The Chinese government, as a policy response, has continued to invest efforts and resources to implement cost-effective air pollution control technologies and stringent regulation to reduce emissions from the most contributing sectors to protect the environment and public health. The higher density of monitoring stations (> 1600) distributed across China provides a timely opportunity to use them to study in detail the national pollution trends in light of more stringent air pollution control policies. In the present study, air quality datasets comprising hourly concentrations of PM2.5, O3, NO2, and SO2 collected across 1309, 1341, 1289, and 1347 monitoring stations respectively were obtained from the National Environmental Monitoring Centre over 4 years (2015-2018) and trend analysis was performed. Results indicate that the overall annual trends for PM2.5 and SO2 were - 2.9 ± 2.7 and - 3.2 ± 3.2 μg/m3/year, while the winter trends were - 4.8 ± 5.8 and - 6.9 ± 8.4 μg/m3/year respectively across China. The daily maximum 8-h average (DMA8) ozone concentration showed a significant positive trend of 2.4 ± 4.6 μg/m3/year, which was comparatively higher in summer at 4.4 ± 9.0 μg/m3/year. On the other side, NO2 trend is not great in number (- 0.45 ± 2.0 μg/m3/year). Overall, 62.2%, 61.8%, and 20.9% of PM2.5, SO2, and NO2 monitoring stations were associated with a negative trend of ≥ - 2 μg/m3/year. For O3 DMA8 concentrations, 50.7% of the monitoring stations showed a significant positive trend of ≥ 2 μg/m3/year. In light of the Chinese government's increasing impetus on combating air pollution and climate change via new policy regulations, it is important to understand the spatio-temporal distributions and relative contributions of the spectrum of gaseous pollutants to the pollution loads as well as identify changing emission loads across sectors. The results of this study will facilitate the formulation of evidence-based air pollution reduction strategies and policies.
Keywords: Air pollution; China; Monitoring stations data; Responsible Editor: Gerhard Lammel; Spatio-temporal analysis; Trend analysis.