Impacts of meteorology and precursor emission change on O3 variation in Tianjin, China from 2015 to 2021

High summer background O3 levels in the desert of northwest China

Generally speaking, the precursors of ozone (O3), nitrogen oxides and volatile organic compounds are very low in desert areas due to the lack of anthropogenic emissions and natural emissions, and thus O3 concentrations are relatively low. However, high summer background concentrations of about 100 µg/m3 or 60 ppb were found in the Alxa Desert in the highland of northwest China based on continuous summer observations from 2019 to 2021, which was higher than the most of natural background areas or clean areas in world for summer O3 background concentrations. The high O3 background concentrations were related to surface features and altitude. Heavy-intensity anthropogenic activity areas in desert areas can cause increased O3 concentrations or pollution, but also generated O3 depleting substances such as nitrous oxide, which eventually reduced the regional O3 baseline values. Nitrogen dioxide (NO2) also had a dual effect on O3 generation, showing promotion at low concentrations and inhibition at high concentrations. In addition, sand-dust weather reduced O3 clearly, but O3 eventually stabilized around the background concentration values and did not vary with sand-dust particulate matter.

Long-term trends and response of wet ammonia deposition to changes in anthropogenic emissions in the Pearl River delta of China

The Pearl River Delta (PRD) region has been identified as a significant hotspot of wet ammonium deposition. However, the absence of long-term monitoring data in the area hinders the comprehension of the historical trends and changes in wet NH4+-N deposition in response to emissions, which interferes with the ability to make effective decisions. This study has analyzed the long-term trends of wet NH4+-N deposition flux and has quantified the effect of anthropogenic emissions and meteorological factors at a typical urban site and a typical forest site in the PRD region from 2009 to 2020. It revealed a significant decreasing trend in wet NH4+-N flux in both the typical urban and forest areas of the PRD region, at -6.2%/year (p < 0.001) and -3.3%/year (p < 0.001), respectively. Anthropogenic emissions are thought to have contributed 47%-57% of the wet NH4+-N deposition trend over the past 12 years compared to meteorological factors. Meteorological conditions dominated the inter-annual fluctuations in wet NH4+-N deposition with an absolute contribution of 46%-52%, while anthropogenic emissions change alone explained 10%-31%. NH3 emissions have the greatest impact on the urban area among anthropogenic emission factors, while SO2 emissions have the greatest impact on the forest area. Additionally, precipitation was identified as the primary meteorological driver for both sites. Our findings also imply that the benefits of NH3 emissions reductions might not immediately emerge due to interference from weather-related factors.

Identifying the spatiotemporal patterns and natural and socioeconomic influencing factors of PM2.5 and O3 pollution in China

To promote collaborative governance of PM2.5 and O3 pollution, understanding their spatiotemporal patterns and determining factors is crucial to control air pollution in China. Using the ground-monitored data encompassing PM2.5 and O3 concentrations in 2019 across 337 Chinese cities, this study explores the spatiotemporal patterns of PM2.5 and O3 concentrations, and then employed the Multi-scale Geographically Weighted Regression (MGWR) model to examine the socioeconomic and natural factors affecting PM2.5 or O3 concentrations. The results show that PM2.5 and O3 concentrations exhibit distinct monthly U-shaped and inverted U-shaped temporal fluctuation patterns across Chinese cities, respectively. Spatially, both pollutants manifest spatial clustering characteristic and a certain degree of bivariate spatial correlation. Elevated PM2.5 concentrations are predominantly concentrated on north and central China, as well as the Xinjiang Autonomous Region, whereas higher O3 concentrations are distributed widely across north, east, and northwest China. The MGWR model outperforms traditional OLS and global spatial regression models, evidenced by its enhanced goodness-of-fit metrics. Specifically, the R2 values for the PM2.5 and O3 MGWR models are notably high, at 0.842 and 0.861, respectively. Socioeconomic and natural factors are found to have multi-scale spatial effects on PM2.5 and O3 concentrations in China. On average, PM2.5 concentrations show positively correlations with population density, the proportion of the added value of secondary industry in GDP, wind speed, relative humidity, and atmospheric pressure, but negatively relationship with per capita GDP, road density, urban greening, air temperature, precipitation, and sunshine duration. In contrast, O3 concentrations are also positively associated with population density, the proportion of the added value of secondary industry in GDP, energy consumption, precipitation, wind speed, atmospheric pressure, and sunshine duration, but negatively correlated with per capita GDP, road density, and air temperature. Our findings offer valuable insights to inform the development of comprehensive air pollution management policies in in developing countries.

Co-doped cryptomelane-type manganese oxide in situ grown on a nickel foam substrate for high humidity ozone decomposition

Monolithic catalysts with excellent O3 catalytic decomposition performance were prepared by in situ loading of Co-doped KMn8O16 on the surface of nickel foam. The triple-layer structure with Co-doped KMn8O16/Ni6MnO8/Ni foam was grown spontaneously on the surface of nickel foam by tuning the molar ratio of KMnO4 to Co(NO3)2·6H2O precursors. Importantly, the formed Ni6MnO8 structure between KMn8O16 and nickel foam during in situ synthesis process effectively protected nickel foam from further etching, which significantly enhanced the reaction stability of catalyst. The optimum amount of Co doping in KMn8O16 was available when the molar ratio of Mn to Co species in the precursor solution was 2:1. And the Mn2Co1 catalyst had abundant oxygen vacancies and excellent hydrophobicity, thus creating outstanding O3 decomposition activity. The O3 conversion under dry conditions and relative humidity of 65%, 90% over a period of 5 hr was 100%, 94% and 80% with the space velocity of 28,000 hr-1, respectively. The in situ constructed Co-doped KMn8O16/Ni foam catalyst showed the advantages of low price and gradual applicability of the preparation process, which provided an opportunity for the design of monolithic catalyst for O3 catalytic decomposition.

