Kolmogorov-Zurbenko Filter Research Articles

Predictive Model for O3 in Shanghai Based on the KZ Filtering Technique and LSTM

In this study, a Kolmogorov-Zurbenko （KZ） filter was proposed to decompose the original ozone （O3） sequence to improve the accuracy of ozone long-term series prediction and select relevant meteorological features. Furthermore, the enhanced maximal minimal redundancy （mRMR） feature selection technique was combined with the support vector regression （SVR） approach to select the most illuminating meteorological features. Subsequently, from May to August 2023, during high ozone concentration periods, a long short-term memory network （LSTM） was utilized to assess and predict high ozone concentration periods at the monitoring stations of Jingan （urban area）, Pudong-Chuansha （suburban area）, and Dianshan Lake （suburban area） in Shanghai. The results showed that pressure, temperature, humidity, boundary layer height, and wind direction were the best combinations of O3 baseline and short-term components, as chosen by feature screening. The R2 values for Jingan Station, Pudong-Chuansha Station, and Dianshan Lake Station were 0.86, 0.83, and 0.85, respectively. The RMSE values were 18.26, 18.74, and 20.02 μg·m-3, respectively. These findings suggest that decomposing the original O3 sequence improved the prediction accuracy of ozone concentrations. Additionally, as indicated by the R2 and RMSE values found for every monitoring station, feature screening preserved the model's predictive performance.

Fine particulate matter and ozone variability with regional and local meteorology in Beijing, China

Fine particulate matter (PM2.5) and surface ozone air pollution have severe health consequences. Since China implemented the nationwide Air Pollution Prevention and Control Action Plan (APPCAP) in 2013, annual average PM2.5 concentrations have decreased while O3 concentration have increased in Beijing. It is unclear, however, to the extent that short-term meteorological variability has influenced these trends. To separate the influence of meteorology from long-term emissions-induced changes, we quantify the portion of the long-term PM2.5 and O3 concentration signals associated with short-term meteorological variability. Building off recent literature highlighting the regional nature of pollutant variability, we incorporate both locally observed meteorology and average regional reanalysis. We isolated sub-seasonal variability using the Kolmogorov-Zurbenko filter. Next, we trained and compared the utility of three statistical models of varying complexity (a linear model, a general additive model with splines, and a random forest) in their ability to predict out-of-sample daily concentrations. The random forest model yields the best predictive capability in holdout tests and predicts changing meteorological contributions to PM2.5 and ozone concurrent with changing concentrations across the study period. Results show an overall decrease in meteorology induced PM2.5 concentration variability at daily scales since 2013, but variability from meteorology has increased relative to mean PM2.5 concentrations. In contrast, O3 shows the opposite trend, with increasing meteorology-induced variability (meteorology-induced variability normalized by mean observed O3 has decreased). Our results show the importance of including regional meteorology in explaining local PM2.5 and O3 variability and quantify links between long-term policy implementation and short-term meteorological contributions to pollutant concentrations.

Kolmogorov-Zurbenko filter coupled with machine learning to reveal multiple drivers of surface ozone pollution in China from 2015 to 2022

Near-surface ozone pollution is a significant concern in China. Its meteorological drivers are uncontrolled, stressing an urgent need to quantify the anthropogenic-driven components. This study employs explainable machine learning (ML) algorithms to predict ozone concentrations, emphasizing the anthropogenic-driven trends after accounting for meteorological effects. Results present that radiation is the most important meteorological factor affecting ozone pollution in the Yangtze River Delta (YRD), Pearl River Delta (PRD), and Sichuan Basin (SCB), while temperature is dominant for the North China Plain (NCP). Even at lower temperatures, stronger solar radiation can still lead to the accumulation of higher ozone concentrations. The anthropogenic-driven ozone concentration showed an upward trend in China, with an interannual growth rate of 2.61 μg/m3 a−1 from 2015 to 2022. Nonetheless, its rising trend experienced a post-2019 downturn, due to the COVID-19 lockdown and emission reduction strategies. It started to rise in 2022. Regionally, NCP has the highest ozone concentration, the SCB has the most pronounced increase in 2022, but the PRD has no noticeable variation and no significant seasonal change after 2019. As for precursor emissions, the urban areas in China are mostly located in the VOC-limited (volatile organic component) and transitional regimes, highlighting that VOC control is more cost-effective.

