical methods, inverse distance weighting (IDW) and Kriging. Theoretically, IDW and Kriging are short of the capacity of using the heterogeneous characteristics of the population and remedying the sample bias. Empirically, the accuracy of the STPI-BSHADE method was assessed using hourly particulate matter 2.5 data, collected from May 13 to December 31, 2014, in the Beijing-Tianjin-Hebei areas, where air quality presents spatial heterogeneity. The experimental results also demonstrated that STPI-BSHADE significantly outperformed the traditional methods. Studies in environmental fields often suffer from air quality datasets incomplete at certain places and times. Here, a Spatial-Temporal Point Interpolation based on Biased Sentinel Hospitals Areal Disease Estimation (STPI-BSHADE) interpolation method was introduced to address this issue. The method was based on the spatial statistic trinity theory, where the statistical error is determined by the population properties, the condition of the sample, and the method of estimation. In our study, the spatial association of the variables was quantified by the covariance and the ratio of air quality data between stations, resulting in linear unbiased estimates of the missing data. STPI-BSHADE was compared with two widely used statistical methods, inverse distance weighting (IDW) and Kriging. Theoretically, IDW and Kriging are short of the capacity of using the heterogeneous characteristics of the population and remedying the sample bias. Empirically, the accuracy of the STPI-BSHADE method was assessed using hourly particulate matter 2.5 data, collected from May 13 to December 31, 2014, in the Beijing-Tianjin-Hebei areas, where air quality presents spatial heterogeneity. The experimental results also demonstrated that STPI-BSHADE significantly outperformed the traditional methods.Studies in environmental fields often suffer from air quality datasets incomplete at certain places and times. Here, a Spatial-Temporal Point Interpolation based on Biased Sentinel Hospitals Areal Disease Estimation (STPI-BSHADE) interpolation method was introduced to address this issue. The method was based on the spatial statistic trinity theory, where the statistical error is determined by the population properties, the condition of the sample, and the method of estimation. In our study, the spatial association of the variables was quantified by the covariance and the ratio of air quality data between stations, resulting in linear unbiased estimates of the missing data. STPI-BSHADE was compared with two widely used statistical methods, inverse distance weighting (IDW) and Kriging. Theoretically, IDW and Kriging are short of the capacity of using the heterogeneous characteristics of the population and remedying the sample bias. Empirically, the accuracy of the STPI-BSHADE method was assessed using hourly particulate matter 2.5 data, collected from May 13 to December 31, 2014, in the Beijing-Tianjin-Hebei areas, where air quality presents spatial heterogeneity. The experimental results also demonstrated that STPI-BSHADE significantly outperformed the traditional methods. |
| ArticleNumber | 107538 |
| Author | Wang, Jinfeng Hu, Maogui Wang, Wei Xu, Chengdong |
| Author_xml | – sequence: 1 givenname: Chengdong surname: Xu fullname: Xu, Chengdong organization: State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China – sequence: 2 givenname: Jinfeng surname: Wang fullname: Wang, Jinfeng organization: State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China – sequence: 3 givenname: Maogui surname: Hu fullname: Hu, Maogui email: humg@lreis.ac.cn organization: State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China – sequence: 4 givenname: Wei surname: Wang fullname: Wang, Wei organization: State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China |
