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Water security is essential for ensuring energy security, sustainable development, and human survival. However, due to a series of challenges, including rising water demand, environmental pollution, and water resource shortages, the global water security situation remains concerning and poses a threat to global sustainable development. To assess water security in China, this study uses data from 30 provinces in China from 2012 to 2021. A comprehensive evaluation method was applied to determine the level of water resource security in China. The Dagum Gini coefficient, Moran index, and spatial model were used to clarify regional differentiation characteristics and the driving factors. The results indicate that while China's water resource security is relatively low, it has shown steady improvement in recent years. Significant regional disparities exist in water resource security across China, with notable spatial characteristics, and socio-economic factors are the primary causes of these differences. Based on the above research, we put forward policy recommendations from the aspects of water resources management, public participation and inter-regional water resources cooperation, to provide reference for water resources security in developing countries.

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The spatial-temporal distribution pattern of surface O3 over the Qinghai-Xizang Plateau （QXP） was analyzed based on air quality monitoring data and meteorological data from 12 cities on the QXP from 2015 to 2021. Kolmogorov-Zurbenko （KZ） filtering was employed to separate the original O3-8h series into components at different time scales. Then, multiple linear regression of meteorological variables was used to quantitatively isolate the effects of meteorology and emissions. The results revealed that the annual mass concentrations of surface O3-8h from 2015 to 2021 in 12 cities over the QXP ranged from 78.7 to 156.7 µg·m-3, and the exceedance rates of O3 mass concentrations （National Air Quality Standard of grade II） ranged from 0.7%-1.5%. The monthly O3-8h mass concentration displayed a single-peak inverted "V"-shape and a multi-peak "M"-shape. The maximum monthly concentration of O3 occurred in April to July, and valleys occurred in July, September, and December. The short-term, seasonal, and long-term components of O3-8hdecomposed by KZ filtering contributed 29.6%, 51.4%, and 9.1% to the total variance in the original O3 sequence in 12 cities, respectively. From the whole region, the meteorological conditions were unfavorable for O3 reduction on the QXP from 2015 to 2017, which made the long-term component of O3 increase by 0.2-2.1 µg·m-3. The meteorological conditions were favorable for O3-8h reduction from 2018 to 2021, which led to the long-term component of O3-8h decrease by 0.4-1.1 µg·m-3. The meteorological conditions increased the long-term component of O3-8h in Ngari, Lhasa, Naqu, Nyingchi, Qamdo, Haixi, and Xining, with an average contribution of 30.1%. The meteorological conditions decreased the long-term component of O3-8h in Shigatse and Golog, with contributions of 359.0% and 56.5%, respectively. The increase in the long-term component of O3-8h in Ngari, Shigatse, Nagqu, Haixi, and Xining could be due to the rapid decrease in the long-term component of PM2.5 （4.04 µg·ï¼ˆm3·a）-1）.

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Top-blowing furnace systems, characterized by a large number of sensors and harsh working environments, are prone to sensor failures due to factors like component aging and external interference. These failures can significantly impact the system's safe and reliable operation. However, traditional sensor fault diagnosis methods often neglect the exploration of spatial-temporal characteristics and focus solely on learning temporal relationships between sensors, failing to effectively consider their spatial relationships. In this study, we propose a spatial correlation model based on the maximal information-based graph convolutional network (MI-GCN) by constructing a sensor network knowledge graph using maximal mutual information. The MI-GCN leverages the graph convolution mechanism to extract multi-scale spatial features and capture the spatial relationships between sensors. Additionally, we develop a spatial-temporal graph-level prediction model, known as the spatial-temporal graph transformer (STGT), to extract temporal features. By combining the spatial features extracted by the MI-GCN with the temporal features captured by the STGT, accurate predictions can be achieved. Sensor fault diagnosis is conducted by analysing the normalized residuals between the predicted values and the ground truth. Finally, the feasibility and effectiveness of the proposed method are validated using test data from a top-blowing furnace system in the nickel smelting process.