Chemical composition, multiple sources, and health risks of PM2.5: A case study in Linyi, China's plate and logistics capital

Elucidating the chemical composition, sources, and health risks of fine particulate matter (PM2.5) is crucial for effectively preventing and controlling air pollution. This study collected PM2.5 samples in Linyi from November 10, 2021, to October 15, 2022, spanning the period of the 2022 Winter Olympics and Paralympics. The analysis focused on seasonal variations in the chemical composition of PM2.5, including water-soluble ions, inorganic elements, and carbonaceous aerosols. Results from the random forest model indicated that control measures during the Olympics and Paralympics reduced PM2.5 concentrations by 21.5% in Linyi. Organic matter was the dominant component of PM2.5, followed by NO3-, SO42-, and NH4+. Among secondary inorganic ions, SO42- exhibited the highest concentration in summer, while NO3- and NH4+ showed the lowest concentrations. The inorganic elements S, K, Fe, and Si had high mean annual concentrations, underscoring the need for targeted control measures for plate production, bulk coal burning, and biomass combustion in Linyi. The organic carbon (OC) to elemental carbon ratio (17.7-20.5) in Linyi was high, highlighting the importance of addressing secondary OC pollution. According to the positive matrix factorization model, coal burning, and the secondary formation processes of sulfate and nitrate were the dominant sources of PM2.5. Backward air mass trajectories revealed substantial contributions from the southeastern, local, and southwestern regions of Linyi. This suggests the need for enhanced regional joint prevention and control efforts between Linyi and neighboring cities, such as Rizhao and Jining in Shandong Province, as well as northern cities in Jiangsu Province. The highest non-carcinogenic and carcinogenic risks (CRs) were associated with As. coal burning posed significant noncarcinogenic risks and a moderate CR, contributing 41.7% and 44.0% of the total health risk, respectively. These findings are crucial for developing effective air pollution prevention and control strategies.

Research on the impact of land use and land cover changes on local meteorological conditions and surface ozone in the north China plain from 2001 to 2020

Land use and land cover changes (LULCC) alter local surface attributes, thereby modifying energy balance and material exchanges, ultimately impacting meteorological parameters and air quality. The North China Plain (NCP) has undergone rapid urbanization in recent decades, leading to dramatic changes in land use and land cover. This study utilizes the 2020 land use and land cover data obtained from the MODIS satellite to replace the default 2001 data in the Weather Research and Forecasting-Community Multiscale Air Quality (WRF-CMAQ) model. It simulates and analyzes the direct impact of LULCC on meteorological parameters and the indirect impact on surface ozone (O3) concentration through physical and chemical processes in the North China Plain during July in the summer. Six rapidly urbanizing cities were selected to represent the North China Plain. The results show that LULCC significantly increased sensible heat flux and 2-m temperature in rapidly urbanizing areas throughout the diurnal cycle, with more pronounced effects during the daytime, ranging from 6.49 to 23.46 W/m2 and 0.20-0.59 °C, respectively. The 10-m wind speed decreased at night and increased during the day, with changes ranging from - 0.43 to 0.27 m/s at night and - 0.16 to 0.15 m/s during the day. The planetary boundary layer height generally increased, with a larger rise during the daytime, ranging from 23.63 to 84.74 m. Simultaneously, surface O3 concentrations increased during both daytime and nighttime. The daytime increase ranged from 2.89 to 9.82 μg/m3, while the nighttime increase ranged from 1.76 to 7.77 μg/m3. LULCC enhanced meteorological and chemical processes as well as vertical transport, leading to an increase in O3. At the same time, it reduced the increase in O3 through horizontal transport and dry deposition processes. These changes are related to the meteorological variations. The impact on O3 concentrations was not limited to the surface but extended to the top of the planetary boundary layer (approximately 1500 m). Below 500 m, vertical transport increased O3 concentrations, while horizontal transport decreased O3 concentrations. Additionally, the meteorological and chemical processes induced by LULCC showed enhanced effects above the surface, whereas the dry deposition process had a smaller impact on O3 concentrations above the surface. This study reveals the significant impact of urban expansion on regional meteorological parameters and air quality. It optimizes the model's simulation of regional air quality and provides new insights into understanding the effects of urbanization on meteorological conditions and air quality.