Applying the Kolmogorov–Zurbenko filter followed by random forest models to 7Be observations in Spain (2006–2021)

In this study, we analysed 7Be weekly surface measurements from six Spanish laboratories from 2006 to 2021. The Kolmogorov–Zurbenko filter was applied to the six 7Be time series, and following an iterative process, the original data were divided into two fractions: one related to variations characterized by periods above 33 days (including, among others, the seasonal cycle) and the second noisier fraction related to mechanisms originating from variations with periods below 33 days. Both fractions were independent at the six locations. The second machine-based step using random forest models was applied with the aim of identifying the most influential inputs to the observed 7Be concentrations, and machine learning-inspired regression models were fitted. With respect to seasonal components, the results indicated that the memory of the system was the most influential input, as expected by the large fraction of variance explained by the seasonal cycle, followed by that of humidity and wind-related variables. For the fraction corresponding to periods below 33 d, precipitation-, humidity-, and radiation-related variables were the most influential. This methodology has made it possible to successfully describe the major mechanisms known to be involved in the generation of the surface 7Be concentrations observed in Spain.

Differential response of various pollutant-meteorology factors on O3 in key regions of China: Based on multiple methods and datasets

Scientific control is urgently required to address O3 pollution in key regions of China. Therefore, based on air pollutant concentrations and meteorological datasets, the methods including distributed lag nonlinear models, Kolmogorov–Zurbenko filter and stepwise multiple linear regression (SMLR) were applied to analyze and clarify the response of pollution and meteorological factors to O3, as well as the comprehensive impact under different time dimensions in Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD) and Chengdu-Chongqing regions (CY). Results indicate O3 concentrations in the study area fluctuated at high pollution levels. The effects of various air pollutants and meteorological factors on O3 varied among cities. Temperature is the dominant driver for O3, followed by solar radiation and relative humidity. The influence of wind vector indicates different transport sources of O3 and its precursors. Direct photochemical process based on SMLR fitting coefficient calculation is the main pathway for O3 generation, with contribution of 68.7%–93.8%. In terms of air-pollutant factors, U-shaped response between PM2.5 and O3 in all cities highlighted the interaction of the two air pollutants. The undeniable impact of NO2 on O3 is reflected in its range of effect. Meteorological adjustments led to −10.4–15.1 μg/m3 variations on O3 during 2014–2021, but performed obvious spatiotemporal differences. The changes in anthropogenic emissions deteriorated O3 pollution from 2014 to 2017, while have fluctuated effect since 2018. From the actual cases in special periods, in order to alleviate O3 pollution, it is necessary to strengthen the understanding of the synergistic contribution of meteorology and anthropogenic emissions.

Quantitative impacts of meteorology and emissions on the long-term trend of O3 in the Yangtze River Delta (YRD), China from 2015 to 2022

Extensive spatiotemporal analyses of long-trend surface ozone in the Yangtze River Delta (YRD) region and its meteorology-related and emission-related have not been systematically analyzed. In this study, by using 8-year-long (2015–2022) surface ozone observation data, we attempted to reveal the variation of multiple timescale components using the Kolmogorov–Zurbenko filter, and the effects of meteorology and emissions were quantitatively isolated using multiple linear regression with meteorological variables. The results showed that the short-term, seasonal, and long-term components accounted for daily maximum 8-hr average O3 (O3–8 hr) concentration, 46.4%, 45.9%, and 1.0%, respectively. The meteorological impacts account for an average of 71.8% of O3–8 hr, and the YRD's eastern and northern sections are meteorology-sensitive areas. Based on statistical analysis technology with empirical orthogonal function, the contribution of meteorology, local emission, and transport in the long-term component of O3–8 hr were 0.21%, 0.12%, and 0.6%, respectively. The spatiotemporal analysis indicated that a distinct decreasing spatial pattern could be observed from coastal cities towards the northwest, influenced by the monsoon and synoptic conditions. The central urban agglomeration north and south of the YRD was particularly susceptible to local pollution. Among the cities studied, Shanghai, Anqing, and Xuancheng, located at similar latitudes, were significantly impacted by atmospheric transmission—the contribution of Shanghai, the maximum accounting for 3.6%.

