Intercomparison of Data Products for Studying Trends in PM 2.5 and Ozone Air Quality over Space and Time in China: Implications for Sustainable Air Quality Management

Shreya Guha () and Lucas R. F. Henneman
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Shreya Guha: Department of Civil, Environmental and Infrastructural Engineering, George Mason University, Fairfax, VA 22030, USA
Lucas R. F. Henneman: Department of Civil, Environmental and Infrastructural Engineering, George Mason University, Fairfax, VA 22030, USA

Sustainability, 2025, vol. 17, issue 22, 1-14

Abstract: Clean air is listed by the United Nations under several Sustainable Development Goals. Particulate matter (PM 2.5 ) and ground-level ozone (O 3 ) are pollutants with severe public health and environmental impacts. In China, multiple fine-scale datasets integrating ground monitors, satellites, and chemical transport models have been developed to estimate PM 2.5 and O 3 concentrations, but differences between the fine-scale datasets complicate applications in exposure and policy research. This study presents the first systematic intercomparison of five PM 2.5 datasets (V5.GL.03, Ma et al. 2021, Huang et al. 2021, CHAP, TAP) and two O 3 datasets (CHAP, TAP) from 2014 to 2023, evaluated against ground-based observations at national, regional, and provincial levels. We present both operational (single time point) and dynamic (change over time) evaluations to understand how model results compare with observations for each year, and quantify the performances of the models in assessing long term changes in air quality. Results show nationwide declines in PM 2.5 (by 22.1 µgm −3 ; regional range: 8.4–30.1 µgm −3 ) and O 3 (by 28.5 µgm −3 ; regional range: 19.3–34.3 µgm −3 ). Operational and dynamic evaluation shows that CHAP consistently has higher R 2 (greater than 0.7 in all regions) and lower errors (less than 3.7 µgm −3 in all regions) compared to other datasets across most years and regions for PM 2.5 . The same is true for TAP for O 3 (R 2 greater than 0.3 and ME less than 28.6 µgm −3 in all regions). However, the model performances vary spatially and temporally in alignment with several factors ranging from the number of observational monitors in a location, to recent changes in pollutant concentration levels, to extreme meteorological conditions. For example, higher predictive errors (>3.6 µgm −3 ) in operational evaluations are observed in all datasets for PM 2.5 in the sparsely monitored northwest region. Similarly, we find higher errors (ME > 28.5 µgm −3 ) in all O 3 datasets in the densely populated northern region, especially in the heavily industrialized Beijing–Tianjin–Hebei (BTH) area.

Keywords: operational evaluation; dynamic evaluation; PM 2.5; O 3 (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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