Analyzing variation of water inflow to inland lakes under climate change: Integrating deep learning and time series data mining

The alarming depletion of global inland lakes in recent decades makes it essential to predict water inflow from rivers to lakes (WIRL) trend and unveil the dominant influencing driver, particularly in the context of climate change. The raw time series data contains multiple components (i.e., long-term trend, seasonal periodicity, and random noise), which makes it challenging for traditional machine/deep learning techniques to effectively capture long-term trend information. In this study, a novel FactorConvSTLnet (FCS) method is developed through integrating STL decomposition, convolutional neural networks (CNN), and factorial analysis into a general framework. FCS is more robust in long-term WIRL trend prediction through separating trend information as a modeling predictor, as well as unveiling predominant drivers. FCS is applied to typical inland lakes (the Aral Sea and the Lake Balkhash) in Central Asia, and results indicate that FCS (Nash-Sutcliffe efficiency = 0.88, root mean squared error = 67m³/s, mean relative error = 10%) outperforms the traditional CNN. Some main findings are: (i) during 1960-1990, reservoir water storage (WSR) was the dominant driver for the two lakes, respectively contributing to 71% and 49%; during 1991-2014 and 2015-2099, evaporation (EVAP) would be the dominant driver, with the contribution of 30% and 47%; (ii) climate change would shift the dominant driver from human activities to natural factors, where EVAP and surface snow amount (SNW) have an increasing influence on WIRL; (iii) compared to SSP1-2.6, the SNW contribution would decrease by 26% under SSP5-8.5, while the EVAP contribution would increase by 9%. The findings reveal the main drivers of shrinkage of the inland lakes and provide the scientific basis for promoting regional ecological sustainability.

Unique impacts of strong and westward-extended western Pacific subtropical high on ozone pollution over eastern China

As a subtropical anticyclonic high-pressure system that typically forms over the northwestern Pacific Ocean in summer, the Western Pacific subtropical high (WPSH) affects meteorological conditions and ozone pollution in China. The relationship between maximum daily 8-h average ozone (MDA8 O3) concentrations and the extremely strong and westward-extended WPSH occurred in 2022 is investigated using observations, reanalysis data and atmospheric chemistry model simulations. During July-August 2022, a significant positive relationship existed between the intensity of the WPSH and MDA8 O3 over southern China, with a correlation coefficient of +0.44, but the correlation is negative (-0.40) in northern China. During the strong WPSH days, MDA8 O3 increased by 16.5 μg m-3 (16.4% relative to July-August average) over southern China and decreased by 19.0 μg m-3 (14.5%) in northern China compared to the weak WPSH days. The unique dipole pattern in the relationship between ozone levels and the WPSH in 2022 exhibited a contrast to that during 2015-2021. The difference is primarily due to the extremely strong WPSH intensity and its unusual westward expansion in 2022. In this case, an anomalous anticyclone at 500 hPa dominates over southern China, which creates conditions conducive for ozone formation and accumulation. The anticyclone weakened horizontal winds and reduced the dispersion of ozone, alongside a high temperature and low relative humidity, which favored the chemical production of ozone. In contrast, abnormal northerly winds enhanced ozone diffusion in northern China and the low temperature reduced ozone chemical production. This study reveals the mechanism for the significant impact of strong and westward-extended WPSH on ozone concentrations over China, emphasizing the role of the WPSH location in modulating meteorology and ozone levels.

Transformer-based ozone multivariate prediction considering interpretable and priori knowledge: A case study of Beijing, China

Ozone pollution is the focus of current environmental governance in China and high-quality prediction of ozone concentration is the prerequisite to effective policymaking. The studied ozone pollution time series exhibits distinct seasonality and secular trends and is associated with various factors. This study developed an interpretable hybrid model by combining STL decomposition and the Transformer (STL-Transformer) with the prior information of ozone time series and global multi-source information as prediction basis. The STL decomposition decomposes ozone time series into trend, seasonal, and remainder components. Then, the three components, along with other air quality and meteorological data, are integrated into the input sequence of the Transformer. The experiment results show that the STL-Transformer outperforms the other five state-of-the-art models, including the standard Transformer. Specially, the univariate forecasting for ozone relies on mimicking the patterns and trends that have occurred in the past. In contrast, multivariate forecasting can effectively capture complex relationships and dependencies involving multiple variables. The method successfully grasps the prior and global multi-source information and simultaneously improves the interpretability of ozone prediction with high precision. This study provides new insights for air pollution forecasting and has reliable theoretical value and practical significance for environmental governance.

Vertically resolved meteorological adjustments of aerosols and trace gases in Beijing, Taiyuan, and Hefei by using RF model

Air pollution represents a complex phenomenon defined by the presence of various gases and particulate matter, leading to intricate spatio-temporal fluctuations. This study aims to enhance our understanding of how meteorological factors influence trace gases and aerosols, exacerbating air pollution in various geographical locations, specifically in Beijing's Fengtai (BJFT), Taiyuan City (SXTY), and Hefei's Science Island (HFDP). The study employs 2D-MAX-DOAS observations and utilizes the Random Forest (RF) model to decouple the influence of meteorological conditions from pollutant data. The vertical profile of nitrogen dioxide (NO2), sulfur dioxide (SO2), formaldehyde (HCHO), and aerosols at each study site was classified into four distinct layers, followed by conducting a meteorological decoupling analysis on each layer. This decoupling analysis demonstrates that meteorology significantly influences aerosols across all sites, with reductions ranging from 75 % to 95 % after de-weathering. SO2 shows minimal susceptibility, with the changes ranging from ±20 % to ±60 % after de-weathering. Among all sites, BJFT's pollutants exhibit less susceptibility overall, while pollutants at HFDP are more susceptible. The findings further reveal significant meteorological interventions in pollutants in surface layers (0.05 km and 0.2-0.4 km) at BJFT, with some exceptions at SXTY. However, pollutants, particularly NO2 and aerosols in higher layers (0.6-0.8 km and 1.0-1.2 km) at HFDP, also experience significant meteorological interferences. The findings at HFDP and SXTY reveal that removing meteorological influence also adjusts the profile shape of pollutants. For instance, the NO2 profile at HFDP during the winter season shifted from a bimodal to an exponential shape after de-weathering. Overall, this study sheds light on the complex interplay between meteorological factors and trace gases at various altitudes across different geographic locations, offering insights crucial for holistic and effective pollution mitigation strategies.

