Geographical distribution of ozone seasonality over China

Parsimonious estimation of hourly surface ozone concentration across China during 2015-2020

Surface ozone is an important air pollutant detrimental to human health and vegetation productivity, particularly in China. However, high resolution surface ozone concentration data is still lacking, largely hindering accurate assessment of associated environmental impacts. Here, we collected hourly ground ozone observations (over 6 million records), remote sensing products, meteorological data, and social-economic information, and applied recurrent neural networks to map hourly surface ozone data (HrSOD) at a 0.1° × 0.1° resolution across China during 2015-2020. The coefficient of determination (R2) values in sample-based, site-based, and by-year cross-validations were 0.72, 0.65 and 0.71, respectively, with the root mean square error (RMSE) values being 11.71 ppb (mean = 30.89 ppb), 12.81 ppb (mean = 30.96 ppb) and 11.14 ppb (mean = 31.26 ppb). Moreover, it exhibits high spatiotemporal consistency with ground-level observations at different time scales (diurnal, seasonal, annual), and at various spatial levels (individual sites and regional scales). Meanwhile, the HrSOD provides critical information for fine-resolution assessment of surface ozone impacts on environmental and human benefits.

Ozone and its precursors at an urban site in the Yangtze River Delta since clean air action plan phase II in China

In response to regional ozone (O3) pollution, Chinese government has implemented air pollution control measures in recent years. Here, a case study was performed at an O3-polluted city, Wuhu, in Yangtze River Delta region of China to investigate O3 variation trend and the relationship to its precursors after implementation of Clean Air Action Plan Phase II, which aims to reduce O3 pollution. The results showed that peak O3 concentration was effectively reduced since Clean Air Action Plan Phase II. Due to significant NOx reduction, O3 formation tended to shift from volatile organic compound (VOC)-limited regimes to NOx-limited regimes during 2018-2022. VOC/NOx ratios measured in 2022 revealed that peak O3 concentration tended to respond positively to NOx. Apart from high-O3 period, Wuhu was still in a VOC-limited regime. The relationship of maximum daily 8-h ozone average and NO2 followed a lognormal distribution with an inflection point at 20 μg m-3 of NO2, suggesting that Wuhu should conduct joint control of VOC and NOx with a focus on VOC reduction before the inflection point. Alkenes and aromatics were suggested to be preferentially controlled due to their higher ozone formation potentials. Using random forest meteorological normalization method, meteorology had a positive effect on O3 concentration in 2018, 2019 and 2022, but a negative effect in 2020 and 2021. The meteorology could explain 44.0 ± 19.1% of the O3 variation during 2018-2022. High temperature favors O3 production and O3 pollution occurred more easily when temperature was over 25 °C, while high relative humidity inhibits O3 generation and no O3 pollution was found at relative humidity above 70%. This study unveils some new insights into the trend of urban O3 pollution in Yangtze River Delta region since Clean Air Action Plan Phase II and the findings provide important references for formulating control strategies against O3 pollution.

Driving Forces of Meteorology and Emission Changes on Surface Ozone in the Huaihe River Basin, China

Surface ozone (O3) pollution in China has become a serious environmental problem in recent years. In the present study, we targeted the HRB, a large region located in China's north-south border zone, to assess the driving forces of meteorology and emission changes on surface ozone. A Kolmogorov-Zurbenko (KZ) filter method was performed on the maximum daily average 8-h (MDA8) concentrations of ozone in the HRB during 2015-2020 to decompose the original time series. The findings demonstrated that the short-term (O3ST), seasonal (O3SN), and long-term components (O3LT) of MDA8 O3 variations accounted for 34.2%, 56.1%, and 2.9% of the total variance, respectively. O3SN has the greatest influence on the daily variation in MDA8 O3, followed by O3ST. In coastal cities, the influence of O3ST was enhanced. The influence of O3SN was stronger in the northwestern HRB. Air temperature is the prevailing variable that influences the photochemical formation of ozone. A clear phase lag (7-34 days) of the baseline component between MDA8 O3 and the atmospheric temperature was found in the HRB. Using multiple linear regression, the effect of temperature on ozone was removed. We estimated that the increase in ozone concentration in the HRB was mainly caused by the emission changes (79.4%), and the meteorological conditions made a small contribution (20.6%). This study suggests that reductions in volatile organic compounds (VOCs) will play an important role in further ozone pollution reduction in the HRB.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11270-023-06345-1.

