Search Results (10)

Although China has achieved substantial reductions in particulate matter pollution, continually rising ground-level ozone now constitutes the primary challenge to further air-quality improvements. A systematic assessment of the long-term spatiotemporal behavior of ozone (O₃) and its links to meteorology and emissions is still lacking. Here, we develop a novel framework that couples Kolmogorov–Zurbenko (KZ) filtering with an optimized random forest (RF) regression model to examine daily maximum 8 h average ozone (O₃-8h) in 372 Chinese cities from 2013 to 2023. The approach quantitatively disentangles meteorological and emission contributions at the national scale, overcoming the limitations of traditional linear methods in capturing non-linear processes. Long-term components explain, in general, <40% of total O₃ variance. In eastern urban agglomerations, long-term meteorological factors—particularly temperature and surface ultraviolet radiation—account for up to 80% of the trend, whereas long-term emission contributions remain modest (≈5–6%), with pronounced signals in the Beijing–Tianjin–Hebei and Fenwei Plain regions. Empirical orthogonal function analysis further reveals distinct spatial patterns of emission influence: long-term O₃ trends in mega-cities such as Beijing, Tianjin, and Shanghai are driven mainly by local emissions (1.5–3% contribution), while key transport hubs including Xi’an, Tangshan, and Langfang are markedly affected by traffic-related emissions (>2%). These findings clarify the heterogeneous mechanisms governing O₃ formation across China and provide a scientific basis for designing and implementing the next phase of region-specific, joint prevention-and-control policies. Full article

NO₂ primarily originates from natural and anthropogenic emissions. Given China’s vast territory and significant differences in topography and meteorological conditions, a detailed understanding of the impacts of weather and human emissions in different regions is essential. This study employs Kolmogorov–Zurbenko (KZ) filtering and stepwise multiple linear regression to isolate the effects of meteorological conditions on tropospheric NO₂ vertical column densities. Long term trends indicate an overall decline, with anthropogenic contribution rates exceeding 90% in Shanghai, Changchun, Urumqi, Shijiazhuang, and Wuhan, where interannual variations are primarily driven by human emissions. In Guangzhou, the anthropogenic contribution rate exceeds 100%, highlighting the significant impact of human factors in this region, although meteorological conditions somewhat mitigate their effect on NO₂. In Chengdu, meteorological factors also play a role. Seasonal variations display a U-shaped trend, and there are significant differences in the impact of meteorological factors on seasonal variations among different regions. Meteorological contribution rates in Changchun and Chengdu are below 36.90% and anthropogenic contributions exceed 63.10%. This indicates that changes in NO₂ are less influenced by meteorological factors than by human activities, with human emissions dominating. In other regions, meteorological contributions are greater than those from human activities. Full article

Air pollution is influenced by a combination of pollutant emissions and meteorological conditions. Anthropogenic emissions and meteorological conditions are the two main causes of atmospheric pollution, and the contribution of meteorology and emissions to the reduction of PM_2.5 concentrations across the country has not yet been comprehensively examined. This study used the Kolmogorov–Zurbenko (KZ) filter and random forest (RF) model to decompose and reconstruct PM_2.5 time series in five major urban agglomerations in China, analyzing the impact of meteorological factors on PM_2.5 concentrations. From 2015 to 2021, PM_2.5 concentrations significantly decreased in all urban agglomerations, with annual averages dropping by approximately 50% in Beijing–Tianjin–Hebei (BTH), Yangtze River Delta (YRD), Pearl River Delta (PRD), Central Plain (CP), and Chengdu–Chongqing (CC). This reduction was due to both favorable meteorological conditions and emission reductions. The KZ filter effectively separated the PM_2.5 time series, and the RF model achieved high squared correlation coefficient (R²) values between predicted and observed values, ranging from 0.94 to 0.98. Initially, meteorological factors had a positive contribution to PM_2.5 reduction, indicating unfavorable conditions, but this gradually turned negative, indicating favorable conditions. By 2021, the rates of meteorological contribution to PM_2.5 reduction in BTH, YRD, PRD, CP, and CC changed from 14.3%, 16.9%, 7.2%, 12.2%, and 11.5% to −36.5%, −31.5%, −26.9%, −30.3%, and −23.5%, respectively. Temperature and atmospheric pressure had the most significant effects on PM_2.5 concentrations. The significant decline in PM_2.5 concentrations in BTH and CP after 2017 indicated that emission control measures were gradually taking effect. This study confirms that effective pollution control measures combined with favorable meteorological conditions jointly contributed to the improvement in air quality. Full article