Determination of major drive of ozone formation and improvement of O3 prediction in typical North China Plain based on interpretable random forest model

O3 pollution in China has become prominent in recent years, and it has become one of the most challenging issues in air pollution control. We used data on atmospheric pollutants and meteorology from 2019 to 2021 to build an interpretable random forest (RF) model, applying this model to predict O3 concentration in 2022 in five cities in the Southwest North China Plain. The model was also used to identify and explain the influence of various factors on O3 formation. The correlation coefficient R2 between the predicted O3 concentration and observed O3 concentration was 0.82, the MAE was 15.15 μg/m3, and the RMSE was 20.29 μg/m3, indicating that the model can effectively predict O3 concentration in the studying area. The results of correlation analysis, feature importance, and the driving factor analysis from SHapley Additive exPlanations (SHAP) model indicated that temperature (T), NO2, and relative humidity (RH) are the top three features affecting O3 prediction, while the weights of wind speed and wind direction were relatively low. Thus, O3 in the southwestern North China Plain may mainly come from the formation of local photochemical activities. The dominant factors behind O3 also varied in different seasons. In spring and autumn, O3 pollution is more likely to occur under high NO2 concentration and high-temperature conditions, while in summer, it is more likely to occur under high-temperature and precipitation-free weather. In winter, NO2 is the dominant factor in O3 formation. Finally, the interpretable RF model is used to predict future O3 concentration based on features provided by Community Multiscale Air Quality (CMAQ) and Weather Research & Forecast (WRF) model, and the simulation performance of CMAQ on O3 concentration is enhanced to a certain extent, improving the prediction of future O3 pollution situations and guiding pollution control.

Elucidating transport dynamics and regional division of PM2.5 and O3 in China using an advanced network model

Air pollution exhibits significant spatial spillover effects, complicating and challenging regional governance models. This study innovatively applied and optimized a statistics-based complex network method in atmospheric environmental field. The methodology was enhanced through improvements in edge weighting and threshold calculations, leading to the development of an advanced pollutant transport network model. This model integrates pollution, meteorological, and geographical data, thereby comprehensively revealing the dynamic characteristics of PM2.5 and O3 transport among various cities in China. Research findings indicated that, throughout the year, the O3 transport network surpassed the PM2.5 network in edge count, average degree, and average weighted degree, showcasing a higher network density, broader city connections, and greater transmission strength. Particularly during the warm period, these characteristics of the O3 network were more pronounced, showcasing significant transport potential. Furthermore, the model successfully identified key influential cities in different periods; it also provided detailed descriptions of the interprovincial spillover flux and pathways of PM2.5 and O3 across various time scales. It pinpointed major pollution spillover and receiving provinces, with primary spillover pathways concentrated in crucial areas such as the Beijing-Tianjin-Hebei (BTH) region and its surrounding areas, the Yangtze River Delta, and the Fen-Wei Plain. Building on this, the model divided the O3, PM2.5, and synergistic pollution transmission regions in China into 6, 7, and 8 zones, respectively, based on network weights and the Girvan Newman (GN) algorithm. Such division offers novel perspectives and strategies for regional joint prevention and control. The validity of the model was further corroborated by source analysis results from the WRF-CAMx model in the BTH area. Overall, this research provides valuable insights for local and regional atmospheric pollution control strategies. Additionally, it offers a robust analytical tool for research in the field of atmospheric pollution.

Analyzing ozone formation sensitivity in a typical industrial city in China: Implications for effective source control in the chemical transition regime

Volatile organic compounds (VOCs) play a major role in O3 formation in urban environments. However, the complexity in the emissions of VOCs and nitrogen oxides (NOx) in industrial cities has made it challenging to identify the key factors influencing O3 formation. This study used observation-based-model (OBM) to analyze O3 sensitivities to VOCs and NOx during summer in a typical industrial city in China. The OBM model results were coupled with a receptor model to analyze the sources of O3. Higher concentrations of O3 precursors were observed during polluted periods indicating that precursor accumulation contributed to the higher maxima of the net ozone formation rate and HOx concentrations. Analyses of ROx· budgets and relative incremental reactivity (RIR) indicated that O3 production is in a chemical transition regime and was sensitive to both VOCs and NOx. Results from Positive Matrix Factorization (PMF) analysis indicated that gasoline vehicle emissions, industrial processes, and coal combustion were major sources of O3 precursors. The sensitivities of O3 production to these sources depend on if both VOC and NOx sensitivities are considered. If only VOCs sensitivity is considered, in contrast, the contribution of anthropogenic sources to O3 production was significantly underestimated. This study highlights the importance of accounting for both VOCs and NOx sensitivities when O3 chemistry is in a transition regime in O3 production attribution studies.