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The Chengdu-Chongqing twin-city economic circle is a vital growth pole and a new power source for Chinese high-quality development. Studying the spatial-temporal characteristics of carbon emissions and the role of factors affecting them under the transportation perspective is of great significance for this region to realize the carbon peak and carbon neutrality and to formulate carbon emission reduction policies. We use the exploring spatial data analysis (ESDA) and spatial regression model combined with the STIRPAT model, and research finding: (1) The total carbon emissions in the research area gradually increased from 2014 to 2020, but the growth rate showed a significant decline in 2019. (2) There is significant spatial heterogeneity of carbon emissions in the study area; the hotspot areas of total carbon emissions are in Chongqing and Chengdu, forming a high-low aggregation of carbon emissions. Per capita carbon emissions show a high trend in the southwest and a low in the northeast. (3) From the factors of transportation perspective, highway density and private vehicles have a positive impact on carbon emissions, and urban road areas and public transportation have a very significant inhibition of carbon emissions and a spatial spillover effect. (4) Other factors, such as population size, national economic development, urbanization level, and industrial structure, all have a positive effect on carbon emissions, and disposable income has a negative effect on carbon emissions.

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Comprehending the spatial-temporal characteristics, contributions, and evolution of driving factors in agricultural non-CO2 greenhouse gas (GHG) emissions at a macro level is pivotal in pursuing temperature control objectives and achieving China's strategic goals related to carbon peak and carbon neutrality. This study employs the Intergovernmental Panel on Climate Change (IPCC) carbon emissions coefficient method to comprehensively evaluate agricultural non-CO2 GHG emissions at the provincial level. Subsequently, the contributions and spatial-temporal evolution of six driving factors derived from the Kaya identity were quantitatively explored using the Logarithmic Mean Divisia Index (LMDI) and Geographical and Temporal Weighted Regression (GTWR) methods. The results revealed that the distribution of agricultural non-CO2 GHG emissions is shifting from the central provinces to the northwest regions. Moreover, the dominant driving factors of agricultural non-CO2 GHG emissions were primarily economic factor (EDL) with positive impact (cumulative promotion is 2939.61 million metric tons (Mt)), alongside agricultural production efficiency factor (EI) with negative impact (cumulative reduction is 2208.98 Mt). Influence of EDL diminished in the eastern coastal regions but significantly impacted underdeveloped regions such as the northwest and southwest. In the eastern coastal regions, EI gradually became the absolute dominant driver, demonstrating a rapid reduction effect. Additionally, a declining birth rate and rural-to-urban population migration have significantly amplified the driving effects of the population factor (RP) at a national scale. These findings, in conjunction with the disparities in geographic and socioeconomic development among provinces, can serve as a guiding framework for the development of a region-specific roadmap aimed at reducing agricultural non-CO2 GHG emissions.

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Urban shrinkage has become increasingly prevalent in the context of global urbanization. Understanding its spatio-temporal characteristics and influencing factors is of great significance for promoting sustainable development and high-quality urbanization. This paper identified county-level shrinking cities in Heilongjiang Province from the perspective of urban entities. It employed spatial autocorrelation methods to analyze their spatial-temporal characteristics and explored the factors that contributed to this phenomenon. The results indicated that between 2010 and 2020, the phenomenon of urban shrinkage at the county level in Heilongjiang Province was primarily concentrated in the province's hinterland. Moreover, they exhibited characteristics of the Great Recession and Small Growth. The main factors influencing the shrinkage included the urban industrial structure, living standards, and population composition.

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Background: The 2022 Beijing Winter Olympics is a representative large-scale sporting event, which not only promotes the development of the national and regional economy, society, and culture but also increases the demand of residents for winter sports, thus helping achieve the grand goal of "encouraging 300 million people to participate in winter sports." This research explores the influence of the Beijing Winter Olympics on residents' demand for winter sports in the Beijing-Tianjin-Hebei and Yangtze River Delta urban agglomerations in China. Methods: Applying big data mining techniques, the Baidu Index of Winter Olympics-related terms are used to measure residents' interest in the Beijing Winter Olympics, and the ratio of the Baidu Index of five winter sports (ice skating, ice hockey, curling, luge, and skiing) to the number of internet searches is used to capture residents' demand for winter sports. Moreover, we explore the spatial-temporal pattern of the interest in the Winter Olympics and the demand for winter sports and construct an econometric model to test the driving effect of the Winter Olympics empirically. Results: The results show that 1) since 2011, interest in Winter Olympics has been on the rise, and the interest of residents in Beijing-Tianjin-Hebei has been higher than that of the Yangtze River Delta; 2) the demand for skating and skiing, which are two popular winter sports, shows a declining geographical concentration, indicating that the popularity of these two sports is on the increase; 3) the demand for winter sports in the peripheral cities in Beijing-Tianjin-Hebei shows a trend of specialization, while Beijing, Tianjin, and some cities in the Yangtze River Delta present a trend of diversification; and 4) the interest in the Beijing Winter Olympics influences the demand for winter sports positively. Conclusion: This study shows that the increase in interest in the Beijing Winter Olympics boosts residents' demand for winter sports, which implies that hosting Winter Olympics successfully drives winter sports participation in China.

