Spatial-temporal Variations Research Articles

Ocean weather, biological rates, and unexplained global ecological patterns.

As on land, oceans exhibit high temporal and spatial temperature variation. This "ocean weather" contributes to the physiological and ecological processes that ultimately determine the patterns of species distribution and abundance, yet is often unrecognized, especially in tropical oceans. Here, we tested the paradigm of temperature stability in shallow waters (<12.5 m) across different zones of latitude. We collated hundreds of in situ, high temporal-frequency ocean temperature time series globally to produce an intuitive measure of temperature variability, ranging in scale from quarter-diurnal to annual time spans. To estimate organismal sensitivity of ectotherms (i.e. microbes, algae, and animals whose body temperatures depend upon ocean temperature), we computed the corresponding range of biological rates (such as metabolic rate or photosynthesis) for each time span, assuming an exponential relationship. We found that subtropical regions had the broadest temperature ranges at time spans equal to or shorter than a month, while temperate and tropical systems both exhibited narrow (i.e. stable) short-term temperature range estimates. However, temperature-dependent biological rates in tropical regions displayed greater ranges than in temperate systems. Hence, our results suggest that tropical ectotherms may be relatively more sensitive to short-term thermal variability. We also highlight previously unexplained macroecological patterns that may be underpinned by short-term temperature variability.

Vegetation’s Dynamic Changes, Spatial Trends, and Responses to Drought in the Yellow River Basin, China

Drought is a complex and recurrent natural disaster that can have devastating impacts on economies, societies, and ecosystems around the world. In light of climate change, the frequency, duration, and severity of drought events worldwide have increased, and extreme drought events have caused more severe and irreversible damage to terrestrial ecosystems. Therefore, estimating the resilience of different vegetation to drought events and vegetation’s response to damage is crucial to ensuring ecological security and guiding ecological restoration. Based on meteorological and remote-sensing datasets from 1982 to 2022, the spatial distribution characteristics and temporal variability of vegetation were identified in the Yellow River Basin (YRB), the dynamic changes and recurrence periods of typical drought events were clarified, and the driving effects of different drought types on vegetation were revealed. The results indicated that (1) during the research period, the standardized vegetation water-deficit index (SVWI) showed a downward trend in the YRB, with a 99.52% probability of abrupt seasonal changes in the SVWI occurring in January 2003; (2) the characteristic values of the grid trend Zs were −1.46 and 0.20 in winter and summer, respectively, indicating a significant downward trend in the winter SVWI; (3) the drought with the highest severity (6.48) occurred from September 1998 to February 1999, with a recurrence period of 8.54 years; and (4) the growth of vegetation was closely related to drought, and as the duration of drought increased, the sensitivity of vegetation to drought events gradually weakened. The research results provide a new perspective for identifying vegetation’s dynamic changes and responses to drought, which is of great significance in revealing the adaptability and potential influencing factors of vegetation in relation to climate.

Seasonal Temperature Distributions and Variations in Salt Marshes: Field Investigation and Numerical Simulation

AbstractSoil temperature regulates biogeochemical processes and is a key environmental factor affecting salt marsh ecosystems. Previous studies on soil temperature and heat transport in intertidal marshes predominantly focused on short‐term changes, leaving seasonal variations unclear. This study conducted a yearlong field and modeling investigation to examine temporal and spatial temperature variations in a creek‐marsh section under estuarine and meteorological influences. The results showed that inundating tidal water propagated the seasonal water temperature signal to marsh soils, especially at low elevations, thereby modulating air temperature‐induced soil temperature variations and distributions. The response of soil temperature to air and tidal water temperatures exhibited a damped and delayed pattern. In contrast, tide‐induced porewater circulations near the creek facilitated the temperature responses. A regression model incorporating a Gamma distribution function was developed to quantify the delayed and cumulative thermal effects of tidal water and air on shallow soil temperatures. The model coefficients varied along the creek‐marsh section, capturing the seasonal regulation of periodic tidal inundation on soil temperature in the low‐elevation marsh and near the creek. Sensitivity analyses indicated that relative sea level rise lowered yearly‐averaged temperatures for the marsh platform by enhancing latent heat export. Surface water warming increased the marsh temperatures at lower elevations. This study demonstrates that the creek‐marsh topography, warming, and relative sea level rise jointly affect soil temperature dynamics, advancing our understanding of temperature‐dependent biogeochemical processes in marsh ecosystems.

