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Assessments of spatiotemporal fine particulate matter (PM2.5) concentrations are crucial for establishing risk maps and maintaining human health. This study spatiotemporally assessed PM2.5 concentrations in Taiwan by using multivariate indicator kriging (MVIK) according to current Taiwanese and US regulatory standards for annual average PM2.5 concentrations (15 and 12 μg/m3, respectively). First, multivariate integration was implemented to analyze data on PM2.5 concentrations for 2019–2021 and 2020–2022 because of no statistical difference of the 3-year PM2.5 data. MVIK was then used for modeling probabilities according to the two standards. Finally, quantile estimates on the basis of the occurrence probabilities of analyzing PM2.5 concentrations were employed to determine the optimal classifications for establishing risk maps according to the two PM2.5 standards. The study results indicated that the multivariate integration of temporal PM2.5 data in MVIK can effectively streamline the analytic process. The multivariate integration of 3-year PM2.5 data was suitable for assessing the risk categories of the regulatory standards for annual average PM2.5. The greatest estimated difference between the 2019–2021 and 2020–2022 multivariate integrations was in the Northern and Chumiao air quality regions. Because many air quality regions were in the PM2.5 categories of exceeding 12 μg/m3, the regulatory standard for annual average PM2.5 of 12 μg/m3 was inappropriate in Taiwan at this point in time according to assessing the 3-year spatiotemporal variability of PM2.5 concentrations. [ABSTRACT FROM AUTHOR]