A new approach of air pollution regionalization based on geographically weighted variations for multi-pollutants in China

Air pollution regionalization is a key and necessary action to identify pollution regions for implementing control measures. Here we present a new approach called Geographically Weighted Rotation Empirical Orthogonal Function (GWREOF) for air pollution regionalization in China. Compared with previous methods, such as EOF, REOF, and K-mean, GWREOF better accounts for the variability of air pollution conditions driven by emission patterns and meteorology with centralized spatial locations. We apply GWREOF to multiple air pollutants (such as PM_2.5, O₃, and other monitored air pollutants) and air quality metrics using their measured spatial and temporal variations in 337 Chinese cities over 2015-2020. We find that the regionalization results for different air pollutants are highly similar, primarily determined by topography and meteorological conditions in China. Therefore, we propose an integrated regionalization result, which identifies 18 air pollution control regions in China and can be applied to multiple pollutants and different years. We further analyze PM_2.5, O₃, and O_X (O₃ + NO₂) pollution levels and their correlations in these regions. PM_2.5 and O₃ correlations are generally strongly positive in southern China while negative in northern China. However, PM_2.5 and O_X correlations are broadly positive in China, reflecting the crucial role of atmospheric oxidizing capacity. Regional-specific and coordinated control measures are in need as China's air pollution strategy transits from PM_2.5-focused to PM_2.5-O₃ synergic control.

Association analysis between socioeconomic factors and urban ozone pollution in China

Ozone pollution in China has gradually increased, attracting extensive attention. Existing studies on ozone pollution typically take environmental and chemical perspectives. As air pollution is closely related to social and economic activities, it is also important to study ozone pollution from a socioeconomic perspective. Using the association rule mining technique, we uncovered hidden patterns between ozone variance and socioeconomic factors in macro-, meso-, and micro-scenarios in 297 Chinese cities. We found that the acceleration of urbanization and industrialization has indeed aggravated urban ozone pollution. The supply of water and power resources may be a significant factor influencing urban ozone pollution. Transportation hub cities with more developed economies and industries are more likely to suffer from ozone pollution in summer and autumn. Human behavior is a critical factor influencing the weekly variance in ozone concentration during weekdays and weekends. The influence of plant-derived VOC emissions on the formation of ozone cannot be overlooked. Our results deepen the understanding of ozone pollution in Chinese cities, and we provide corresponding policy recommendations to alleviate ozone pollution and improve air quality.

Study on Air Quality and Its Annual Fluctuation in China Based on Cluster Analysis

Exploring the spatial and temporal distribution characteristics of air quality has become an important topic for the harmonious development of human and nature. Based on the hourly data of CO, O₃, NO₂, SO₂, PM_2.5 and PM₁₀ of 1427 air quality monitoring stations in China in 2016, this paper calculated the annual mean and annual standard deviation of six air quality indicators at each station to obtain 12 variables. Self-Organizing Maps (SOM) and K-means clustering algorithms were carried out based on MATLAB and SPSS Statistics, respectively. Kriging interpolation was used to get the clustering distribution of air quality and fluctuation in China, and Principal Component Analysis (PCA) was used to analyze the main factors affecting the clustering results. The results show that: (1) Most areas in China are low-value regions, while the high-value region is the smallest and more concentrated. Air quality in northern China is worse, and the annual fluctuations of the indicators are more dramatic. (2) Compared with AQI, AQFI has a strong indication significance for the comprehensive situation of air quality and its fluctuation. (3) The spatial distribution of SOM clustering results is more discriminative, while K-means clustering results have a large proportion of low-mean regions. (4) PM_2.5, PM₁₀ and CO are the main pollutants affecting air quality and fluctuation, followed by SO₂, NO₂ and O₃.