Resolution of Hydroacoustic Noise Signals under Conditions of Spatial–Temporal Variability of Ocean Environment

Resolution of Hydroacoustic Noise Signals under Conditions of Spatial–Temporal Variability of Ocean Environment

Modeling the evaporation and drying of two-component slurry droplets

In this paper, we present a mathematical model and numerical simulation for the evaporation and drying of a liquid droplet containing suspended solids, relevant to processes such as spray drying and spray pyrolysis in the food and pharmaceutical industries. The model comprises three stages: first is the evaporation of the liquid droplet consisting of solid particles, followed by the second stage starting with the formation of a porous crust around a wet-core region and, finally, the third stage with sensible heating of the dry particle. Using a finite difference method with a moving grid, we account for the moving interface between the crust and wet core. Our model incorporates spatial temperature variations and is validated against experimental data on colloidal silica droplet drying, showing good agreement. We examine model assumptions and analyze the impact of drying conditions on drying rate and final particle morphology. Along with the temperature and velocity of the drying gas, we also find that the shape of suspended solid particles inside the droplet and assuming continuum flow of vapor through the crust influence drying quality. Finally, we develop a regime map to predict whether the final particle will be solid or hollow based on operating conditions.

Investigation on phytoplankton dynamics and its environmental correlates in the Xiangjiaba channel-type reservoir

Mega reservoirs, such as the Three Gorges Reservoir on the Yangtze River, have garnered significant attention due to their environmental impacts. However, the ecological ramifications of upstream cascade reservoirs remain understudied, despite their potential influence on the Yangtze River ecosystem. The Xiangjiaba Reservoir (XJB), presents a unique case for understanding the ecological impacts of large channel-type reservoirs marked with considerable water depth and extensive runoff, which lies between the characteristics of rivers and lakes. This study delves into the phytoplankton community dynamics and associated environmental factors within the XJB. Through field surveys and laboratory analysis, we explore the drivers of phytoplankton distribution and their temporal shifts. Our findings revel significant changes in phytoplankton dominance among various phyla. Bacillariophyta and Chlorophyta dominated throughout the year, while Cryptophyta prevailed in spring and Xanthophyta peaked in autumn, indicating a unique ecological feature of XJB. The reservoir’s water quality was found to be moderate. The average chlorophyll-a exhibited significant spatial–temporal variations, peaking at 26 μg/L at the mainstream-tributary confluence. Our investigation reveals a tenfold increase in algal abundance since the reservoir’s inception, accompanied by a significant transition from a Bacillariophyta-dominated community to a diverse, multi-phylum system. This shift has been significantly influenced by hydrodynamic conditions, with water stratification favoring flagellated algae like Chlorophyta and Cryptophyta. Notably, differences in phytoplankton composition between XJB and the Three Gorges Reservoir were linked to the latter’s pronounced vertical mixing. The slower ecological responses of the phytoplankton community to hydrodynamic changes underscore the importance of long-term, continuous monitoring to maintain the reservoir’s ecological balance. Our research offers insights into the ecological impacts of reservoir construction, highlighting the role of hydrodynamics in reservoir ecosystems and aiding in understanding reservoir functioning, water quality management, and biodiversity conservation.

TEMDI: A Temporal Enhanced Multisource Data Integration model for accurate PM2.5 concentration forecasting

Accurate forecasting of PM2.5 concentration is crucial for implementing effective protective measures and mitigating the adverse health impacts of air pollution. To address the complex spatial propagation dynamics and temporal variations of PM2.5, we developed the Temporal Enhanced Multisource Data Integration (TEMDI) model. This innovative approach combines spatial modeling by a Graph Neural Network (GNN) to capture the intricate spatial propagation patterns based on multi-source data fusion, and a novel Time Series Enhancement (TSE) module that includes Ensemble Empirical Mode Decomposition (EEMD), Gated Recurrent Units (GRUs), and a self-attention mechanism to adequately manage the time series’ short-term and long-term trends. Our results demonstrate TEMDI’s superior performance, achieving exceptionally high Probability of Detection (POD) rates of 96.15%, 80.28%, and 71.86% for forecast horizons of 3, 36, and 72 h, respectively. Furthermore, our feature importance analysis reveals that multi-scale features extracted by the EEMD component become increasingly crucial as the prediction horizon extends. The TEMDI model’s ability to provide accurate, reliable PM2.5 forecasts and its enhanced interpretability position it as a valuable tool for guiding environmental policy and management decisions to safeguard public health.

