1b:I[76749,["/_next/static/chunks/bf18fea59be6d5b1.js","/_next/static/chunks/1ba21a8e081e29d1.js","/_next/static/chunks/0383663887945ae7.js","/_next/static/chunks/2060b26370dc6c0a.js","/_next/static/chunks/c89b4435b8c37fcf.js","/_next/static/chunks/8ddf2550f38c2f2a.js","/_next/static/chunks/5bf36002c846d2d8.js"],"default"] 1c:I[3486,["/_next/static/chunks/bf18fea59be6d5b1.js","/_next/static/chunks/1ba21a8e081e29d1.js","/_next/static/chunks/0383663887945ae7.js","/_next/static/chunks/2060b26370dc6c0a.js","/_next/static/chunks/c89b4435b8c37fcf.js","/_next/static/chunks/8ddf2550f38c2f2a.js","/_next/static/chunks/5bf36002c846d2d8.js"],"default"] 1d:I[17707,["/_next/static/chunks/bf18fea59be6d5b1.js","/_next/static/chunks/1ba21a8e081e29d1.js","/_next/static/chunks/0383663887945ae7.js","/_next/static/chunks/2060b26370dc6c0a.js","/_next/static/chunks/c89b4435b8c37fcf.js","/_next/static/chunks/8ddf2550f38c2f2a.js","/_next/static/chunks/5bf36002c846d2d8.js"],"default"] 1e:I[66528,["/_next/static/chunks/bf18fea59be6d5b1.js","/_next/static/chunks/1ba21a8e081e29d1.js","/_next/static/chunks/0383663887945ae7.js","/_next/static/chunks/2060b26370dc6c0a.js","/_next/static/chunks/c89b4435b8c37fcf.js","/_next/static/chunks/8ddf2550f38c2f2a.js","/_next/static/chunks/5bf36002c846d2d8.js"],"default"] 1f:I[63176,["/_next/static/chunks/bf18fea59be6d5b1.js","/_next/static/chunks/1ba21a8e081e29d1.js","/_next/static/chunks/0383663887945ae7.js","/_next/static/chunks/2060b26370dc6c0a.js","/_next/static/chunks/c89b4435b8c37fcf.js","/_next/static/chunks/8ddf2550f38c2f2a.js","/_next/static/chunks/5bf36002c846d2d8.js"],"default"] 20:I[87099,["/_next/static/chunks/bf18fea59be6d5b1.js","/_next/static/chunks/1ba21a8e081e29d1.js","/_next/static/chunks/0383663887945ae7.js","/_next/static/chunks/2060b26370dc6c0a.js","/_next/static/chunks/c89b4435b8c37fcf.js","/_next/static/chunks/8ddf2550f38c2f2a.js","/_next/static/chunks/5bf36002c846d2d8.js"],"default"] 21:I[26200,["/_next/static/chunks/bf18fea59be6d5b1.js","/_next/static/chunks/1ba21a8e081e29d1.js","/_next/static/chunks/0383663887945ae7.js","/_next/static/chunks/2060b26370dc6c0a.js","/_next/static/chunks/c89b4435b8c37fcf.js","/_next/static/chunks/8ddf2550f38c2f2a.js","/_next/static/chunks/5bf36002c846d2d8.js"],"Group"] 22:I[23365,["/_next/static/chunks/bf18fea59be6d5b1.js","/_next/static/chunks/1ba21a8e081e29d1.js","/_next/static/chunks/0383663887945ae7.js","/_next/static/chunks/2060b26370dc6c0a.js","/_next/static/chunks/c89b4435b8c37fcf.js","/_next/static/chunks/8ddf2550f38c2f2a.js","/_next/static/chunks/5bf36002c846d2d8.js"],"default"] 23:T53b,It remains unclear on how PM_2.5 interacts with other air pollutants and meteorological factors at different temporal scales, while such knowledge is crucial to address the air pollution issue more effectively. In this study, we explored such interaction at various temporal scales, taking the city of Nanjing, China as a case study. The ensemble empirical mode decomposition (EEMD) method was applied to decompose time series data of PM_2.5, five other air pollutants, and six meteorological factors, as well as their correlations were examined at the daily and monthly scales. The study results show that the original PM_2.5 concentration significantly exhibited non-linear downward trend, while the decomposed time series of PM_2.5 concentration by EEMD followed daily and monthly cycles. The temporal pattern of PM₁₀, SO₂ and NO₂ is synchronous with that of PM_2.5. At both daily and monthly scales, PM_2.5 was positively correlated with CO and negatively correlated with 24-h cumulative precipitation. At the daily scale, PM_2.5 was positively correlated with O₃, daily maximum and minimum temperature, and negatively correlated with atmospheric pressure, while the correlation pattern was opposite at the monthly scale.

Zhang YiTing

Publications