| BookMark | eNqNkb1OHDEUha2ISFlI3iCFS5pZ_O8ZCiSEgCChpElqy2PfIV7N2ovtBfH2GCY0KULkwtLV-Y7uPecQHcQUAaGvlKwpoepks4b4EGJdM8JYG2nJ-w9oRXvNO6UlOUCrJiOdoIx8QoelbAghTPRyhb6f4wiPeAv1d_J4Shk3H8i7NNsaUsRpwttQSoh32IaM7_d2DvUJe1stthVvUwy1QaW-ystn9HGyc4Evf_4j9Ovq8ufFt-72x_XNxflt50Qvase146Ok3oJjnI7eOw898WoYKeVtN62YlBSmfhTUM6cFGSYNgio7aCkU5UfoZvH1yW7MLoetzU8m2WBeBynfGZtrcDMYNQAHIkc1wiSUdSNVTHlhtR89AT40r-PFa5fT_R5KNe1iB_NsI6R9MUy3Hdvr5f9IKZMDG0STni5Sl1MpGSbjwhJSzTbMhhLz0p3ZmKU789KdWbprsPgLfjvxHexswaBF_xAgm-ICRAc-ZHC1ZRP-bfAMrKC3AA |
| CitedBy_id | crossref_primary_10_1016_j_measurement_2025_116649 crossref_primary_10_1109_ACCESS_2024_3424265 crossref_primary_10_1007_s10668_024_04781_5 crossref_primary_10_1016_j_apr_2024_102089 crossref_primary_10_1109_ACCESS_2024_3460187 crossref_primary_10_1016_j_atmosenv_2024_120388 crossref_primary_10_1080_24694452_2023_2289982 crossref_primary_10_1016_j_atmosres_2024_107754 crossref_primary_10_1061_JHTRBP_HZENG_1450 crossref_primary_10_1016_j_heliyon_2024_e32812 crossref_primary_10_3390_geographies3040033 crossref_primary_10_1007_s10596_023_10206_5 crossref_primary_10_1016_j_chemosphere_2022_137636 crossref_primary_10_1109_JIOT_2024_3422470 crossref_primary_10_3390_w15081635 crossref_primary_10_1007_s00521_024_09637_7 crossref_primary_10_1111_tgis_13141 crossref_primary_10_1080_24694452_2023_2206469 |
| Cites_doi | 10.1007/BF00890327 10.1016/S0022-1694(00)00144-X 10.1007/s11004-010-9284-7 10.1007/s11356-018-3763-7 10.1016/j.envres.2012.11.003 10.1016/j.envres.2019.05.025 10.1016/j.envsoft.2020.104835 10.1016/j.ecolind.2016.02.052 10.2113/gsecongeo.58.8.1246 10.1289/ehp.1205006 10.1016/j.envpol.2017.12.070 10.1289/ehp.7673 10.1175/JCLI-D-17-0150.1 10.1016/j.envpol.2020.114259 10.1016/j.rse.2017.12.018 10.1056/NEJMoa0803894 10.1164/rccm.201303-0609OC 10.1289/ehp.1307049 10.1155/2016/6295878 10.1038/s41586-020-1983-8 10.1016/j.spasta.2012.08.001 10.1175/JCLI-D-12-00633.1 10.1016/S0140-6736(17)30505-6 |
| ContentType | Journal Article |
| Copyright | 2022 The Authors Copyright © 2022 The Authors. Published by Elsevier Ltd.. All rights reserved. |
| Copyright_xml | – notice: 2022 The Authors – notice: Copyright © 2022 The Authors. Published by Elsevier Ltd.. All rights reserved. |
| DBID | 6I. AAFTH AAYXX CITATION 7X8 7S9 L.6 DOA |
| DOI | 10.1016/j.envint.2022.107538 |
| DatabaseName | ScienceDirect Open Access Titles Elsevier:ScienceDirect:Open Access CrossRef MEDLINE - Academic AGRICOLA AGRICOLA - Academic DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef MEDLINE - Academic AGRICOLA AGRICOLA - Academic |
| DatabaseTitleList | AGRICOLA MEDLINE - Academic |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering Public Health Environmental Sciences |
| EISSN | 1873-6750 |
| ExternalDocumentID | oai_doaj_org_article_69e3e05b6bef46acb1626d4a7dbd0e39 10_1016_j_envint_2022_107538 S0160412022004652 |
| GroupedDBID | --- --K --M .~1 0R~ 0SF 1B1 1RT 1~. 1~5 29G 4.4 457 4G. 53G 5GY 5VS 6I. 7-5 71M 8P~ 9JM AABNK AACTN AAEDT AAEDW AAFTH AAFWJ AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AAQXK AAXUO ABEFU ABFNM ABFYP ABJNI ABLST ABMAC ABXDB ABYKQ ACDAQ ACGFS ACRLP ADEZE ADMUD AEBSH AEKER AENEX AFKWA AFPKN AFTJW AFXIZ AGHFR AGUBO AGYEJ AHEUO AHHHB AIEXJ AIKHN AITUG AJBFU AJOXV AKIFW ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ ASPBG AVWKF AXJTR AZFZN BKOJK BLECG BLXMC CS3 DU5 EBS EFJIC EFLBG EJD EO8 EO9 EP2 EP3 F5P FDB FEDTE FGOYB FIRID FNPLU FYGXN G-2 G-Q GBLVA GROUPED_DOAJ HMC HVGLF HZ~ IHE J1W K-O KCYFY KOM LY9 M41 MO0 N9A NCXOZ O-L O9- OAUVE OK1 OZT P-8 P-9 P2P PC. Q38 R2- RIG RNS ROL RPZ SCC SDF SDG SDP SEN SES SEW SSJ SSZ T5K TN5 WUQ XPP ~02 ~G- AAHBH AATTM AAXKI AAYWO AAYXX ABWVN ACRPL ACVFH ADCNI ADNMO ADVLN AEGFY AEIPS AEUPX AFJKZ AFPUW AGCQF AGQPQ AGRNS AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP BNPGV CITATION SSH 7X8 7S9 EFKBS L.6 |