Dynamic Spatiotemporal Evolution and Driving Mechanisms of Vegetation in the Lower Reaches of the Tarim River, China

Analyzing the changes in vegetation under different factors is crucial for ecological protection in arid areas. The spatial-temporal variations of vegetation in the lower reaches of the Tarim River (LRTR) from 2000 to 2020, were analyzed using the Theil-Sen estimator and the Mann-Kendall test. The future trends of NDVI are projected to use the Hurst exponent method. The driving mechanisms of vegetation changes were analyzed using the GeoDetector method and multivariate residual analysis. The NDVI values in the LRTR significantly increased during the study period, indicating good vegetation recovery. The overall vegetation level remains poor and was primarily concentrated around the riverine areas. There is still a risk of vegetation degradation in most areas of the future LRTR. Compared to climate change, vegetation was more affected by human activities. Human activities have helped restore the riparian vegetation and prevented the degradation of vegetation far from the river. Therefore, distance from river channels is the strongest explanatory factor (q = 0.078) for vegetation changes, followed by precipitation, and temperature, while changes in slope have minimal impact on vegetation. Statistics have found that when two factors are combined, their impact on vegetation change is stronger. These findings are beneficial for identifying vegetation evolution patterns in LRTR and providing theoretical support for the government to carry out ecological restoration.

Structure-aware sign language recognition with spatial–temporal scene graph

Continuous sign language recognition (CSLR) is essential for the social participation of deaf individuals. The structural information of sign language motion units plays a crucial role in semantic representation. However, most existing CSLR methods treat motion units as a whole appearance in the video sequence, neglecting the exploitation and explanation of structural information in the models. This paper proposes a Structure-Aware Graph Convolutional Neural Network (SA-GNN) model for CSLR. This model constructs a spatial–temporal scene graph, explicitly capturing motion units’ spatial structure and temporal variation. Furthermore, to effectively train the SA-GNN, we propose an adaptive bootstrap strategy that enhances weak supervision using dense pseudo labels. This strategy incorporates a confidence cross-entropy loss to adjust the distribution of pseudo labels adaptively. Extensive experiments validate the effectiveness of our proposed method, achieving competitive results on popular CSLR datasets.

Aircraft-derived CH4 emissions from surface and in-situ mining activities in the Alberta oil sands region

The identification and reduction of methane sources is considered an important part of the fight to stem greenhouse gas (GHG) emissions to the atmosphere. One of the largest industrial contributors to national GHG emissions in Canada is the Alberta Oil Sands Region. To quantify and investigate the spatial distribution and temporal variability of methane emissions from this region, airborne measurements were conducted in 2017 and 2018 with three aircraft. 59 flights were conducted in total to assess emissions for both open-pit and in-situ facilities, in both cold and warm seasons. Derived emission rates were higher than those reported in national inventories by 30%–96% depending on the facility. In-situ facilities had emission rates an order of magnitude lower than surface mining operations and differed significantly from inventory estimates. No statistical differences in CH4 emissions between cold and warm seasons were observed, substantiating the use of simple upscaling to annual emissions within inventories. Rather than confirming a reported decrease in emissions between 2013 and 2018, the measurements suggest essentially no change from the 18 t h−1 for the region observed in 2013. Overall, the results suggest that current methods of CH4 emission determination within the oil sands region, for use in reporting, require improvement.