Relationship between ozone and air temperature in future conditions: A case study in sichuan basin, China

The Sichuan Basin (SCB) is located in southwestern China and has a unique topography where ozone (O3) pollution is frequent during summer. Few studies have clarified the relationship between O3 and air temperature in SCB. Here, the SCB was divided into four major urban agglomerations. The weather research and forecasting model-community multiscale air quality model (WRF-CMAQ) was used to analyze the meteorology, spatial distribution characteristics of pollutants, and interactions among the urban agglomerations in the SCB. WRF-CMAQ was used to study the historical changes in the climate penalty factor (CPF) from 2015 to 2020 and the climate pathways under the SSP2-4.5 CPF in values in 2030 for the ambitious pollution NDC-goal scenario (NDC) and current-goals scenario (Current). The results show that the SCB is warmer in the summer months with prevailing northeasterly winds. Ozone accumulated in the western part of the SCB, and a high CPF of O3 concentration was most prominent in NW urban agglomeration, where the O3 concentration increased by 4.12-5.40 ppb for every 1 °C increase in air temperature. The observed CPF in the SCB in 2020 averaged 3.64 ppb/°C. The average CPF in the SCB in 2030 was 1.152 ppb/°C under the NDC scenario and 1.269 ppb/°C under the current scenario. This study is critical for understanding the relationship between O3 concentration and air temperature in China.

Contrasting effects of clean air actions on surface ozone concentrations in different regions over Beijing from May to September 2013-2020

Due to the nonlinear impacts of meteorology and precursors, the response of ozone (O3) trends to emission changes is very complex over different regions in megacity Beijing. Based on long-term in-situ observations at 35 air quality sites (four categories, i.e., urban, traffic, northern suburban and southern suburban sites) and satellite data, spatiotemporal variability of O3, gaseous precursors, and O3-VOCs-NOx sensitivity were explored through multiple metrics during the warm season from 2013 to 2020. Additionally, the contribution of meteorology and emissions to O3 was separated by a machine-learning-based de-weathered method. The annual averaged MDA8 O3 and O3 increased by 3.7 and 2.9 μg/m3/yr, respectively, with the highest at traffic sites and the lowest in northern suburb, and the rate of Ox (O3 + NO2) was 0.2 μg/m3/yr with the highest in southern suburb, although NO2 declined strongly and HCHO decreased slightly. However, the increment of O3 and Ox in the daytime exhibited decreasing trends to some extent. Additionally, NOx abatements weakened O3 loss through less NO titration, which drove narrowing differences in urban-suburban O3 and Ox. Due to larger decrease of NO2 in urban region and HCHO in northern suburb, the extent of VOCs-limited regime fluctuated over Beijing and northern suburb gradually shifted to transition or NOx-limited regime. Compared with the directly observed trends, the increasing rate of de-weathered O3 was lower, which was attributed to favorable meteorological conditions for O3 generation after 2017, especially in June (the most polluted month); whereas the de-weathered Ox declined except in southern suburb. Overall, clean air actions were effective in reducing the atmospheric oxidation capacity in urban and northern suburban regions, weakening local photochemical production over Beijing and suppressing O3 deterioration in northern suburb. Strengthening VOCs control and keeping NOx abatement, especially in June, will be vital to reverse O3 increase trend in Beijing.

Changes in source apportioned VOCs during high O3 periods using initial VOC-concentration-dispersion normalized PMF

Ambient volatile organic compounds (VOCs) concentrations are affected by emissions, dispersion, and chemistry. This work developed an initial concentration-dispersion normalized PMF (ICDN-PMF) to reflect the changes in source emissions. The effects of photochemical losses for VOC species were corrected by estimating the initial data, and then applying dispersion normalization to reduce the impacts of atmospheric dispersion. Hourly speciated VOC data measured in Qingdao from March to May 2020 were utilized to test the method and had assessed its effectiveness. Underestimated solvent use and biogenic emissions contributions due to photochemical losses during the O3 pollution (OP) period reached 4.4 and 3.8 times the non-O3 pollution (NOP) period values, respectively. Increased solvent use contribution due to air dispersion during the OP period was 4.6 times the change in the NOP period. The influence of chemical conversion and air dispersion on the gasoline and diesel vehicle emissions was not apparent during either period. The ICDN-PMF results suggested that biogenic emissions (23.1 %), solvent use (23.0 %), motor-vehicle emissions (17.1 %), and natural gas and diesel evaporation (15.8 %) contributed most to ambient VOCs during the OP period. Biogenic emissions and solvent use contributions during the OP period increased by 187 % and 135 % compared with the NOP period, respectively, whereas that of liquefied petroleum gas substantially decreased during the OP period. Controlling solvent use and motor-vehicles could be effective in controlling VOCs in the OP period.

