A time–scaling property of air pollution indices: a case study of Shanghai, China

The Time-Scaling Analysis of Chinese Stock Market Based on Fractal Methods

“…In addition, SZCI's α value is greater than SHCI's represent the former has stronger persistence and weaker anti-persistence than latter. The results are in agreement with that of other researchers [9].…”

Fractal and Long-Memory Traces in PM10 Time Series in Athens, Greece

“…The above temporal alignment may partially be attributed to the vicinity of the AGP, LYK, and MAR stations. Although the visual observation of data variations may reveal some patterns, it cannot be compared with the outcomes of cutting-edge methods such as neural networks [5][6][7][8][9][10][11][12][13][14][15][16]83] wavelets [4,17,18], and high-level statistics [20,24,28,37]; most importantly, fractal and long-memory methods are more powerful in signal exploration. The latter methods, as already emphasised, are advantageous because they can outline trends that are hidden in the time series.…”

“…Caused by human intervention, urban air pollution is now a sustained ever-changing part of the urban environment, governed by mechanisms such as emission, dilution, and transportation [3]. Significant pollutants are sulphur dioxides and nitrogen oxides, the ozone, and respirable particulate matter (PM) [4]. The scientific research of the topic focuses on understanding the variations of urban air pollutants with respect to time, investigating the contribution of environmental and meteorological parameters to those variations through modeling, forecasting, and predicting future trends and alterations.…”

“…The scientific research of the topic focuses on understanding the variations of urban air pollutants with respect to time, investigating the contribution of environmental and meteorological parameters to those variations through modeling, forecasting, and predicting future trends and alterations. Noteworthy approaches utilize neural networks [5][6][7][8][9][10][11][12][13][14][15][16], wavelets [17,18], statistical [19,20], and deterministic [21] models, and, currently, cutting-edge techniques based on chaos and complexity (e.g., References [4,[22][23][24][25][26][27][28][29][30][31][32][33][34][35][36]). Since no forecast method is solid, reliable, and accurate enough to sufficiently match all air-contamination time series [37], it is a challenging task to achieve credible estimations.…”

“…Regarding air-pollution environmental systems, Shi et al [24] reported two power laws in three air-pollution time series in Shanghai, China, indicating different self-organised critical (SOC) states. Liu et al [4] employed detrended fluctuation analysis (DFA) and multifractals to investigate the temporal variations of SO 2 , NO 2 , and PM 10 concentrations in the city of Shanghai, China. They reported that the corresponding air pollution was a self-organised critical (SOC) process.…”