{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:54:29Z","timestamp":1760147669932,"version":"build-2065373602"},"reference-count":52,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2023,2,21]],"date-time":"2023-02-21T00:00:00Z","timestamp":1676937600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea (NRF)","doi-asserted-by":"publisher","award":["2021R1I1A1A01060652"],"award-info":[{"award-number":["2021R1I1A1A01060652"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In recent decades, air pollution in Ulaanbaatar has become a challenge regarding the health of the citizens of Ulaanbaatar, due to coal combustion in the ger area. Households burn fuel for cooking and to warm their houses in the morning and evening. This creates a difference between daytime and nighttime air pollution levels. The accurate mapping of air pollution and assessment of exposure to air pollution have thus become important study objects for researchers. The city center is where most air quality monitoring stations are located, but they are unable to monitor every residential region, particularly the ger area, which is where most particulate matter pollution originates. Due to this circumstance, it is difficult to construct an LUR model for the entire capital city\u2019s residential region. This study aims to map peak PM2.5 dispersion during the day using the Linear and Nonlinear Land Use Regression (LUR) model (Multi-Linear Regression Model (MLRM) and Generalized Additive Model (GAM)) for Ulaanbaatar, with monitoring station measurements and mobile device (DUST TRUK II) measurements. LUR models are frequently used to map small-scale spatial variations in element levels for various types of air pollution, based on measurements and geographical predictors. PM2.5 measurement data were collected and analyzed in the R statistical software and ArcGIS. The results showed the dispersion map MLRM R2 = 0.84, adjusted R2 = 0.83, RMSE = 53.25 \u00b5g\/m3 and GAM R2 = 0.89, and adjusted R2 = 0.87, RMSE = 44 \u00b5g\/m3. In order to validate the models, the LOOCV technique was run on both the MLRM and GAM. Their performance was also high, with LOOCV R2 = 0.83, RMSE = 55.6 \u00b5g\/m3, MAE = 38.7 \u00b5g\/m3, and GAM LOOCV R2 = 0.77, RMSE = 65.5 \u00b5g\/m3, MAE = 47.7 \u00b5g\/m3. From these results, the LUR model\u2019s performance is high, especially the GAM model, which works better than MRLM.<\/jats:p>","DOI":"10.3390\/rs15051174","type":"journal-article","created":{"date-parts":[[2023,2,22]],"date-time":"2023-02-22T01:39:47Z","timestamp":1677029987000},"page":"1174","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Linear and Nonlinear Land Use Regression Approach for Modelling PM2.5 Concentration in Ulaanbaatar, Mongolia during Peak Hours"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9361-0753","authenticated-orcid":false,"given":"Odbaatar","family":"Enkhjargal","sequence":"first","affiliation":[{"name":"Department of Environmental Science, School of Environmental Science and Engineering, Tianjin University, Tianjin 300350, China"},{"name":"Division of Physical Geography and Environmental Study, Institute of Geography and Geoecology, Mongolian Academy of Sciences, Ulaanbaatar 15170, Mongolia"}]},{"given":"Munkhnasan","family":"Lamchin","sequence":"additional","affiliation":[{"name":"Ojeong Eco-Resilience Institute (OJERI), Korea University, 145, Anam-Ro, Seongbuk Gu, Seoul 02841, Republic of Korea"},{"name":"Department of Environment and Forest Engineering, School of Engineering and Applied Scences, Institute for Sustainable Development, National University of Mongolia, Ulaanbaatar 14201, Mongolia"}]},{"given":"Jonathan","family":"Chambers","sequence":"additional","affiliation":[{"name":"United Nations Organization Stabilization Mission in the Democratic Republic of the Congo (MONUSCO), 12 Avenue des Aviateurs, Gombe, Kinshasa BP 8811, Congo"}]},{"given":"Xue-Yi","family":"You","sequence":"additional","affiliation":[{"name":"Department of Environmental Science, School of Environmental Science and Engineering, Tianjin University, Tianjin 300350, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1111\/cag.12279","article-title":"A description of methods for deriving air pollution land use regression model predictor variables from remote sensing data in Ulaanbaatar, Mongolia","volume":"60","author":"Yuchi","year":"2016","journal-title":"Can. 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