Significant land cover change in China during 2001-2019: Implications for direct and indirect effects on surface ozone concentration

China has become one of the most prominent areas of global land cover change during the past few decades. These changes can directly influence meteorological parameters thus further regulating tropospheric ozone (O3) formation. Moreover, changes in biogenic emissions due to land cover variation can also have an indirect effect on O3 concentration. This study applied the Community Multiscale Air Quality model to comprehensively evaluate the impacts of significant land cover change on O3 levels in China during summertime between 2001 and 2019. The results showed that the daily maximum 8-h average O3 concentration (MDA8 O3) increased by 3.6-8.9 μg/m3, 2.8-8.0 μg/m3, 3.8-9.6 μg/m3, -1.5-6.2 μg/m3, and -0.6-2.5 μg/m3 in Beijing-Tianjin-Hebei region, Yangtze River Delta, Pearl River Delta, Sichuan Basin, and Fenwei Plain, respectively, in response to land cover variation. The research identified that the direct effect was the primary factor in raising O3 levels which mainly altered O3 concentration by changing vertical import and dry deposition velocity. Moreover, land cover variation tended to decrease biogenic nitric oxide emission and increase biogenic volatile organic compounds emission on the whole, and cause an obvious increase of MDA8 O3 by 1.8-4.9 μg/m3 in Pearl River Delta due to the indirect effect. This study offered valuable insights into the impacts of land cover change on O3 levels, highlighting the need for policymakers to consider land cover variation on air pollutants concentration for devising comprehensive multi-pollutant control strategies.

Statistical significance of PM2.5 and O3 trends in China under long-term memory effects

Over the past decade, the Chinese government has implemented the "Clean Air Action" measures to enhance the atmospheric environmental quality, primarily focusing on curbing PM2.5 and O3 concentrations. The efficacy of these strategies and the underlying causes (human factors or natural variability) of any observed increases or decreases in PM2.5 and O3 concentrations are of great importance. Examining the hourly PM2.5 and O3 concentration time series from six representative regions in China between 2015 and 2021 revealed an overall downward trend in PM2.5 concentrations. However, the O3 concentration time series indicated upward trends in some regions, except for the Northeast area (NE) and Sichuan Basin (SCB). In the context of conventional significance tests, the assumption is typically that the time series' samples are independent and therefore memoryless. However, in situations where the time series exhibits strong autocorrelation and limited sample size, this assumption can lead to an overestimation of the statistical significance of the linear trend. To account for this, we utilized a long-term memory model that can reproduce the long-term persistence of pollutant records to improve the accuracy of significance tests. By comparing the P-values of real and surrogate data generated by the long-term memory model, we found that only PM2.5 concentrations in the Pearl River Delta (PRD) were slightly insignificant. For the remaining five regions, the P-values of PM2.5 concentrations were smaller than the significant level of 0.05, suggesting that the observed downward trends in PM2.5 concentrations are not due to natural variability, thereby confirming the effectiveness of the government's policies aimed at curbing atmospheric particulate matter in recent years. Our results show that O3 pollution is significantly increasing only in the Beijing-Tianjin-Hebei (BTH) region, beyond natural variability. In contrast, the trends of O3 pollution in many regions of China are markedly impacted by natural and climate variability.

Long-term spatiotemporal evolution and coordinated control of air pollutants in a typical mega-mountain city of Cheng-Yu region under the "dual carbon" goal

Clarifying the spatiotemporal distribution and impact mechanism of pollution is the prerequisite for megacities to formulate relevant air pollution prevention and control measures and achieve carbon neutrality goals. Chongqing is one of the dual-core key megacities in Cheng-Yu region and as a typical mountain-city in China, environmental problems are complex and sensitive. This research aims to investigate the exceeding standard levels and spatio-temporal evolution of criteria pollutants between 2014 and 2020. The results indicated that PM10, PM2.5, CO and SO2 were decreased significantly by 45.91%, 52.86%, 38.89% and 66.67%, respectively. Conversely, the concentration of pollutant O3 present a fluctuating growth and found a "seesaw" phenomenon between it and PM. Furthermore, PM and O3 are highest in winter and summer, respectively. SO2, NO2, CO, and PM showed a "U-shaped", and O3 showed an inverted "U-shaped" seasonal variation. PM and O3 concentrations are still far behind the WHO, 2021AQGs standards. Significant spatial heterogeneity was observed in air pollution distribution. These results are of great significance for Chongqing to achieve "double control and double reduction" of PM2.5 and O3 pollution, and formulate a regional carbon peaking roadmap under climate coordination. Besides, it can provide an important platform for exploring air pollution in typical terrain around the world and provide references for related epidemiological research.Implications: Chongqing is one of the dual-core key megacities in Cheng-Yu region and as a typical mountain city, environmental problems are complex and sensitive. Under the background of the "14th Five-Year Plan", the construction of the "Cheng-Yu Dual-City Economic Circle" and the "Dual-Carbon" goal, this article comprehensively discussed the annual and seasonal excess levels and spatiotemporal evolution of pollutants under the multiple policy and the newest international standards (WHO,2021AQG) backgrounds from 2014 to 2020 in Chongqing. Furthermore, suggestions and measures related to the collaborative management of pollutants were discussed. Finally, limitations and recommendations were also put forward.Clarifying the spatiotemporal distribution and impact mechanism of pollution is the prerequisite for cities to formulate relevant air pollution control measures and achieve carbon neutrality goals. This study is of great significance for Chongqing to achieve "double control and double reduction" of PM2.5 and O3 pollution, study and formulate a regional carbon peaking roadmap under climate coordination and an action plan for sustained improvement of air quality.In addition, this research can advanced our understanding of air pollution in complex terrain. Furthermore, it also promote the construction of the China national strategic Cheng-Yu economic circle and build a beautiful west. Moreover, it provides scientific insights for local policymakers to guide smart urban planning, industrial layout, energy structure, and transportation planning to improve air quality throughout the Cheng-Yu region. Finally, this is also conducive to future scientific research in other regions of China, and even megacities with complex terrain in the world.