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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.

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Electroencephalogram (EEG)-based emotion computing has become a hot topic of brain-computer fusion. EEG signals have inherent temporal and spatial characteristics. However, existing studies did not fully consider the two properties. In addition, the position encoding mechanism in the vanilla transformer cannot effectively encode the continuous temporal character of the emotion. A temporal relative (TR) encoding mechanism is proposed to encode the temporal EEG signals for constructing the temporality self-attention in the transformer. To explore the contribution of each EEG channel corresponding to the electrode on the cerebral cortex to emotion analysis, a channel-attention (CA) mechanism is presented. The temporality self-attention mechanism cooperates with the channel-attention mechanism to utilize the temporal and spatial information of EEG signals simultaneously by preprocessing. Exhaustive experiments are conducted on the DEAP dataset, including the binary classification on valence, arousal, dominance, and liking. Furthermore, the discrete emotion category classification task is also conducted by mapping the dimensional annotations of DEAP into discrete emotion categories (5-class). Experimental results demonstrate that our model outperforms the advanced methods for all classification tasks.

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Zhejiang Province is a "demonstration area for high-quality development and construction of common prosperity" in China. Moreover, the county is the basic unit and power source for the economic development of Zhejiang Province. Therefore, the research on the spatial-temporal characteristics and influencing factors of county-level carbon emissions is of great significance for Zhejiang Province to achieve the strategic goal of carbon peak and carbon neutrality. Based on the carbon emissions and socio-economic data of 62 counties in Zhejiang Province from 2014 to 2020, the spatial dependence and agglomeration of county-level carbon emissions are analyzed through the spatial autocorrelation test and local spatial autocorrelation test respectively. According to the spatial-temporal characteristics of county-level carbon emissions revealed by the index of Moran's I and local Moran's I, the spatial error STIRPAT model is used to study the influencing factors of county-level carbon emissions in Zhejiang Province, China. The main results are as follows: (1) The total amount of county-level carbon emissions of 62 counties fluctuates from 259.69 to 326.28 million tons and shows a growth trend. (2) Moran's I index is between 0.369 and 0.399. The county-level carbon emissions have a significant spatial correlation, and the spatial agglomeration trend is relatively stable, which is consistent with the hypothesis of the geographical polarization effect. (3) High-high agglomeration counties are concentrated in the northeast of Zhejiang Province, while low-low agglomeration counties are mainly in the southwest. (4) The relationship between county per capita GDP and carbon emissions has not been "decoupled," because when other variables remain unchanged, the county's total carbon emissions will increase by 2.866% for every 1% increase in the county's per capita GDP; the increase of the proportion of secondary industry contributes to the decline of carbon emissions, and the low-carbon effect brought by large-scale industrial development as well as scientific and technological innovation has not yet appeared. (5) The estimate of the spatial coefficient λ was 0.324, which illustrates that the carbon emission of a single county is positively affected by the carbon emission of the neighboring counties, and other socio-economic factors affecting carbon emission among counties also have a spatial correlation. Therefore, the policy of realizing regional coordinated development as well as the carbon peaking and carbon neutrality goals should not only focus on industrial layout, but also take a dynamic and comprehensive consideration from a spatial perspective.