Spatiotemporal Patterns and Regional Transport of Ground-Level Ozone in Major Urban Agglomerations in China

Ground-level ozone (O3) pollution has become a serious environmental issue in major urban agglomerations in China. To investigate the spatiotemporal patterns and regional transports of O3 in Beijing–Tianjin–Hebei (BTH-UA), the Yangtze River Delta (YRD-UA), the Triangle of Central China (TC-UA), Chengdu–Chongqing (CY-UA), and the Pearl River Delta urban agglomeration (PRD-UA), multiple transdisciplinary methods were employed to analyze the O3-concentration data that were collected from national air quality monitoring networks operated by the China National Environmental Monitoring Center (CNEMC). It was found that although ozone concentrations have decreased in recent years, ozone pollution is still a serious issue in China. O3 exhibited different spatiotemporal patterns in the five urban agglomerations. In terms of monthly variations, O3 had a unimodal structure in BTH-UA but a bimodal structure in the other urban agglomerations. The maximum O3 concentration was in autumn in PRD-UA, but in summer in the other urban agglomerations. In spatial distribution, the main distribution of O3 concentration was aligned in northeast–southwest direction for BTH-UA and CY-UA, but in northwest–southeast direction for YRD-UA, TC-UA, and PRD-UA. O3 concentrations exhibited positive spatial autocorrelations in BTH-UA, YRD-UA, and TC-UA, but negative spatial autocorrelations in CY-UA and PRD-UA. Variations in O3 concentration were more affected by weather fluctuations in coastal cities while the variations were more affected by seasonal changes in inland cities. O3 transport in the center cities of the five urban agglomerations was examined by backward trajectory and potential source analyses. Local areas mainly contributed to the O3 concentrations in the five cities, but regional transport also played a significant role. Our findings suggest joint efforts across cities and regions will be necessary to reduce O3 pollution in major urban agglomerations in China.

When a Generalized Linear Model Meets Bayesian Maximum Entropy: A Novel Spatiotemporal Ground-Level Ozone Concentration Retrieval Method

In China, ground-level ozone has shown an increasing trend and has become a serious ambient pollutant. An accurate spatiotemporal distribution of ground-level ozone concentrations (GOCs) is urgently needed. Generalized linear models (GLMs) and Bayesian maximum entropy (BME) models are practical for predicting GOCs. However, GLMs have limited capacity to capture temporal variations and can miss some short-term and regional patterns, while the performance of BME models may degrade in cases of sparse or imperfect monitoring networks. Thus, to predict nationwide 1 km monthly average GOCs for China, we designed a novel hybrid model containing three modules. (1) A GLM was established to accurately describe the variability in GOCs in the space domain. (2) A BME model incorporating GLM residuals was employed to capture the temporal variability of GOCs in detail. (3) A combination of GLM and BME models was developed based on the specific broad range of each submodel. According to the cross-validation results, the hybrid model exhibited superior performance, with coefficient of determination (R2) values of 0.67. The predictive performance of the large-scale and high-resolution hybrid model is superior to that in previous studies. The nationwide spatiotemporal variability of the GOCs derived from the hybrid model shows that they are valuable indicators for ground-level ozone pollution control and prevention in China.

Analysis of Ozone Pollution Characteristics and Influencing Factors in Northeast Economic Cooperation Region, China

The increase in tropospheric ozone (O3) concentration has become one of the factors restricting urban development. This paper selected the important economic cooperation areas in Northeast China as the research object and collected the hourly monitoring data of pollutants and meteorological data in 11 cities from 1 January 2015 to 31 December 2019. The temporal and spatial variation trend of O3 concentration and the effects of meteorological factors and other pollutants, including CO (carbon monoxide), SO2 (sulfur dioxide), NO2 (nitrogen dioxide), and PM2.5 and PM10 (PM particles with aerodynamic diameters less than 2.5 μm and 10 μm) on ozone concentration were analyzed. At the same time, the variation period of O3 concentration was further analyzed by Morlet wavelet analysis. The results showed that the O3 pollution in the study area had a significant spatial correlation. The spatial distribution showed that the O3 concentration was relatively high in the south and low in the northeast. Seasonally, the O3 concentration was the highest in spring, followed by summer, and the lowest in winter. The diurnal variation of O3 concentration presented a “single peak” pattern. O3 concentration had a significant positive correlation with temperature, sunshine duration, and wind speed and a significant anticorrelation with CO, NO2, SO2, and PM2.5 concentration. Under the time scale of a = 9, 23, O3 had significant periodic fluctuation, which was similar to those of wind speed and temperature.