Prediction of soil moisture using BiGRU-LSTM model with STL decomposition in Qinghai-Tibet Plateau

Ali Network data based on the Qinghai-Tibetan Plateau (QTP) can provide representative coverage of the climate and surface hydrometeorological conditions in the cold and arid region of the QTP. Among them, the plateau soil moisture can effectively quantify the uncertainty of coarse resolution satellite and soil moisture models. With the objective of constructing an "end-to-end" soil moisture prediction model for the Tibetan Plateau, a combined prediction model based on time series decomposition and a deep neural network is proposed in this article. The model first performs data preprocessing and seasonal-trend decomposition using loess (STL) to obtain the trend component, seasonal component and random residual component of the original time series in an additive way. Subsequently, the bidirectional gated recurrent unit (BiGRU) is used for the trend component, and the long short-term memory (LSTM) is used for the seasonal and residual components to extract the time series information. The experiments based on the measured data demonstrate that the use of STL decomposition and the combination model can effectively extract the information in soil moisture series using its concise and clear structure. The proposed model in this article has a stable performance improvement of 5-30% over a single model and existing prediction models in different prediction time domains. In long-range prediction, the proposed model also achieves the best accuracy in the shape and temporal domains described by using dynamic time warping (DTW) index and temporal distortion index (TDI). In addition, the generalization performance experiments show that the combined method proposed in this article has strong reference value for time series prediction of natural complex systems.

Trends of source apportioned PM2.5 in Tianjin over 2013-2019: Impacts of Clean Air Actions

A long-term (2013-2019) PM_2.5 speciation dataset measured in Tianjin, the largest industrial city in northern China, was analyzed with dispersion normalized positive matrix factorization (DN-PMF). The trends of source apportioned PM_2.5 were used to assess the effectiveness of source-specific control policies and measures in support of the two China's Clean Air Actions implemented nationwide in 2013-2017 and 2018-2020, respectively. Eight sources were resolved from the DN-PMF analysis: coal combustion (CC), biomass burning (BB), vehicular emissions, dust, steelmaking and galvanizing emissions, a mixed sulfate-rich factor and secondary nitrate. After adjustment for meteorological fluctuations, a substantial improvement in PM_2.5 air quality was observed in Tianjin with decreases in PM_2.5 at an annual rate of 6.6%/y. PM_2.5 from CC decreased by 4.1%/y. The reductions in SO₂ concentration, PM_2.5 contributed by CC, and sulfate demonstrated the improved control of CC-related emissions and fuel quality. Policies aimed at eliminating winter-heating pollution have had substantial success as shown by reduced heating-related SO₂, CC, and sulfate from 2013 to 2019. The two industrial source types showed sharp drops after the 2013 mandated controls went into effect to phaseout outdated iron/steel production and enforce tighter emission standards for these industries. BB reduced significantly by 2016 and remained low due to the no open field burning policy. Vehicular emissions and road/soil dust declined over the Action's first phase followed by positive upward trends, showing that further emission controls are needed. Nitrate concentrations remained constant although NO_X emissions dropped significantly. The lack of a decrease in nitrate may result from increased ammonia emissions from enhanced vehicular NO_X controls. The port and shipping emissions were evident implying their impacts on coastal air quality. These results affirm the effectiveness of the Clean Air Actions in reducing primary anthropogenic emissions. However, further emission reductions are needed to meet global health-based air quality standards.

Spatial characteristics of change trends of air pollutants in Chinese urban areas during 2016-2020: The impact of air pollution controls and the COVID-19 pandemic

Air pollution is a threat to public health in China, and several actions and plans have been implemented by Chinese authorities in recent years to mitigate it. This study examined the spatial distribution of changes in urban air pollutants (UAP) in 336 Chinese cities from 2016 to 2020 and their responses to air pollution controls and the COVID-19 pandemic. Based on the harmonic model, decreases in fine particles (PM_2.5), inhalable particles (PM₁₀), nitrogen dioxide (NO₂), sulfur dioxide (SO₂), and carbon monoxide (CO) levels were found in 90.7%, 91.9%, 75.2%, 94.3%, and 88.7% of cities, respectively, while an increase in ozone (O₃) was found in 87.2% of cities. Notable spatial heterogeneity was observed in the air pollution trends. The greatest improvement in air quality occurred mainly in areas with poor air quality, such as Hebei province and its surrounding cities. However, some areas (i.e., Yunnan and Hainan provinces) with good air quality showed a worsening trend. During the 13th Five-Year Plan period (2016-2020), the remarkable effects of PM_2.5 and SO₂ pollution control plans were confirmed. Additionally, economic growth in 74.2% of the Chinese provinces decoupled from air quality after implementing pollution control measures. In 2020, several Chinese cities were locked down to reduce the spread of COVID-19. Except for SO₂, the national air pollution in 2020 improved to a greater extent than that in 2016-2019; In particularly, the contribution of simulated COVID-19 pandemic to NO₂ reduction was 66.7%. Overall, air pollution control actions improved urban PM_2.5, PM₁₀, SO₂, and CO, whereas NO₂ was reduced primarily because of the COVID-19 pandemic.