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Both the realization of the "double carbon" goal and the low-carbon economy development requires a focus on transportation CO2 emissions. Calculating Chinese transportation CO2 emissions and exploring its principles are essential for achieving high-quality development of the transportation industry. Firstly, we use a "top-down" method to assess carbon emissions from transportation operations from 2003 to 2019. Secondly, the study decomposes the influencing factors of transportation CO2 emissions in China using the log-average weight decomposition method. Thirdly, the Tapio decoupling model is applied to study the decoupling effect of transportation CO2 emissions in each province of China. The findings suggest that China's transport carbon emissions are growing at an annual rate of roughly 16%. All GDP per capita, transportation energy intensity, and population size increase the growth of transportation CO2 emissions. Contrastly, energy use per unit of turnover and transportation intensity decrease the growth of transportation CO2 emissions. There is much variation in China's carbon emission decoupling index from year to year. Policy recommendations are proposed in response to the study of the above findings and the differences in carbon reduction potential among provinces.

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Background: With the growth of trajectory data, the large amount of data causes a lot of problems with storage, analysis, mining, etc. Most of the traditional trajectory data compression methods are focused on preserving spatial characteristic information and pay little attention to other temporal information on trajectory data, such as speed change points or stop points. Methods: A data compression algorithm based on the spatio-temporal characteristics (CASC) of the trajectory data is proposed to solve this problem. This algorithm compresses trajectory data by taking the azimuth difference, velocity difference and time interval as parameters in order to preserve spatial-temporal characteristics. Microsoft's Geolife1.3 data set was used for a compression test to verify the validity of the algorithm. The compression results were compared with the traditional Douglas-Peucker (DP), Top-Down Time Ratio (TD-TR) and Opening Window (OPW) algorithms. Compression rate, the direction information of trajectory points, vertical synchronization distance, and algorithm type (online/offline) were used to evaluate the above algorithms. Results: The experimental results show that with the same compression rate, the ability of the CASC to retain the forward direction trajectory is optimal, followed by TD-TR, DP, and then OPW. The velocity characteristics of the trajectories are also stably retained when the speed threshold value is not more than 100%. Unlike the DP and TD-TR algorithms, CASC is an online algorithm. Compared with OPW, which is also an online algorithm, CASC has better compression quality. The error distributions of the four algorithms have been compared, and CASC is the most stable algorithm. Taken together, CASC outperforms DP, TD-TR and OPW in trajectory compression.

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PM2.5 is the main component of haze, and Henan Province has become one of the key areas of PM2.5 pollution control. Based on the PM2.5 concentration data of Henan Province from 2015 to 2019, spatial autocorrelation, spatial hot spot detection, and other methods were used to analyze its temporal and spatial characteristics, and the geodetector method was introduced to analyze the interpretation strength of meteorological factors, air quality factors, and social factors on PM2.5 concentration. The results showed that:from 2015 to 2019, the concentration of PM2.5 in Henan Province showed an overall downward trend, the days of high pollution decreased, the days of low pollution increased, and the high pollution gradually transformed into medium pollution. The concentration of PM2.5 had obvious characteristics of spatial aggregation. The five-year global spatial autocorrelation index first dropped and then rose, and the spatial hot spots were concentrated in northern Henan (Anyang, Hebi, Xinxiang, and Jiaozuo); the spatial cold spots were concentrated in western Henan (Sanmenxia, Luoyang, and Nanyang). The shift in space center of gravity showed a trend of going north. Single-factor detection showed that among the nine influencing factors, land use type (0.511), precipitation (0.312), and NO2(0.277) were the most obvious factors affecting PM2.5 concentration, and the other factors were PM10(0.255), temperature (0.209), wind speed (0.183), O3(0.121), GDP(0.073), and population (0.046). Interaction detection showed that the combined effect of multiple factors was more significant than that of single factors. These results can provide theoretical support for the control of air pollution in Henan Province.