An intercomparison of weather normalization of PM2.5 concentration using traditional statistical methods, machine learning, and chemistry transport models

Traditional statistical methods (TSM) and machine learning (ML) methods have been widely used to separate the effects of emissions and meteorology on air pollutant concentrations, while their performance compared to the chemistry transport model has been less fully investigated. Using the Community Multiscale Air Quality Model (CMAQ) as a reference, a series of experiments was conducted to comprehensively investigate the performance of TSM (e.g., multiple linear regression and Kolmogorov–Zurbenko filter) and ML (e.g., random forest and extreme gradient boosting) approaches in quantifying the effects of emissions and meteorology on the trends of fine particulate matter (PM2.5) during 2013−2017. Model performance evaluation metrics suggested that the TSM and ML methods can explain the variations of PM2.5 with the highest performance from ML. The trends of PM2.5 showed insignificant differences (p > 0.05) for both the emission-related (PM2.5EMI\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${{\\rm{PM}}}_{2.5}^{{\\rm{EMI}}}$$\\end{document}) and meteorology-related components between TSM, ML, and CMAQ modeling results. PM2.5EMI\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${{\\rm{PM}}}_{2.5}^{{\\rm{EMI}}}$$\\end{document} estimated from ML showed the least difference to that from CMAQ. Considering the medium computing resources and low model biases, the ML method is recommended for weather normalization of PM2.5. Sensitivity analysis further suggested that the ML model with optimized hyperparameters and the exclusion of temporal variables in weather normalization can further produce reasonable results in emission-related trends of PM2.5.

Achievements and challenges in improving air quality in China: Analysis of the long-term trends from 2014 to 2022

Due to the implementation of air pollution control measures in China, air quality has significantly improved, although there are still additional issues to be addressed. This study used the long-term trends of air pollutants to discuss the achievements and challenges in further improving air quality in China. The Kolmogorov-Zurbenko (KZ) filter and multiple-linear regression (MLR) were used to quantify the meteorology-related and emission-related trends of air pollutants from 2014 to 2022 in China. The KZ filter analysis showed that PM2.5 decreased by 7.36 ± 2.92% yr−1, while daily maximum 8-h ozone (MDA8 O3) showed an increasing trend with 3.71 ± 2.89% yr−1 in China. The decrease in PM2.5 and increase in MDA8 O3 were primarily attributed to changes in emission, with the relative contribution of 85.8% and 86.0%, respectively. Meteorology variations, including increased ambient temperature, boundary layer height, and reduced relative humidity, also contributed to the reduction of PM2.5 and the enhancement of MDA8 O3. The emission-related trends of PM2.5 and MDA8 O3 exhibited continuous decrease and increase, respectively, from 2014 to 2022, while the variation rates slowed during 2018–2020 compared to that during 2014–2017, highlighting the challenges in further improving air quality, particularly in simultaneously reducing PM2.5 and O3. This study recommends reducing NH3 emissions from the agriculture sector in rural areas and transport emissions in urban areas to further decrease PM2.5 levels. Addressing O3 pollution requires the reduction of O3 precursor gases based on site-specific atmospheric chemistry considerations.

