[Spatio-temporal Variations in PM2.5and Its Influencing Factors in the Yangtze River Delta Urban Agglomeration]

To coordinate the contradiction between economic development and environmental pollution and achieve the sustainable development of the economy and society, the spatio-temporal variations in PM2.5 were analyzed based on PM2.5 concentration and meteorological data of the Yangtze River Delta (YRD) urban agglomeration. Wavelet transform coherence (WTC), partial wavelet coherence (PWC), and multiple wavelet coherence (MWC) were used to analyze the multi-scale coupling oscillation between PM2.5 and meteorological factors in the time-frequency domain. The results showed that:① the concentration of PM2.5 in the YRD decreased from northwest to southeast, and the spatial range with high PM2.5 concentration decreased annually. The spatial distribution characteristics of the seasonal average PM2.5 concentration were similar to those of the annual average PM2.5 concentration. PM2.5 concentration exhibited the seasonal variation characteristics of high in winter, low in summer, and transitioning between spring and autumn. ② PM2.5 concentration decreased from 2015 to 2021, and the compliance rate increased. The difference in annual average PM2.5 concentration was decreased with dynamic convergence characteristics. The convergence of PM2.5 concentration in summer was greater than that in winter. During the whole study period, the daily average PM2.5 concentration showed a "U" distribution, and the proportion of days with excellent and good PM2.5 levels were 49.72% and 41.45%, respectively. ③ The wavelet coherence between PM2.5 and meteorological factors was different in different time-frequency domains. The main factors affecting PM2.5 were different in different time-frequency scales. At all time-frequency scales, WTC and PWC showed that wind speed and temperature were the best explanatory variables of PM2.5 variation, respectively. ④ The larger the time-frequency scale, the stronger the interaction of multi-factor combinations to explain PM2.5 variations. The synergistic effect of temperature and wind speed could better explain the variation in PM2.5. These results can provide reference for air pollution control in the YRD.

Selection of Optimal Palmer Predictors for Increasing the Predictability of the Danube Discharge: New Findings Based on Information Theory and Partial Wavelet Coherence Analysis

The purpose of this study was to obtain synergistic information and details in the time-frequency domain of the relationships between the Palmer drought indices in the upper and middle Danube River basin and the discharge (Q) in the lower basin. Four indices were considered: the Palmer drought severity index (PDSI), Palmer hydrological drought index (PHDI), weighted PDSI (WPLM) and Palmer Z-index (ZIND). These indices were quantified through the first principal component (PC1) analysis of empirical orthogonal function (EOF) decomposition, which was obtained from hydro-meteorological parameters at 15 stations located along the Danube River basin. The influences of these indices on the Danube discharge were tested, both simultaneously and with certain lags, via linear and nonlinear methods applying the elements of information theory. Linear connections were generally obtained for synchronous links in the same season, and nonlinear ones for the predictors considered with certain lags (in advance) compared to the discharge predictand. The redundancy-synergy index was also considered to eliminate redundant predictors. Few cases were obtained in which all four predictors could be considered together to establish a significant information base for the discharge evolution. In the fall season, nonstationarity was tested through wavelet analysis applied for the multivariate case, using partial wavelet coherence (pwc). The results differed, depending on the predictor kept in pwc, and on those excluded.

California's carbon market and energy prices: a wavelet analysis

Carbon price is a key variable in management and risk decisions in activities related to the burning of fossil fuels. Different major players in this market, such as polluters, regulators and financial actors, have different time horizons. We use innovative multivariate wavelet analysis tools, including partial wavelet coherence and partial wavelet gain, to study the link between carbon prices and final energy prices in the time and frequency dimensions in California's carbon market, officially known as the California cap-and-trade programme. We find that gasoline prices lead an anti-phase relation with carbon prices. This result is very stable at lower frequencies (close to 1-year period cycles), and it is also present before mid-2015 in the 20-34 weeks frequency band. Regarding electricity, we find that at about a 1-year period, a rise in carbon prices is reflected in higher electricity prices. We conclude that the first 5 years of compliance of the California cap-and-trade programme show that emissions trading is a significant measure for climate change mitigation, with visible rising carbon prices. The quantitative financial analytics we present supports the recent decision to extend the current market to 2030 without the need for complementary carbon pricing schemes.This article is part of the theme issue 'Redundancy rules: the continuous wavelet transform comes of age'.