| ID | FETCH-LOGICAL-c484t-37c3b51daec231bddcde80d69b113000762551ef8b41d2c7409f7e416a9754613 |
| IEDL.DBID | DOA |
| ISSN | 0160-4120 1873-6750 |
| IngestDate | Wed Aug 27 01:31:19 EDT 2025 Fri Aug 22 20:22:22 EDT 2025 Fri Jul 11 07:59:38 EDT 2025 Tue Jul 01 02:38:17 EDT 2025 Thu Apr 24 22:53:34 EDT 2025 Fri Feb 23 02:38:28 EST 2024 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Sparse sample Interpolation Heterogeneous population Air quality dataset |
| Language | English |
| License | This is an open access article under the CC BY-NC-ND license. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c484t-37c3b51daec231bddcde80d69b113000762551ef8b41d2c7409f7e416a9754613 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| OpenAccessLink | https://doaj.org/article/69e3e05b6bef46acb1626d4a7dbd0e39 |
| PQID | 2721259294 |
| PQPubID | 23479 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_69e3e05b6bef46acb1626d4a7dbd0e39 proquest_miscellaneous_2723131385 proquest_miscellaneous_2721259294 crossref_citationtrail_10_1016_j_envint_2022_107538 crossref_primary_10_1016_j_envint_2022_107538 elsevier_sciencedirect_doi_10_1016_j_envint_2022_107538 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | November 2022 2022-11-00 20221101 2022-11-01 |
| PublicationDateYYYYMMDD | 2022-11-01 |
| PublicationDate_xml | – month: 11 year: 2022 text: November 2022 |
| PublicationDecade | 2020 |
| PublicationTitle | Environment international |
| PublicationYear | 2022 |
| Publisher | Elsevier Ltd Elsevier |
| Publisher_xml | – name: Elsevier Ltd – name: Elsevier |
| References | de Gruijter, ter Braak (b0030) 1990; 22 Jerrett, Burnett, Pope, Ito, Thurston, Krewski, Shi, Calle, Thun (b0090) 2009; 360 Requia, Coull, Koutrakis (b0125) 2019; 175 Haining (b0050) 2003 He, Christakos, Jankowski (b0070) 2019; 12 Xu, Wang, Li (b0170) 2018; 31 Lee, Serre, van Donkelaar, Martin, Burnett, Jerrett (b0100) 2012; 120 Burnett, Pope, Ezzati, Olives, Lim, Mehta, Shin, Singh, Hubbell, Brauer, Anderson, Smith, Balmes, Bruce, Kan, Laden, Pruss-Ustun, Michelle, Gapstur, Diver, Cohen (b0015) 2014; 122 Goovaerts (b0045) 2000; 228 Han, Fang, Li, Wang, Shi (b0055) 2020; 262 Gilliland, Avol, Kinney, Jerrett, Dvonch, Lurmann, Buckley, Breysse, Keeler, de Villiers, McConnell (b0040) 2005; 113 Wang, Stein, Gao, Ge (b0150) 2012; 2 Hu, Waller, Al-Hamdan, Crosson, Estes, Estes, Quattrochi, Sarnat, Liu (b0080) 2013; 121 Wang, Gao, Stein (b0145) 2020; 134 Xu, Wang, Hu, Li (b0165) 2013; 26 Dedoussi, Eastham, Monier, Barrett (b0035) 2020; 578 Bai, Li, Chang, Gao (b0005) 2019; 254 Chu, Bilal (b0020) 2019; 26 Berrocal, Guan, Muyskens, Wang, Reich, Mulholland, Chang (b0010) 2020; 222 Cohen, Brauer, Burnett, Anderson, Frostad, Estep, Balakrishnan, Brunekreef, Dandona, Dandona, Feigin, Freedman, Hubbell, Jobling, Kan, Knibbs, Liu, Martin, Morawska, Pope, Shin, Straif, Shaddick, Thomas, van Dingenen, van Donkelaar, Vos, Murray, Forouzanfar (b0025) 2017; 389 Wang, Zhang, Fu (b0160) 2016; 67 Matheron (b0120) 1963; 58 Li, T., Wang, H., Zhao, T., Xue, M., Wang, Y., Che, H., Jiang, C., 2016. The Impacts of Different PBL Schemes on the Simulation of PM2.5 during Severe Haze Episodes in the Jing-Jin-Ji Region and Its Surroundings in China. Adv Meteorol,2016. Jerrett, Burnett, Beckerman, Turner, Krewski, Thurston, Martin, van Donkelaar, Hughes, Shi, Gapstur, Thun, Pope (b0095) 2013; 188 Liu, Cao, Zhao, Mulligan, Ye (b0115) 2018; 235 He, Huang (b0075) 2018; 206 Wang, Xue, Zhang (b0155) 2016; 39 Harris, Fotheringham, Crespo, Charlton (b0060) 2010; 42 Gilliland (10.1016/j.envint.2022.107538_b0040) 2005; 113 Wang (10.1016/j.envint.2022.107538_b0145) 2020; 134 Dedoussi (10.1016/j.envint.2022.107538_b0035) 2020; 578 de Gruijter (10.1016/j.envint.2022.107538_b0030) 1990; 22 Bai (10.1016/j.envint.2022.107538_b0005) 2019; 254 Han (10.1016/j.envint.2022.107538_b0055) 2020; 262 Requia (10.1016/j.envint.2022.107538_b0125) 2019; 175 Jerrett (10.1016/j.envint.2022.107538_b0095) 2013; 188 Jerrett (10.1016/j.envint.2022.107538_b0090) 2009; 360 Goovaerts (10.1016/j.envint.2022.107538_b0045) 2000; 228 Hu (10.1016/j.envint.2022.107538_b0080) 2013; 121 He (10.1016/j.envint.2022.107538_b0075) 2018; 206 Harris (10.1016/j.envint.2022.107538_b0060) 2010; 42 Wang (10.1016/j.envint.2022.107538_b0155) 2016; 39 Matheron (10.1016/j.envint.2022.107538_b0120) 1963; 58 Burnett (10.1016/j.envint.2022.107538_b0015) 2014; 122 Liu (10.1016/j.envint.2022.107538_b0115) 2018; 235 Wang (10.1016/j.envint.2022.107538_b0150) 2012; 2 Xu (10.1016/j.envint.2022.107538_b0165) 2013; 26 Lee (10.1016/j.envint.2022.107538_b0100) 2012; 120 10.1016/j.envint.2022.107538_b0105 Berrocal (10.1016/j.envint.2022.107538_b0010) 2020; 222 Chu (10.1016/j.envint.2022.107538_b0020) 2019; 26 Cohen (10.1016/j.envint.2022.107538_b0025) 2017; 389 Haining (10.1016/j.envint.2022.107538_b0050) 2003 He (10.1016/j.envint.2022.107538_b0070) 2019; 12 Wang (10.1016/j.envint.2022.107538_b0160) 2016; 67 Xu (10.1016/j.envint.2022.107538_b0170) 2018; 31 |
| References_xml | – volume: 39 start-page: 34 year: 2016 end-page: 42 ident: b0155 article-title: Analysis of Spatial-temporal Distribution Characteristics and Main Cause of Air Pollution in Beijing-Tianjin-Hebei region in 2014 publication-title: Meteorological and Environmental Sciences. – reference: Li, T., Wang, H., Zhao, T., Xue, M., Wang, Y., Che, H., Jiang, C., 2016. The Impacts of Different PBL Schemes on the Simulation of PM2.5 during Severe Haze Episodes in the Jing-Jin-Ji Region and Its Surroundings in China. Adv Meteorol,2016. – volume: 26 start-page: 1902 year: 2019 end-page: 1910 ident: b0020 article-title: PM2.5 mapping using integrated geographically temporally weighted regression (GTWR) and random sample consensus (RANSAC) models publication-title: Environ Sci Pollut R. – volume: 113 start-page: 1447 year: 2005 end-page: 1454 ident: b0040 article-title: Air pollution exposure assessment for epidemiologic studies of pregnant women and children: Lessons learned from the Centers for Children's Environmental Health and Disease Prevention Research publication-title: Environ Health Persp. – volume: 262 year: 2020 ident: b0055 article-title: Heterogeneity of influential factors across the entire air quality spectrum in Chinese cities: A spatial quantile regression analysis publication-title: Environ. Pollut. – volume: 228 start-page: 113 year: 2000 end-page: 129 ident: b0045 article-title: Geostatistical approaches for incorporating elevation into the spatial interpolation of rainfall publication-title: J. Hydrol. – volume: 22 start-page: 407 year: 1990 end-page: 415 ident: b0030 article-title: Model-free estimation from spatial samples: a reappraisal of classical sampling theory publication-title: Math. Geol. – year: 2003 ident: b0050 article-title: Spatial Data Analysis: Theory and Practice – volume: 578 start-page: 261 year: 2020 ident: b0035 article-title: Premature mortality related to United States cross-state air pollution publication-title: Nature – volume: 2 start-page: 1 year: 2012 end-page: 