Change in Air Quality during 2014-2021 in Jinan City in China and Its Influencing Factors

Air pollution affects climate change, food production, traffic safety, and human health. In this paper, we analyze the changes in air quality index (AQI) and concentrations of six air pollutants in Jinan during 2014-2021. The results indicate that the annual average concentrations of PM10, PM2.5, NO2, SO2, CO, and O3 and AQI values all declined year after year during 2014-2021. Compared with 2014, AQI in Jinan City fell by 27.3% in 2021. Air quality in the four seasons of 2021 was obviously better than that in 2014. PM2.5 concentration was the highest in winter and PM2.5 concentration was the lowest in summer, while it was the opposite for O3 concentration. AQI in Jinan during the COVID epoch in 2020 was remarkably lower compared with that during the same epoch in 2021. Nevertheless, air quality during the post-COVID epoch in 2020 conspicuously deteriorated compared with that in 2021. Socioeconomic elements were the main reasons for the changes in air quality. AQI in Jinan was majorly influenced by energy consumption per 10,000-yuan GDP (ECPGDP), SO2 emissions (SDE), NOx emissions (NOE), particulate emissions (PE), PM2.5, and PM10. Clean policies in Jinan City played a key role in improving air quality. Unfavorable meteorological conditions led to heavy pollution weather in the winter. These results could provide a scientific reference for the control of air pollution in Jinan City.

First long-term surface ozone variations at an agricultural site in the North China Plain: Evolution under changing meteorology and emissions

Significant upward trends in surface ozone (O₃) have been widely reported in China during recent years, especially during warm seasons in the North China Plain (NCP), exerting adverse environmental effects on human health and agriculture. Quantifying long-term O₃ variations and their attributions helps to understand the causes of regional O₃ pollution and to formulate according control strategy. In this study, we present long-term trends of O₃ in the warm seasons (April-September) during 2006-2019 at an agricultural site in the NCP and investigate the relative contributions of meteorological and anthropogenic factors. Overall, the maximum daily 8-h average (MDA8) O₃ exhibited a weak decreasing trend with large interannual variability. < 6 % of the observed trend could be explained by changes in meteorological conditions, while the remaining 94 % was attributed to anthropogenic impacts. However, the interannual variability of warm season MDA8 O₃ was driven by both meteorology (36 ± 28 %) and anthropogenic factors (64 ± 27 %). Daily maximum temperature was the most essential factor affecting O₃ variations, followed by ultraviolet radiation b (UVB) and boundary layer height (BLH), with rising temperature trends inducing O₃ inclines throughout April to August, while UVB mainly influenced O₃ during summer months. Under changes in emissions and air quality, warm season O₃ production regime gradually shifted from dominantly VOCs-limited during 2006-2015 to NO_x-limited afterwards. Relatively steady HCHO and remarkably rising NO_x levels resulted in the fast decreasing MDA8 O₃ (-2.87 ppb yr^-1) during 2006-2012. Rapidly decreasing NO_x, flat or slightly increasing HCHO promoted O₃ increases during 2012-2015 (9.76 ppb yr^-1). While afterwards, slow increases in HCHO and downwards fluctuating NO_x led to decreases in MDA8 O₃ (-4.97 ppb yr^-1). Additionally, continuous warming trends might promote natural emissions of O₃ precursors and magnify their impacts on agricultural O₃ by inducing high variability, which would require even more anthropogenic reduction to compensate for.

Effect of photochemical losses of ambient volatile organic compounds on their source apportionment

Photochemical losses of ambient volatile organic compounds (VOCs) substantially affect source apportionment analysis. Hourly speciated VOC data measured from April to August 2020 in Tianjin, China were used to analyze the photochemical losses of VOC species and assess the impacts of photochemical losses on source apportionment by comparing the positive matrix factorization (PMF) results based on observed and initial concentration data (OC-PMF and IC-PMF). The initial concentrations of the VOC species were estimated using a photochemical age-based parameterization method. The results suggest that the average photochemical loss of total VOCs (TVOCs) during the ozone pollution period was 2.4 times higher than that during the non-ozone pollution period. The photochemical loss of alkenes was more significant than that of the other VOC species. Temperature has an important effect on photochemical losses, and different VOC species have different sensitivities to temperature; high photochemical losses mainly occurred at temperatures between 25 °C and 35 °C. Photochemical losses reduced the concentrations of highly reactive species in the OC-PMF factor profile. Compared with the IC-PMF results, the OC-PMF contributions of biogenic emissions and polymer production-related industrial sources were underestimated by 73 % and 50 %, respectively, likely due to the oxidation of isoprene and propene, respectively. The contribution of diesel and gasoline evaporation was underestimated by 39 %, which was likely due to the loss of m,p-xylene. Additionally, the contributions of liquefied petroleum gas, vehicle emissions, natural gas, and oil refinery were underestimated by 31 %, 29 %, 23 %, and 13 %, respectively. When the O₃ concentrations were higher than 140 μg m^-3 or the temperatures were higher than 30 °C, the photochemical losses from most sources increased substantially. Additionally, solar radiation produced different photochemical losses for different source types.