Surface ozone (O₃), a critical air pollutant, poses significant challenges in urban environments, as exemplified by the city of Chaozhou in southeastern China. This study employs a novel combination of trend analysis and spatial source attribution techniques to evaluate the long-term dynamics of surface ozone and identify its sources. Utilizing the Kolmogorov–Zurbenko (KZ) filter and percentile regression, we analyzed the temporal trends of daily maximum 8 h moving average ozone (MDA8 O₃) concentrations from 2014 to 2023. Our analysis revealed a general long-term downward trend in MDA8 O₃ values alongside notable monthly fluctuations, with peak concentrations typically occurring in October and April. Additionally, the percentile regression analysis demonstrated a significant downward trend in MDA8 O₃ concentrations across nearly all percentiles, with larger decline rates at higher percentiles, highlighting the effectiveness of local and regional O₃ management strategies in Chaozhou. The changes in MDA8 O₃ concentrations were mainly influenced by the short-term component, contributing 62.2%, while the contribution of the long-term fraction is relatively small. This suggests a significant influence of immediate meteorological conditions and transient pollution events on local O₃ levels. To further elucidate the origins of high O₃ concentrations, trajectory cluster analysis, trajectory sector analysis (TSA), and potential source contribution function (PSCF) analysis were conducted. The trajectory cluster analysis revealed that the northeast air mass was the main transport air mass in Chaozhou during the study period, accounting for 39.1% of occurrences. The northeast cluster C with medium-distance trajectories corresponds to higher concentration of O₃, which may be the main transport pathway of O₃ pollution in Chaozhou. TSA corroborates these findings, with northeast sectors 1, 2, and 3 accounting for 50.3% of trajectory residence time and contributing 52.2% to O₃ levels in Chaozhou. PSCF results further indicate potential high O₃ sources from the northeast, especially in autumn. This comprehensive analysis suggests that Chaozhou’s elevated O₃ levels are influenced by both regional transport from the northeast and local emissions. These findings offer crucial insights into the temporal dynamics of surface O₃ in Chaozhou, paving the way for more effective and targeted air quality management strategies. Full article

Based on the hourly monitoring data including meteorological elements and PM_2.5 mass concentration in Yancheng from 2017 to 2021, PM_2.5 mass concentration variations, influencing factors and source apportionment were studied by the Kolmogorov–Zurbenko filter and Potential Source Contribution Function Analysis (PSCF) method. The results showed that the mass concentration of PM_2.5 in Yancheng showed a decreasing trend from 2017 to 2021, with a decline rate of about 33.8% (2017, 44.79 ± 31.22 μg/m³; 2021, 29.66 ± 21.69 μg/m³); the visibility increased by 18.4% (2017, 11.69 ± 6.46 km; 2021,13.8 ± 6.24 km), which is mainly related to emission reduction measures in China. The mass concentration of PM_2.5 has significant seasonal variation characteristics, with the highest in winter, reaching 60.61 μg/m³, and the lowest in summer, only 23.11 μg/m³. The diurnal variation of PM_2.5 showed a unimodal distribution, and concentration difference is obvious under the influence of land–sea breeze (36.60 μg/m³, easterly wind; 43.57 μg/m³, westerly wind). Meteorological factors have an important impact on the mass concentration of PM_2.5, which fluctuates with seasons. It is calculated to have a good fitting relationship between the visibility and PM_2.5 concentration, and the correlation decreases with the increase in humidity (−0.71 ~ −0.41). The relatively clean atmosphere under high humidity conditions is also prone to the obstruction to vision. The corresponding PM_2.5 concentration varies significantly under different wind directions and wind speeds in Yancheng, and high values mainly come from the northwest–southeast–southwest direction. The potential source regions in autumn are mainly distributed in southwestern Jiangsu and northwestern Zhejiang; the potential source regions in winter are mainly located in southwestern Jiangsu, southern Anhui and northern Jiangxi. Full article

Critical temporal changes such as weekly fluctuations in surveillance systems often reflect changes in laboratory testing capacity, access to testing or healthcare facilities, or testing preferences. Many studies have noted but few have described day-of-the-week (DoW) effects in SARS-CoV-2 surveillance over the major waves of the novel coronavirus 2019 pandemic (COVID-19). We examined DoW effects by non-pharmaceutical intervention phases adjusting for wave-specific signatures using the John Hopkins University’s (JHU’s) Center for Systems Science and Engineering (CSSE) COVID-19 data repository from 2 March 2020 through 7 November 2021 in Middlesex County, Massachusetts, USA. We cross-referenced JHU’s data with Massachusetts Department of Public Health (MDPH) COVID-19 records to reconcile inconsistent reporting. We created a calendar of statewide non-pharmaceutical intervention phases and defined the critical periods and timepoints of outbreak signatures for reported tests, cases, and deaths using Kolmogorov-Zurbenko adaptive filters. We determined that daily death counts had no DoW effects; tests were twice as likely to be reported on weekdays than weekends with decreasing effect sizes across intervention phases. Cases were also twice as likely to be reported on Tuesdays-Fridays (RR = 1.90–2.69 [95%CI: 1.38–4.08]) in the most stringent phases and half as likely to be reported on Mondays and Tuesdays (RR = 0.51–0.93 [0.44, 0.97]) in less stringent phases compared to Sundays; indicating temporal changes in laboratory testing practices and use of healthcare facilities. Understanding the DoW effects in daily surveillance records is valuable to better anticipate fluctuations in SARS-CoV-2 testing and manage appropriate workflow. We encourage health authorities to establish standardized reporting protocols. Full article