Meteorological influences on PM2.5 variation in China using a hybrid model of machine learning and the Kolmogorov-Zurbenko filter

Quantitatively evaluating the contribution of meteorology and emissions to PM2.5 reduction during the Three-year Action Plan to Fight Air Pollution (2018–2020) is important to guide future emission control measures in China. In this study, a Kolmogorov-Zurbenko (KZ) filter and an artificial neural network (ANN) combined model was used to separate the meteorology-influenced PM2.5 concentration by considering seven meteorological factors including boundary layer height, surface pressure, surface net solar radiation, total precipitation, atmospheric temperature, wind speed and wind direction in China. To compensate for the black-box nature of the ANN, the Shapley additive explanation (SHAP) value was introduced to explain the effect of each meteorological factor on long-term PM2.5 concentration. Results showed that PM2.5 concentration decreased by 14% from 2018 to 2020 in China with annual average concentrations of 37.19, 35.28, and 31.94 μg/m3, respectively. Although the decline in PM2.5 was mainly due to emission reductions (58% for all of China), the contribution of prevailing meteorological conditions should not be ignored, especially in some regions such as Northeast China, which had the highest contribution of meteorology (58%) on PM2.5 reduction. In general, the contributions of meteorological conditions to the long-term trend of PM2.5 varied greatly by region and season. SHAP analysis indicated that temperature, surface pressure and boundary layer height were the dominant meteorological factors affecting long-term PM2.5 with contributions of 65%, 11% and 6% on average. We also found that regions with larger average PM2.5 concentration were more affected by emission changes than by meteorology. Therefore, we recommend the continuation of emission control measures in heavily polluted regions such as North and Central China.

Hospital overcrowding caused by covid-19 and chronic kidney disease mortality

The coronavirus disease (COVID-19) pandemic has been the largest public health emergency in recent decades. While there have been many epidemiology studies on this pandemic, less research has been done to examine its indirect impact on mortality. This paper utilizes Kolmogorov-Zurbenko (KZ) filter to examine chronic kidney disease (CKD) mortality trend in Florida hospitals and finds an indirect impact of the pandemic, namely the excess Florida CKD deaths due to pandemic-period hospital over-crowding. The results show that kidney disease patients suffer higher mortality from hospital overcrowding, thus prompting the need to allocate healthcare resource to more vulnerable patients. This paper also proposes a measurement method of hospital overcrowding with KZ filter. The strong correlation between CKD mortality and hospital overcrowding confirms that the level of healthcare has a significant influence on the wellness of CKD patients.

Spatial-temporal Variation and Driving Factors of Ozone in China from 2019 to 2021 Based on EOF Technique and KZ Filter

Based on the hourly O3 concentration data of 337 prefectural-level divisions and simultaneous surface meteorological data in China, we applied empirical orthogonal function (EOF) analysis to analyze the main spatial patterns, variation trends, and main meteorological driving factors of O3 concentration in China from March to August in 2019-2021. In this study, a KZ (Kolmogorov-Zurbenko) filter was used to decompose the time series of O3 concentration and simultaneous meteorological factors into corresponding short-term, seasonal, and long-term components in 31 provincial capitals.Then, the stepwise regression was used to establish the relationship between O3 and meteorological factors. Ultimately, the long-term component of O3 concentration after "meteorological adjustment" was reconstructed. The results indicated that the first spatial patterns of O3 concentration showed a convergent change, that is, the volatility of O3 concentration was weakened in the high-value region of variability and enhanced in the low-value region.Before and after the meteorological adjustment, the variation trend of O3 concentration in different cities was different to some extent. The adjusted curve was "flatter" in most cities. Among them, Fuzhou, Haikou, Changsha, Taiyuan, Harbin, and Urumqi were greatly affected by emissions. Shijiazhuang, Jinan, and Guangzhou were greatly affected by meteorological conditions. Beijing, Tianjin, Changchun, and Kunming were greatly affected by emissions and meteorological conditions.