14 ident: b0150 article-title: A review of spatial sampling publication-title: Spatial Statistics. – volume: 175 start-page: 421 year: 2019 end-page: 433 ident: b0125 article-title: Evaluation of predictive capabilities of ordinary geostatistical interpolation, hybrid interpolation, and machine learning methods for estimating PM2.5 constituents over space publication-title: Environ. Res. – volume: 360 start-page: 1085 year: 2009 end-page: 1095 ident: b0090 article-title: Long-Term Ozone Exposure and Mortality publication-title: New Engl J Med. – volume: 42 start-page: 657 year: 2010 end-page: 680 ident: b0060 article-title: The Use of Geographically Weighted Regression for Spatial Prediction: An Evaluation of Models Using Simulated Data Sets publication-title: Math. Geosci. – volume: 122 start-page: 397 year: 2014 end-page: 403 ident: b0015 article-title: An Integrated Risk Function for Estimating the Global Burden of Disease Attributable to Ambient Fine Particulate Matter Exposure publication-title: Environ Health Persp. – volume: 254 year: 2019 ident: b0005 article-title: Advancing the prediction accuracy of satellite-based PM2.5 concentration mapping: A perspective of data mining through in situ PM2.5 measurements publication-title: Environ. Pollut. – volume: 31 start-page: 1757 year: 2018 end-page: 1770 ident: b0170 article-title: A New Method for Temperature Spatial Interpolation Based on Sparse Historical Stations publication-title: J Climate. – volume: 134 year: 2020 ident: b0145 article-title: The spatial statistic trinity: A generic framework for spatial sampling and inference publication-title: Environ Modell Softw. – volume: 26 start-page: 7452 year: 2013 end-page: 7463 ident: b0165 article-title: Interpolation of missing temperature data at meteorological stations using P-BSHADE publication-title: J Climate. – volume: 12 start-page: 1734 year: 2019 end-page: 1747 ident: b0070 article-title: Comparative Performance of the LUR, ANN, and BME Techniques in the Multiscale Spatiotemporal Mapping of PM2.5 Concentrations in North China publication-title: Ieee J-Stars. – volume: 235 start-page: 272 year: 2018 end-page: 282 ident: b0115 article-title: Improve ground-level PM2.5 concentration mapping using a random forests-based geostatistical approach publication-title: Environ. Pollut. – volume: 188 start-page: 593 year: 2013 end-page: 599 ident: b0095 article-title: Spatial Analysis of Air Pollution and Mortality in California publication-title: Am J Resp Crit Care. – volume: 222 year: 2020 ident: b0010 article-title: A comparison of statistical and machine learning methods for creating national daily maps of ambient PM2.5 concentration publication-title: Atmos. Environ. – volume: 120 start-page: 1727 year: 2012 end-page: 1732 ident: b0100 article-title: Comparison of Geostatistical Interpolation and Remote Sensing Techniques for Estimating Long-Term Exposure to Ambient PM2.5 Concentrations across the Continental United States publication-title: Environ Health Persp. – volume: 121 start-page: 1 year: 2013 end-page: 10 ident: b0080 article-title: Estimating ground-level PM2.5 concentrations in the southeastern US using geographically weighted regression publication-title: Environ. Res. – volume: 58 start-page: 1246 year: 1963 end-page: 1266 ident: b0120 publication-title: Principles of geostatistics. Economic geology. – volume: 389 start-page: 1907 year: 2017 end-page: 1918 ident: b0025 article-title: Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: an analysis of data from the Global Burden of Diseases Study publication-title: Lancet – volume: 67 start-page: 250 year: 2016 end-page: 256 ident: b0160 article-title: A