Development of season-dependent land use regression models to estimate BC and PM1 exposure

Reliable estimation of exposure to black carbon (BC) and sub-micrometer particles (PM1) within a city is challenging because of limited monitoring data as well as the lack of models suitable for assessing the intra-urban environment. In this study, to estimate exposure levels in the inner-city area, we developed land use regression (LUR) models for BC and PM1 based on specially designed mobile monitoring surveys conducted in 2019 and 2020 for three seasons. The daytime and nighttime LUR models were developed separately to capture additional details on the variation in pollutants. The results of mobile monitoring indicated similar temporal variation characteristics of BC and PM1. The mean concentrations of pollutants were higher in winter (BC: 4.72 pg/m(3); PM1: 56.97 mu g/m(3)) than in fall (BC: 3.74 mu g/m(3); PM1: 33.29 mu g/m(3)) and summer (BC: 2.77 mu g/m(3); PM1: 27.04 pg/m3). For both BC and PM1, higher nighttime concentrations were found in winter and fall, whereas higher daytime concentrations were observed in the summer. A supervised forward stepwise regression method was used to select the predictors for the LUR models. The adjusted R-2 of the LUR models for BC and PM1 ranged from 0.39 to 0.66 and 0.45 to 0.80, respectively. Traffic related predictors were incorporated into all the models for BC. In contrast, more meteorology-related predictors were incorporated into the PM1 models. The concentration surface based on the LUR models was mapped at a spatial resolution of 100 m, and significant seasonal and diurnal trends were observed. PM1 was dominated by seasonal variations, whereas BC showed more spatial variation. In conclusion, the development of season-dependent diurnal LUR models based on mobile monitoring could provide a methodology for the estimation of exposure and screening of influencing factors of BC and PM1 in typical inner-city environments, and support pollution management. (C) 2021 Elsevier B.V. All rights reserved.