Application of Kolmogorov-Zurbenko filter to quantify the long-term meteorological and emission impacts on air quality

High concentrations of Particulate Matter (PM) have become a major problem in Turkey because of its economic development over the past decades, as well as its geographical proximity to natural dust source areas. In this study, PM10 data (for the period of 2010–2020) of 36 ground-based stations in 12 metropolitan cities over different regions in Turkey was used and Kolmogorov-Zurbenko (KZ) filter was implemented to decompose the data into its temporal components. A stepwise Multiple Linear Regression (MLR) model was developed to establish relationships between PM10 concentrations and a set of meteorological variables for each city to quantify the long-term meteorological as well as emission impacts on changes and trends in air quality. In this study analysis has revealed that out of twelve major cities in Turkey, only three of them has PM10 levels below or around 45 μgm−3,which is 24 h Air Quality Guideline (AQG) level identified by WHO (WHO, 2021). Overall, over the selected period, long-term change in PM10 concentration is negative for 10 out of 12 cities, ranging between −39.6 μgm−3 and −1.2 μgm−3. Only Adana and Kayseri have a positive long-term change ranging between 1.0 μgm−3 and 3.1 μgm−3. Long-term change in meteorology related component (χLTmet) is relatively constant, hence, long-term change in PM10 concentration (χLT) is heavily influenced by emission related component (χLTemis). As emission impact reduces over time, there is a decrease in PM10 levels in most of the cities. This finding is further supported by reductions in national emissions data.

Analysis of PM2.5 Characteristics in Yancheng from 2017 to 2021 Based on Kolmogorov–Zurbenko Filter and PSCF Model

Based on the hourly monitoring data including meteorological elements and PM2.5 mass concentration in Yancheng from 2017 to 2021, PM2.5 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 PM2.5 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 PM2.5 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 PM2.5 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 PM2.5, which fluctuates with seasons. It is calculated to have a good fitting relationship between the visibility and PM2.5 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 PM2.5 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.

Meteorological impacts on surface ozone: A case study based on Kolmogorov–Zurbenko filtering and multiple linear regression

Ozone variation, excluding meteorological effects, is very important to assess the effects of air pollution control policies. In this study, the Kolmogorov-Zurbenko (KZ) filter method and multiple linear stepwise regression are combined to study the impact of meteorological parameters on ozone concentration over the past 5 years (2016–2020) in a petrochemical industrial city in northern China. Monte Carlo simulations were used to evaluate the reliability for the potential quasi quantitative prediction of the baseline component. The average level of the city and the details of five stations in the city were studied. The results show that the short-term, seasonal, and long-term component variances of maximum daily running average 8 h (MDA8) ozone in Zibo city (City) decomposed by the KZ filter account for 32.06%, 61.67% and 1.15% of the total variance, for a specific station, the values were 32.37%–34.90%, 56.64%–62.00%, and .35%–3.14%, respectively. The average long-term component increase rate is 3.19 μg m−3 yr−1 on average for the city, while it is 1.52–5.95 μg m−3 yr−1 for a specific station. The overall meteorological impact was not stable and fluctuated between −2.60 μg m−3 and +3.77 μg m−3. This difference in trends between the city and specific stations implied that the O3 precursor’s mitigation strategy should be more precise to improve its practical effects.

Contributions of meteorology to ozone variations: Application of deep learning and the Kolmogorov-Zurbenko filter

From hourly ozone observations obtained from three regions⸻Houston, Dallas, and West Texas⸻we investigated the contributions of meteorology to changes in surface daily maximum 8-h average (MDA8) ozone from 2000 to 2019. We applied a deep convolutional neural network and Shapely additive explanation (SHAP) to examine the complex underlying nonlinearity between variations of surface ozone and meteorological factors. Results of the models showed that between 2000 and 2019, specific humidity (38% and 27%) and temperature (28% and 37%) contributed the most to ozone formation over the Houston and Dallas metropolitan areas, respectively. On the other hand, the results show that solar radiation (50%) strongly impacted ozone variation over West Texas during this time. Using a combination of the Kolmogorov-Zurbenko (KZ) filter and multiple linear regression, we also evaluated the influence of meteorology on ozone and quantified the contributions of meteorological parameters to trends in surface ozone formation. Our findings showed that in Houston and Dallas, meteorology influenced ozone variations to a large extent. The impacts of meteorology on West Texas, however, showed meteorological factors had fewer influences on ozone variabilities from 2000 to 2019. This study showed that SHAP analysis and the KZ approach can investigate the contributions of the meteorological factors on ozone concentrations and help policymakers enact effective ozone mitigation policies.