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Urban population density distribution contributes towards a deeper understanding of peoples' activities patterns and urban vibrancy. The associations between the distribution of urban population density and land use are crucial to improve urban spatial structure. Despite numerous studies on population density distribution and land use, the significance of spatial dependence has attained less attention. Based on the Baidu heat map data and points of interests data in the main urban zone of Guangzhou, China, the current paper first investigated the spatial evolution and temporal distribution characteristics of urban population density and examined the spatial spillover influence of land use on it through spatial correlation analysis methods and the spatial Durbin model. The results show that the urban population density distribution is characterized by aggregation in general and varies on weekends and weekdays. The changes in population density within a day present a trend of "rapid growth-gentle decline-rapid growth-rapid decline". Furthermore, the spatial spillover effects of land use exist and play the same important roles in population density distribution as the direct effects. Additionally, different types of land use show diverse direct effects and spatial spillover effects at various times. These findings suggest that balancing the population density distribution should consider the indirect effect from neighboring areas, which hopefully provide implications for urban planners and policy makers in utilizing the rational allocation of public resources and regarding optimization of urban spatial structure.

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COVID-19 has brought many unfavorable effects on humankind and taken away many lives. Only by understanding it more profoundly and comprehensively can it be soundly defeated. This paper is dedicated to studying the spatial-temporal characteristics of the epidemic development at the provincial-level in mainland China and the civic-level in Hubei Province. Moreover, a correlation analysis on the possible factors that cause the spatial differences in the epidemic's degree is conducted. After completing these works, three different methods are adopted to fit the daily-change tendencies of the number of confirmed cases in mainland China and Hubei Province. The three methods are the Logical Growth Model (LGM), Polynomial fitting, and Fully Connected Neural Network (FCNN). The analysis results on the spatial-temporal differences and their influencing factors show that: (1) The Chinese government has contained the domestic epidemic in early March 2020, indicating that the number of newly diagnosed cases has almost zero increase since then. (2) Throughout the entire mainland of China, effective manual intervention measures such as community isolation and urban isolation have significantly weakened the influence of the subconscious factors that may impact the spatial differences of the epidemic. (3) The classification results based on the number of confirmed cases also prove the effectiveness of the isolation measures adopted by the governments at all levels in China from another aspect. It is reflected in the small monthly grade changes (even no change) in the provinces of mainland China and the cities in Hubei Province during the study period. Based on the experimental results of curve-fitting and considering the time cost and goodness of fit comprehensively, the Polynomial(Degree = 18) model is recommended in this paper for fitting the daily-change tendency of the number of confirmed cases.

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Environmental Information Disclosure (EID) is a new tool for environmental governance in the era of big data and information. Based on the Pollution Information Disclosure Index (PITI) of 120 cities in China from 2003 to 2019, spatial data exploratory analysis and dynamic spatial panel model were adopted to analyze the spatial-temporal evolution characteristics and influencing factors of EID in China. The results show that (1) great progress of China's EID has been made in legislation and practice and its ways and channels are gradually becoming diversified, while it is accompanied by the problem of inadequate and unbalanced development; (2) EID shows the "superposition effect" promoted by previous accumulation has the "peer effect" of mutual imitation and learning and presents "demonstration effect", which shows significant agglomeration distribution pattern of spatial "club", while the spillover effect within the region is significant while the radiation effect between regions is weak. (3) In a dynamic process, cities with better economic development, firm performance, environmental performance and regulation, disclosure more environmental information, while the role of government competition and public participation needs further discussion. (4) Negative factors have a great influence during the economic crisis, while positive factors play a significant role in promoting the disclosure of environmental information during the economic expansion after crises. Cities in the developed regions (coastal, east and large cities) disclosure more than developing regions (inland, west, and small cities), and the positive factors are more likely to take effect.

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The scientific analysis of spatial-temporal differentiation characteristics and driving factors of illegal land use is of great significance for the formulation and optimization of policies to control the emergence of illegal land use. This paper establishes two variable systems of illegal land use and its driving factors, defined the multidimensional characteristic variables of illegal land use and analyzes the relationships among them by the Pearson's correlation coefficient; In addition, the spatial-temporal characteristics of each variable of illegal land use from 2004 to 2017 are described by the spatial autocorrelation analysis; Finally, based on the geographical detectors, the influence direction and degree of the factors of economic structure, social structure and land market behavior on the characteristics of different illegal land use are studied. The results show that the spatial agglomeration of different characteristics of illegal land use had been weakening from 2004 to 2017, but the rate of weakening was different, and L-L agglomeration changed between Xinjiang and other central-western provinces, H-H agglomeration changed in the coastal regions of the central-eastern of China, the level and ability of the central government and local governments to govern illegal land use is constantly improving on the whole; the compositional factors of economic development structure, social development structure, and land market behavior of driving factors had different influence in the degree, the location or the direction of different characteristics of illegal land use. According to the spatial-temporal characteristics and the differences of driving factors, managers can formulate differentiated illegal land use control policies, which will help to control the occurrence of illegal land use and help the settlement of illegal land use cases, and ultimately achieve sustainable development.