measure of spatial stratified heterogeneity publication-title: Ecol. Ind. – volume: 206 start-page: 72 year: 2018 end-page: 83 ident: b0075 article-title: Satellite-based mapping of daily high-resolution ground PM2.5 in China via space-time regression modeling publication-title: Remote Sens. Environ. – volume: 39 start-page: 34 year: 2016 ident: 10.1016/j.envint.2022.107538_b0155 article-title: Analysis of Spatial-temporal Distribution Characteristics and Main Cause of Air Pollution in Beijing-Tianjin-Hebei region in 2014 publication-title: Meteorological and Environmental Sciences. – volume: 22 start-page: 407 year: 1990 ident: 10.1016/j.envint.2022.107538_b0030 article-title: Model-free estimation from spatial samples: a reappraisal of classical sampling theory publication-title: Math. Geol. doi: 10.1007/BF00890327 – volume: 228 start-page: 113 year: 2000 ident: 10.1016/j.envint.2022.107538_b0045 article-title: Geostatistical approaches for incorporating elevation into the spatial interpolation of rainfall publication-title: J. Hydrol. doi: 10.1016/S0022-1694(00)00144-X – volume: 42 start-page: 657 year: 2010 ident: 10.1016/j.envint.2022.107538_b0060 article-title: The Use of Geographically Weighted Regression for Spatial Prediction: An Evaluation of Models Using Simulated Data Sets publication-title: Math. Geosci. doi: 10.1007/s11004-010-9284-7 – volume: 26 start-page: 1902 year: 2019 ident: 10.1016/j.envint.2022.107538_b0020 article-title: PM2.5 mapping using integrated geographically temporally weighted regression (GTWR) and random sample consensus (RANSAC) models publication-title: Environ Sci Pollut R. doi: 10.1007/s11356-018-3763-7 – volume: 121 start-page: 1 year: 2013 ident: 10.1016/j.envint.2022.107538_b0080 article-title: Estimating ground-level PM2.5 concentrations in the southeastern US using geographically weighted regression publication-title: Environ. Res. doi: 10.1016/j.envres.2012.11.003 – volume: 175 start-page: 421 year: 2019 ident: 10.1016/j.envint.2022.107538_b0125 article-title: Evaluation of predictive capabilities of ordinary geostatistical interpolation, hybrid interpolation, and machine learning methods for estimating PM2.5 constituents over space publication-title: Environ. Res. doi: 10.1016/j.envres.2019.05.025 – volume: 134 year: 2020 ident: 10.1016/j.envint.2022.107538_b0145 article-title: The spatial statistic trinity: A generic framework for spatial sampling and inference publication-title: Environ Modell Softw. doi: 10.1016/j.envsoft.2020.104835 – volume: 222 issue: 14 year: 2020 ident: 10.1016/j.envint.2022.107538_b0010 article-title: A comparison of statistical and machine learning methods for creating national daily maps of ambient PM2.5 concentration publication-title: Atmos. Environ. – volume: 67 start-page: 250 year: 2016 ident: 10.1016/j.envint.2022.107538_b0160 article-title: A measure of spatial stratified heterogeneity publication-title: Ecol. Ind. doi: 10.1016/j.ecolind.2016.02.052 – volume: 58 start-page: 1246 year: 1963 ident: 10.1016/j.envint.2022.107538_b0120 publication-title: Principles of geostatistics. Economic geology. doi: 10.2113/gsecongeo.58.8.1246 – volume: 120 start-page: 1727 year: 2012 ident: 10.1016/j.envint.2022.107538_b0100 article-title: Comparison of Geostatistical Interpolation and Remote Sensing Techniques for Estimating Long-Term Exposure to Ambient PM2.5 Concentrations across the Continental United States publication-title: Environ Health Persp. doi: 10.1289/ehp.1205006 – volume: 235 start-page: 272 year: 2018 ident: 10.1016/j.envint.2022.107538_b0115 article-title: Improve ground-level PM2.5 concentration mapping using a random forests-based geostatistical