Estimation of background concentration of ambient pollutants for Delhi NCT region

The increased levels of ambient air pollution in Delhi are hazardous to human health and a matter of concern for policymakers. To develop effective regulatory policies, it is necessary to gain a better understanding of the regional background concentrations of pollutants. In the present study, we proposed a method to estimate regional background concentration for the Delhi NCT region. The study uses an integrated approach that employs meteorological filtering and statistical techniques. The meteorological filtering factored in the phenomena of stagnation caused by low wind speed and temperature inversion. Whereas spectral analysis and the Kolmogorov-Zurbenko (KZ) filter were used in combination to reduce the influence of synoptic fluctuation in the time series. Our estimated annual average background concentration for PM10, PM2.5, NO2, SO2 and CO are 56 μg/m3, 42 μg/m3, 18 μg/m3, 7 μg/m3 and 558 μg/m3 respectively. The estimated background concentrations were validated using the ambient pollutant concentration recorded during the 2020 Covid-19 lockdown period.

Remarkable spring increase overwhelmed hard-earned autumn decrease in ozone pollution from 2005 to 2017 at a suburban site in Hong Kong, South China

Ozone (O3) pollution has been a persistent problem in Hong Kong, particularly in autumn when severe O3 pollution events are often observed. In this study, linear regression analyses of long-term O3 data in suburban Hong Kong revealed that the variation of autumn O3 obviously leveled off during 2005–2017, mainly due to the significant decrease of autumn O3 in 2013–2017 (period II), despite the increase in 2005–2012 (period I). In addition, the rise of O3 in summer and winter also ceased since 2013. In contrary, O3 continuously increased throughout the spring of 2005–2017, especially in period II. Consequently, an incessant increase of overall O3 was observed during 2005–2017. A statistical model combining Kolmogorov-Zurbenko filter with multiple linear regressions, and a photochemical box model incorporating CB05 mechanism were applied to probe the causes of the above trends. In general, O3 production was controlled by VOC-limited regime throughout 13 years. The meteorological variability and regional transport facilitated the O3 growth in period Ι. In contrast, the unchanged O3 level in period II was attributable to the negative impact of meteorological variability and reduction of regional transport effect on O3 formation and accumulation, as well as the negligible change in locally-produced O3. In autumn of period II, the inhibitory meteorological variability, reduced regional transport, and alleviated local production were the driving force for the hard-earned decrease of O3. However, the remarkable rise of spring O3 was caused by the reduction of NOx, especially in the spring of period II. The findings of the long-term and seasonal variations of O3 pollution in Hong Kong are helpful for future O3 mitigation.

Evaluating temporally decomposed associations between PM2.5 and hospitalisation risks of AECOPD: A case study in Beijing from 2010 to 2019

Few studies investigated relative contributions of PM2.5 concentration at multiple time-scales (short-term, seasonal and long-term periods) on the hospitalisation risk for acute exacerbation of COPD (AECOPD). In this study, we specified and discriminated the short-term, seasonal and long-term trend effects of PM2.5 concentration on the hospitalisation for AECOPD in Beijing between 2010 and 2019. Daily PM2.5 observations from US Beijing Embassy from Jan 1, 2010, to Dec 31, 2019 (3652 days) were decomposed to short-term, seasonal and long-term trend components by using the robust Kolmogorov-Zurbenko filter. During the study period, daily counts of AECOPD hospitalisation were obtained from a database compiled by Beijing Public Health Information Center. Two separate generalized additive models were built to assess effects of raw PM2.5 concentrations and the three decomposed components of time-scales on hospitalisation of AECOPD in Beijing. We found raw PM2.5 concentrations were associated with AECOPD in Beijing with a 10 μg/m3 increase corresponding to 0.46% (95% CI: 0.40%–0.52%) increase in hospitalisation. In the model of decomposed PM2.5 time-series, both the short-term and long-term components exhibited statistically significant positive association with AECOPD, which, respectively, was associated to 0.42% (95% CI: 0.36%–0.48%) and 6.91% (95% CI: 6.08%–7.74%) increase in AECOPD hospitalisation per 10 μg/m3 increase. The seasonal trend had an insignificant U-shaped relationship with AECOPD. Our study simultaneously confirmed that a statistically significant positive association between the raw PM2.5 concentrations, the decomposed short-term and long-term trend of PM2.5 concentrations and increased risk for AECOPD hospitalisation. Strong long-term effects in this study indicated that stringent emission control measures would bring strong public health benefits concerning AECOPD.

