Change Points Detection and Trend Analysis to Characterize Changes in Meteorologically Normalized Air Pollutant Concentrations

Identifying changes in ambient air pollution levels and establishing causation is a research area of strategic importance to assess the effectiveness of air quality interventions. A major challenge in pursuing these objectives is represented by the confounding effects of the meteorological conditions which easily mask or emphasize changes in pollutants concentrations. In this study, a methodological procedure to analyze changes in pollutants concentrations levels after accounting for changes in meteorology over time was developed. The procedure integrated several statistical tools, such as the change points detection and trend analysis that are applied to the pollutants concentrations meteorologically normalized using a machine learning model. Data of air pollutants and meteorological parameters, collected over the period 2013–2019 in a rural area affected by anthropic emissive sources, were used to test the procedure. The joint analysis of the obtained results with the available metadata allowed providing plausible explanations of the observed air pollutants behavior. Consequently, the procedure appears promising in elucidating those changes in the air pollutant levels not easily identifiable in the original data, supplying valuable information to identify an atmospheric response after an intervention or an unplanned event.

A novel RSW&TST framework of MCPs detection for abnormal pattern recognition on large-scale time series and pathological signals in epilepsy

To quickly and efficiently recognize abnormal patterns from large-scale time series and pathological signals in epilepsy, this paper presents here a preliminary RSW&TST framework for Multiple Change-Points (MCPs) detection based on the Random Slide Window (RSW) and Trigeminal Search Tree (TST) methods. To avoid the remaining local optima, the proposed framework applies a random strategy for selecting the size of each slide window from a predefined collection, in terms of data feature and experimental knowledge. For each data segment to be diagnosed in a current slide window, an optimal path towards a potential change point is detected by TST methods from the top root to leaf nodes with O(log3(N)). Then, the resulting MCPs vector is assembled by means of TST-based single CP detection on data segments within each of the slide windows. In our experiments, the RSW&TST framework was tested by using large-scale synthetic time series, and then its performance was evaluated by comparing it with existing binary search tree (BST), Kolmogorov-Smirnov (KS)-statistics, and T-test under the fixed slide window (FSW) approach, as well as the integrated method of wild binary segmentation and CUSUM test (WBS&CUSUM). The simulation results indicate that our RSW&TST is both more efficient and effective, with a higher hit rate, shorter computing time, and lower missed, error and redundancy rates. When the proposed RSW&TST framework is executed for MCPs detection on pathological ECG (electrocardiogram)/EEG (electroencephalogram) recordings of people in epileptic states, the abnormal patterns are roughly recognized in terms of the number and position of the resultant MCPs. Furthermore, the severity of epilepsy is roughly analyzed based on the strength and period of signal fluctuations among multiple change points in the stage of a sudden epileptic attack. The purpose of our RSW&TST framework is to provide an encouraging platform for abnormal pattern recognition through MCPs detection on large-scale time series quickly and efficiently.

Using the Change Point Model (CPM) Framework to Identify Windows for Water Resource Management Action in the Lower Colorado River Basin of Texas, USA

In water-stressed river basins with growing urban populations, conflicts over water resources have emerged between urban and agricultural interests, as managerial interventions occur with little warning and tend to favor urban over agricultural water uses. This research documents changes in water use along an urban-to-agricultural gradient to examine whether it is possible to leverage temporal fluctuations in key quantitative data indicators to detect periods in which we could expect substantive managerial interventions in water resource management. We employ the change point model (CPM) framework to locate shifts in water use, climate-related indicators, lake and river characteristics, and agricultural trends across urban and agricultural counties in the lower Colorado River basin of Texas. Three distinctive groupings of change points appear. Increasing water use by urban counties and a shift in local climate conditions characterize the first period. Declines in agricultural counties’ water use and crop production define the second. Drops in lake levels, lower river discharge, and an extended drought mark the third. We interpret the results relative to documented managerial intervention events and show that managerial interventions occur during and after significant change points. We conclude that the CPM framework may be used to monitor the optimal timing of managerial interventions and their effects to avoid negative outcomes.

Flow Indices Variability in Humid Subtropical of Upper Awash River Basin, Ethiopia

Investigating the hydrological extremes indices at high resolutions describing the whole stream spectrum is essential for the comprehensive assessment of watershed hydrology. The study focuses on a wide-ranging assessment of river discharge in annual mean, peak, and high and low percentiles flow at the Upper Awash River basin, Ethiopia. Statistical tests such as coefficient of variation, flood variability to characterize the flow regime and Tukey’s test to detect decadal variability. Modified Mann-Kendall test, Sen’s slope estimator, innovative trend analysis and Pettitt’s test were applied to see trends, and change points in time series, respectively. Results showed that the basin was characterized by moderate to high variability. Spatially, main tributaries showed a higher variability, almost in all-time step and characterized by higher flood variability. The large discharge receiving rivers resulted in a moderate to high and lower discharge variability. Test statistics resulted in a positive increasing trend dominating most time scales at a 5% significant level and higher magnitude of slope trend in peak flow. A negative trends were also exhibited. Hombole main outlet site experienced decreasing trend in high percentile flow. In comparison, complete trend direction agreements were observed (except in few series). Flow indices showed an upward shift and downward shift mainly in the year 2000s and the significant decadal variation resulted in comparable with change points. The study provides an understanding of water resources variability, which will be necessary to apply operational water resources strategies and management to restrain the potential impacts of variability nature of the streamflow.

The regional features of precipitation variation trends over Sichuan in China by the ESMD method

Based on a precipitation time series from 49 meteorological stations in Sichuan Province during the period from 1961 to 2011, the multi-scale characteristics of precipitation variability are analyzed using the extreme-point symmetric mode decomposition method (ESMD). Regional differences in variation trends and change-points were also preliminarily discussed. The results indicated that in the last 50+ years, the overall precipitation in Sichuan Province has exhibited a significant non-linear downward trend, and its changes have clearly exhibited an inter-annual scale (quasi-3 and quasi-8-year) and interdecadal scale (quasi-13-year). The variance contribution rates of each component demonstrated that the inter-annual change had a strong influence on the overall precipitation change in Sichuan Province, and the reconstructed inter-annual variation trend could describe the fluctuation state of the original precipitation during the study period. The reconstructed interdecadal variability revealed that the climate mode in Sichuan Province had divided into three distinct variation periods with 1973 and 1998 as the boundaries. Furthermore, there were regional differences in the non-linear changes and change-points of precipitation. In addition, in order to study the relations between the changing more or less of rising or decrease and meteorological station’s geographical position (latitude, longitude and elevation) i.e., the Cokriging interpolation technique is applied directly to precipitation variation trend components through ESMD decomposition. At the same time, the results also suggested that the ESMD method can effectively reveal variations in long-term precipitation sequences at different time scales and can be used for the complex diagnosis of non-linear and non-stationary signal changes.