approach publication-title: Environ. Pollut. doi: 10.1016/j.envpol.2017.12.070 – volume: 113 start-page: 1447 year: 2005 ident: 10.1016/j.envint.2022.107538_b0040 article-title: Air pollution exposure assessment for epidemiologic studies of pregnant women and children: Lessons learned from the Centers for Children's Environmental Health and Disease Prevention Research publication-title: Environ Health Persp. doi: 10.1289/ehp.7673 – volume: 31 start-page: 1757 year: 2018 ident: 10.1016/j.envint.2022.107538_b0170 article-title: A New Method for Temperature Spatial Interpolation Based on Sparse Historical Stations publication-title: J Climate. doi: 10.1175/JCLI-D-17-0150.1 – volume: 262 year: 2020 ident: 10.1016/j.envint.2022.107538_b0055 article-title: Heterogeneity of influential factors across the entire air quality spectrum in Chinese cities: A spatial quantile regression analysis publication-title: Environ. Pollut. doi: 10.1016/j.envpol.2020.114259 – volume: 206 start-page: 72 year: 2018 ident: 10.1016/j.envint.2022.107538_b0075 article-title: Satellite-based mapping of daily high-resolution ground PM2.5 in China via space-time regression modeling publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2017.12.018 – volume: 360 start-page: 1085 year: 2009 ident: 10.1016/j.envint.2022.107538_b0090 article-title: Long-Term Ozone Exposure and Mortality publication-title: New Engl J Med. doi: 10.1056/NEJMoa0803894 – volume: 12 start-page: 1734 year: 2019 ident: 10.1016/j.envint.2022.107538_b0070 article-title: Comparative Performance of the LUR, ANN, and BME Techniques in the Multiscale Spatiotemporal Mapping of PM2.5 Concentrations in North China publication-title: Ieee J-Stars. – volume: 188 start-page: 593 year: 2013 ident: 10.1016/j.envint.2022.107538_b0095 article-title: Spatial Analysis of Air Pollution and Mortality in California publication-title: Am J Resp Crit Care. doi: 10.1164/rccm.201303-0609OC – volume: 122 start-page: 397 year: 2014 ident: 10.1016/j.envint.2022.107538_b0015 article-title: An Integrated Risk Function for Estimating the Global Burden of Disease Attributable to Ambient Fine Particulate Matter Exposure publication-title: Environ Health Persp. doi: 10.1289/ehp.1307049 – ident: 10.1016/j.envint.2022.107538_b0105 doi: 10.1155/2016/6295878 – volume: 254 issue: 10 year: 2019 ident: 10.1016/j.envint.2022.107538_b0005 article-title: Advancing the prediction accuracy of satellite-based PM2.5 concentration mapping: A perspective of data mining through in situ PM2.5 measurements publication-title: Environ. Pollut. – volume: 578 start-page: 261 year: 2020 ident: 10.1016/j.envint.2022.107538_b0035 article-title: Premature mortality related to United States cross-state air pollution publication-title: Nature doi: 10.1038/s41586-020-1983-8 – year: 2003 ident: 10.1016/j.envint.2022.107538_b0050 – volume: 2 start-page: 1 year: 2012 ident: 10.1016/j.envint.2022.107538_b0150 article-title: A review of spatial sampling publication-title: Spatial Statistics. doi: 10.1016/j.spasta.2012.08.001 – volume: 26 start-page: 7452 year: 2013 ident: 10.1016/j.envint.2022.107538_b0165 article-title: Interpolation of missing temperature data at meteorological stations using P-BSHADE publication-title: J Climate. doi: 10.1175/JCLI-D-12-00633.1 – volume: 389 start-page: 1907 issue: 10082 year: 2017 ident: 10.1016/j.envint.2022.107538_b0025 article-title: Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: an analysis of data from the Global Burden of Diseases Study publication-title: Lancet doi: 10.1016/S0140-6736(17)30505-6 |
| SSID | ssj0002485 |
| Score | 2.480508 |