Simulation of surface ozone over Hebei province, China using Kolmogorov-Zurbenko and artificial neural network (KZ-ANN) combined model

The role of precursors' concentrations and meteorological conditions on the emerging ozone pollution problem in China has received wide attention, especially after the releasing of the Air Pollution Prevention Control and Action Plan (APPC) since 2013. With the decreasing trend of PM2.5 nationwide, the effect of the strict control measures on increasing ozone variation has less been studied due to the challenge of complexity of nonlinear relationship among a number of factors on ozone formation. This paper evaluated the influence of both ozone precursors and meteorology on maximum daily average 8 h (MDA8) ozone at two urban sites and one rural site in Hebei province, China, from 2013 to 2017, by using a combined application of Kolmogorov-Zurbenko (KZ) filter and artificial neural network (ANN) model. Results showed that R2 was 0.80 between the measured and the simulated MDA8 ozone concentration when using meteorological factors and precursors' concentrations as input variables for ANN model. However, ANN model has limitation in estimating O3 concentration peaks. The values of threat score (TS), probability of detection (POD) and false alarm rate (FAR) for MDA8 ozone concentration were 39%, 44% and 21%, respectively, throughout three studied sites in Hebei province. The annual average percentage change of precursor-related ozone was 0.67% from 2014 to 2017. Temperature, atmospheric pressure and boundary layer height were shown to account for 64% of the variability in long-term ozone levels. Ozone variation in Hebei province was reproduced mainly by meteorological parameters, and the contribution from precursors’ concentration was smaller during the years when the APPC was implemented.

A comprehensive investigation of surface ozone pollution in China, 2015–2019: Separating the contributions from meteorology and precursor emissions

Despite the considerable reductions in primary and secondary air pollutants in China, surface ozone levels have increased in recent years. We report a trend of 3.3 ± 4.7 μg.m−3 year−1 in the annual mean maximum daily average ozone over an 8-h period (MDA8 ozone) across China between 2015 and 2019. Leveraging the Kolmogorov–Zurbenko filter method, we find that meteorology enhanced the ozone levels in Beijing–Tianjin–Hebei (BTH), the Yangtze River Delta (YRD), and the Pearl River Delta (PRD) while the reduction of solar radiation and the planetary boundary layer height accelerated ozone decreases in the Sichuan Basin (SCB) after 2017. Solar radiation and temperature increases, together with the reduction in sea level pressure, were the main contributors to enhance ozone in the YRD. They also contributed to 32% of ozone increases in BTH. Weaker meridional wind, lower relative humidity, and higher temperature escalated ozone enhancement in the PRD between 2016 and 2018. Regarding precursor emissions, NO2 long-term components showed a noticeable decline in all regions after 2017, partially due to the introduction of the most current action plan to reduce air pollutants over China in 2018. In contrast, the satellite-retrieved data suggest that VOC concentrations did not change substantially in target regions during the study period. After 2017, however, VOCs slightly increased in BTH, the YRD, and the PRD, which might be driven by temperature enhancements. Overall, the impact of meteorology on ozone variations was dominant in the YRD, the PRD, and the SCB from 2015 to 2019. Precursor emissions, however, played a leading role in ozone enhancement over the BTH. We also found that BTH and the YRD were in a transitional ozone formation regime while the PRD and the SCB tended to be more NOx-sensitive.