The Global Task Force on Cholera Control (GTFCC) created a strategy for early outbreak detection, hotspot identification, and resource mobilization coordination in response to the Yemeni cholera epidemic. This strategy requires a systematic approach for defining and classifying outbreak signatures, or the profile of an epidemic curve and its features. We used publicly available data to quantify outbreak features of the ongoing cholera epidemic in Yemen and clustered governorates using an adaptive time series methodology. We characterized outbreak signatures and identified clusters using a weekly time series of cholera rates in 20 Yemeni governorates and nationally from 4 September 2016 through 29 December 2019 as reported by the World Health Organization (WHO). We quantified critical points and periods using Kolmogorov–Zurbenko adaptive filter methodology. We assigned governorates into six clusters sharing similar outbreak signatures, according to similarities in critical points, critical periods, and the magnitude of peak rates. We identified four national outbreak waves beginning on 12 September 2016, 6 March 2017, 28 May 2018, and 28 January 2019. Among six identified clusters, we classified a core regional hotspot in Sana’a, Sana’a City, and Al-Hudaydah—the expected origin of the national outbreak. The five additional clusters differed in Wave 2 and Wave 3 peak frequency, timing, magnitude, and geographic location. As of 29 December 2019, no governorates had returned to pre-Wave 1 levels. The detected similarity in outbreak signatures suggests potentially shared environmental and human-made drivers of infection; the heterogeneity in outbreak signatures implies the potential traveling waves outwards from the core regional hotspot that could be governed by factors that deserve further investigation. Full article

The Dallas-Fort Worth (DFW) area that experiences high temperature and intense solar radiation falls into the moderate nonattainment classification. The variation in meteorological parameters plays an important role in ambient ozone levels variation. Meteorological influences need to be decoupled from ozone data for long-term trend analysis. Temporal separation of maximum daily average 8-h ozone (MDA8 ozone), maximum daily temperature (TMAX), daily average solar radiation (DASR), and daily average wind speed (DAWS) were conducted using Kolmogorov-Zurbenko (KZ) filter for ozone records at Keller (C17), Arlington (C61), Red Bird (C402) monitoring stations in the DFW area from 2003 to 2017. Temporal separation, regression analysis, and meteorological detrending were performed. The long-term component had a clear and stable trend. The contribution of the long-term component to total variation was negligible, which is less than 2%. This is due to the removal of the data noise from the original time series data. The seasonal component had a major contribution (55% to 72%) in the total variation of the maximum temperature and solar radiation. However, the short-term component was dominant in the total variation of the MDA8 ozone (41–54%) and wind speed (68–79%). Regression analysis showed the baseline component bears the highest correlation than the short-term and raw. Solar radiation had the highest correlation to the MDA8 ozone, followed by temperature data in all three stations. Meteorological detrending showed the detrended long-term ozone had an increasing trend. The increasing trend was significant at C402 with a trend of 0.19 ± 0.006 ppb/y (0.398 R²), whereas slight increasing trends were found at C17 (0.072 ± 0.006 (0.107 R²)) and at C61 (0.019 ± 0.007 (0.005 R²)). The increasing trend of long-term components of MDA8 ozone was justified by the increasing level of NO_x and VOCs from the mobile sources in the DFW area. Full article

This research introduces a hybrid model for forecasting river flood events with an example of the Mohawk River in New York. Time series analysis and artificial neural networks are combined for the explanation and forecasting of the daily water discharge using hydrogeological and climatic variables. A low pass filter (Kolmogorov–Zurbenko filter) is applied for the decomposition of the time series into different components (long, seasonal, and short-term components). For the prediction of the water discharge time series, each component has been described by applying the multiple linear regression models (MLR), and the artificial neural network (ANN) model. The MLR retains the advantage of the physical interpretation of the water discharge time series. We prove that time series decomposition is essential before the application of any model. Also, decomposition shows that the Mohawk River is affected by multiple time scale components that contribute to the hydrologic cycle of the included watersheds. Comparison of the models proves that the application of the ANN on the decomposed time series improves the accuracy of forecasting flood events. The hybrid model which consists of time series decomposition and artificial neural network leads to a forecasting up to 96% of the explanation for the water discharge time series. Full article

Floods typically occur due to ice jams in the winter or extended periods of precipitation in the spring and summer seasons. An increase in the rate of water discharge in the river coincides with a flood event. This research combines the time series decomposition and the time series regression model for the flood prediction in Mohawk River at Schenectady, New York. The time series decomposition has been applied to separate the different frequencies in hydrogeological and climatic data. The time series data have been decomposed into the long-term, seasonal-term, and short-term components using the Kolmogorov-Zurbenko filter. For the application of the time series regression model, we determine the lags of the hydrogeological and climatic variables that provide the maximum performance for the model. The lags applied in the predictor variables of the model have been used for the physical interpretation of the model to strengthen the relationship between the water discharge and the climatic and hydrogeological variables. The overall model accuracy has been increased up to 73%. The results show that using the lags of the variables in the time regression model, and the forecasting accuracy has been increased compared to the raw data by two times. Full article