| Snippet | Studies in environmental fields often suffer from air quality datasets incomplete at certain places and times. Here, a Spatial-Temporal Point Interpolation... |
| SourceID | doaj proquest crossref elsevier |
| SourceType | Open Website Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 107538 |
| SubjectTerms | air quality Air quality dataset covariance data collection environment Heterogeneous population Interpolation kriging particulates Sparse sample spatial variation |
| SummonAdditionalLinks | – databaseName: Elsevier SD Freedom Collection dbid: .~1 link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3PS-tAEB7EkyKiVXn1-WQFr7HJZpNNjr6iFEEvKnhb9lck8kylrQcv_u1vJpuo9aAgPWWZpCEzmf0mO9-3AMd5yaVNeBXJyqWR0EJGhlPVanxmEDGUjhPf-fIqn9yKi7vsbgXGPReG2iq73B9yeputu5FR9zRHT3U9uiZtNJFwoopikZdRHhZCUpSfvL63eZBkV9D3jiOy7ulzbY8Xkcka6qjkHIcQuRdL01Or4r80S33K1-0kdL4Fmx16ZKfhBrdhxTcDWP-gKTiAvbN36hqadu_ufAAb4QsdC8SjHbg6ZQipWdhCmiF2ZXXYcyt0x7FpxTAG6FMC0_WMBfblC6OWUqYX7LFNBjM2D2v58124PT-7GU-ibneFyIpCLDCz2BS94bS3iPGMc9b5InZ5aRJa4ooxSyKa8lVhROK4lVgIVtIjftOlzASigD1YbaaN_wUMYZzhFAyZpXXW1CDs9CIXnpNamxZDSPuHqmwnPU47YPxTfY_ZgwquUOQKFVwxhOjtrKcgvfGN_V_y15stCWe3A9PZveoiR-WlT32cmdz4SuTamgQrOie0dMbFPi2HIHtvq6U4xEvV3_z9UR8cCt1D6y668dPnueJYZWOVyUvxpU2a4K_I9n98B79hjY4CU_IAVhezZ_8HIdPCHLbvxH-wSROD priority: 102 providerName: Elsevier |
| Title | A new method for interpolation of missing air quality data at monitor stations |
| URI | https://dx.doi.org/10.1016/j.envint.2022.107538 https://www.proquest.com/docview/2721259294 https://www.proquest.com/docview/2723131385 https://doaj.org/article/69e3e05b6bef46acb1626d4a7dbd0e39 |
| Volume | 169 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1LT8MwDLZ4XEAIwQAxHlOQuBbaNH0dBwINEDuBxC3Kq9IQdGgbBy78duym5XVgF9RblD7t2J8b-zPAcVrwzES8DLLSxoFQIgs0p6hVu0QjYigsp3rn22E6uBfXD8nDt1ZflBPm6YH9hztNCxe7MNGpdqVIldERQnArVGa1DV1cl-6hz2uDqcYGE1GXZ_UOAxHxsC2aqzO7qISsojxKznEI8Xr-wynV3P0_fNMvK127nssNWG8wI-v7Z92EBVd1YPUbk2AHdi6-CtZwarNipx1Y8__lmC832oJhnyGQZr5xNEPEyka-05bPiWPjkqHk6QcCU6MJ8zWXb4wSSZmasefaBEzY1O_gT7fh_vLi7nwQND0VAiNyMUN7YmKUgVXOILLT1hrr8tCmhY5oYytE24gYypW5FpHlJsPwr8wcojZVZIlA378DS9W4crvAELxpTiqQGNpdjTWCTSdS4ThxtCnRhbj9qNI0hOPU9-JJtpllj9KLQpIopBdFF4LPs1484cac-Wckr8-5RJddD6ASyUaJ5Dwl6kLWSls2yMMjCrzUaM7tj1rlkCge2m1RlRu_TiXH2BpjS16IP-fEER55svcfr7EPK_RovlTyAJZmk1d3iJhppnuwePIe9WC5f3UzGPbqxfIB5F8WNg |
| linkProvider | Directory of Open Access Journals |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1LT9wwEB6h5QBVhWAL6hZaXKnXaBPHeR23CLQU2AsgcbP8SpWqzaLd5dB_35nY2bI9FAnl5jgPZSbjb-z5PgN8yStemITXUVHbNBJKFJHmlLVql2lEDJXlxHe-meXTe_HtIXvYgrOeC0NllSH2-5jeRevQMg5fc_zYNONb0kYTCSeqKCZ5GcbhbVKnygawPbm8ms7WAZlUu7zEdxzRBT2DrivzIj5ZS0WVnGMTgvdyY4TqhPw3Bqp_QnY3Dl3sw14AkGzi3_EAtlw7hDfPZAWHcHT-l72GXcPvuxzCWz9Jxzz36B3MJgxRNfO7SDOEr6zx2275Ajk2rxm6Ac0mMNUsmCdg_mZUVcrUiv3q4sGCLf1y_vIQ7i_O786mUdhgITKiFCsMLiZFg1jlDMI8ba2xroxtXumEVrliDJQIqFxdapFYbgrMBevCIYRTVZEJBAJHMGjnrXsPDJGc5uQPmaGl1lQj8nQiF46TYJsSI0j7jypNUB-nTTB-yr7M7If0ppBkCulNMYJofdWjV994of9Xste6L2lndw3zxXcZnEfmlUtdnOlcu1rkyugEkzorVGG1jV1ajaDorS03XBFv1bzw-M-9c0g0Dy29qNbNn5aSY6KNiSavxH_7pAkeZfbh1W9wCjvTu5treX05uzqGXTrjiZMnMFgtntxHRFAr_Sn8IX8AwhQXwg |
| 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+method+for+interpolation+of+missing+air+quality+data+at+monitor+stations&rft.jtitle=Environment+international&rft.au=Xu%2C+Chengdong&rft.au=Wang%2C+Jinfeng&rft.au=Hu%2C+Maogui&rft.au=Wang%2C+Wei&rft.date=2022-11-01&rft.issn=0160-4120&rft.volume=169+p.107538-&rft_id=info:doi/10.1016%2Fj.envint.2022.107538&rft.externalDBID=NO_FULL_TEXT |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0160-4120&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0160-4120&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0160-4120&client=summon |