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In the context of climate change, ecosystem in Yangtze River Source Region (YRSR) is under threat from severe droughts. This study introduced a new natural vegetation drought index, standardized supply-demand water index (SSDI), and identified natural vegetation drought events and parameters (e.g., duration, severity, peak, and coverage area) based on run theory. Then the drought-prone regions were investigated via 2-dimensional joint copula. The results indicate that (1) compared with traditional meteorological drought index, the SSDI is reliable and can reflect the comprehensive characteristics of the ecological drought information more easily and effectively; (2) the YRSR had witnessed the most severe drought episodes in the periods of late-1970s, mid-1980s, and mid-1990s, but the SSDI showed a wetting trend since the mid-2000s. Additionally, droughts in the Southern YRSR were relatively more severe with longer drought duration; (3) in most areas of Togton River Basin and Dam River Basin, the severe ecological drought events occurred more frequently; (4) drought duration and severity in the YRSR were more susceptible to temperature when the temperature rise was above 1.0 °C. The average drought duration and severity increased by 20.7% and 32.6% with a temperature rise of 1 °C. Investigating and evaluating drought characteristics, causes, and drought index effectiveness provide essential information for balanced water resource allocation, utilization, and drought prevention. Understanding these spatial-temporal characteristics of drought and return period was useful for drought risk assessment and sustainable development of water resources.

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Urbanization adversely affects the ecological environment and reduces the quality of life in China. In view of the current situation, this study aims to determine the dynamics of the ecological security pattern of urban agglomerations using the Yangtze River urban agglomeration (YRUA) as a case study. We used the pressure-state-response (PSR) framework to establish an ecological security assessment system, combined with the technique for order of preference by similarity to an ideal solution (TOPSIS) method and gray correlation method, to estimate a comprehensive ecological security index, and we analyzed its evolution trends and driving mechanisms. The results indicated that the distribution of the regional ecological security level had a linked effect and that industrial pollutants posed the greatest threat to ecological security. Moreover, the main factors affecting the YRUA were urbanization, ecopathology, economic development, population pressure, land pressure, and water resource pressure. For the protection of ecological security, it is necessary to establish an ecological security governance mechanism. Moreover, the study stresses changing the traditional sewage discharge model and establishing an ecologically safe market system. Integr Environ Assess Manag 2021;17:573-583. © 2020 SETAC.

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In recent years, there have been frequent ozone pollution episodes in Dezhou, China. In the summer of 2018 (from June to August), Dezhou experienced serious ozone pollution episodes. The daily 8-hour maximum ozone concentrations exceeded the national standard for 60 days with the standard exceeding ratio of 65%. The average of daily 8-hour maximum ozone concentration was 176 µg ·m-3 over these three months, and the highest value reached was 262 µg ·m-3. In this study, the WRF-CAMx model coupled with the higher-order decoupled direct method (HDDM) was used to analyze the ozone sensitivity and emission control plans in Dezhou during this period. The results showed that ozone formation was in the strong VOC-limited regime in the urban area of Dezhou, while it was in the NOx and VOCs transition regime in suburban areas. VOCs sensitivity values (dO3_V50) were positive every day in summer, which was higher in June (18.7 µg ·m-3 in urban area, 19.7 µg ·m-3 in suburban area) and August (15.3 µg ·m-3 in urban area, 16.4 µg ·m-3 in suburban area) than in July (13.0 µg ·m-3 in urban area, 11.8 µg ·m-3 in suburban area). NOx sensitivity values (dO3_N50) were positive or negative in the urban area, and most days were positive in the suburban area, which were close to the VOCs sensitivity values. For urban areas, VOC reduction should be the priority for emission reduction plans, whereas for suburban areas, NOx：VOCs=1：1 is recommended because the reductions in NOx and VOCs emissions had the same effect on ozone pollution control.

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