Long-term investigation of spatial–temporal variations, risk assessments, and controlling factors of persistent organic pollutants (HCHs, DDTs, and PAHs) and heavy metals in coastal regions along the Chinese Bohai Sea

Long-term investigation of spatial–temporal variations, risk assessments, and controlling factors of persistent organic pollutants (HCHs, DDTs, and PAHs) and heavy metals in coastal regions along the Chinese Bohai Sea

Multi-Year Continuous Observations of Ambient PM2.5 at Six Sites in Akure, Southwestern Nigeria

The spatial–temporal variations of fine particulate matter (PM2.5) in Akure, a city in southwestern Nigeria, are examined based on multi-year continuous observations using low-cost PM2.5 sensors at six different sites. The average annual concentration of PM2.5 across these sites was measured at 41.0 µg/m3, which surpassed both the Nigerian national air quality standard and the World Health Organization air quality guideline level. PM2.5 levels were significantly higher during the dry season (November–March), often exceeding hazardous levels (over 350 µg/m3), than during the wet season. The analyses of trends in air mass trajectories and satellite data on fire occurrences imply that the transport of dust and accumulation of PM2.5 originating from local/regional open burning activities played crucial roles in increased PM2.5 concentrations during the dry season. Further, site-to-site variations in the PM2.5 levels were observed, with relatively high concentrations at less urbanized sites, likely due to high local emissions from solid fuel combustion, waste burning, and unpaved road dust. Diurnal patterns showed morning and evening peaks at less urbanized sites, accounting for an estimated 51–77% of local emissions. These results highlight the importance of local emission sources in driving spatial–temporal PM2.5 variations within the city and the need for targeted mitigation strategies to address the significant air pollution challenges in Akure and similar regional cities in West Africa.

Implications of ocean warming and acidification on heavy metals in surface seawater of the Bohai Sea

At present, a clear dependency of the dynamics upon temperature and pH has not been established for many heavy metals (HMs), so making it difficult to project and quantify the impact of ocean warming and acidification on metal biogeochemistry in future scenarios. To understand the responses of HMs to future ocean warming and acidification, we estimated the spatial-temporal variations and pollution status of six dissolved HMs (i.e., Cu, Zn, Pb, Cd, Hg, and As) in surface seawater throughout the Bohai Sea during 2012–2014. The results showed that the average concentrations of Cu, Zn, Pb, Cd, Hg, and As in seawater of the Bohai Sea were between 2.01–3.18, 10.47–15.58, 0.85–2.31, 0.25–0.55, 0.05–0.13, and 1.24–1.98 μg L-1, respectively. Spatially, the average concentrations of the studied HMs generally decreased from the three bays towards the central area, except for Hg which was relatively high in the central Bohai Sea in some cases. This implied that, in addition to continental inputs, there may be other processes affecting the distribution pattern of Hg, such as cyclonic or anticyclonic gyres, benthic fluxes between surface and bottom layers, and some marine planktonic and microbial activities. The pollution assessments of six HMs in seawater revealed that the major risk pollutants were Pb and Hg across the Bohai Sea. Analyses of the local and interactive effects of temperature and pH on HMs showed that the interactive effect of changing temperature and pH on HMs is much more complex than a direct temperature/pH relationship with HMs. Altogether, the results suggested that future ocean warming and acidification will significantly influence the concentrations of dissolved HMs in seawater of the Bohai Sea, but with different relationships.

Spatial–Temporal Variations in Soil Organic Carbon and Driving Factors in Guangdong, China (2009–2023)

Spatial–temporal variation in soil organic carbon is an important factor for national targets to mitigate climate change and land degradation impacts. In this research, we took Guangdong Province of China as the study area, evaluated the spatial–temporal distributions of soil organic carbon using data from three China Geochemical Baseline projects (conducted in 2009, 2016, and 2023, respectively), and quantified the main driving factors of spatial–temporal variations in soil organic carbon using the random forest algorithm, further predicting the density and inventories of soil organic carbon. The results demonstrate that the mean value of SOC in Guangdong in 2009 was 0.81%; in 2016 it was 1.13%; and in 2023 it was 1.02%. The inventories of soil organic carbon (0–30 cm) in Guangdong Province were 0.61 Pg in 2009, 0.74 Pg in 2016, and 0.62 Pg in 2023. Soil in Guangdong acted as a carbon sink from 2009 to 2023 as a whole, and the most important driving force behind spatial–temporal variations in soil organic carbon was temperature, followed by precipitation and vegetation cover.

Spatial-temporal variations in evapotranspiration across the continental United States: An atmospheric water balance perspective

Accurate estimation of terrestrial evapotranspiration (ET) is crucial for understanding land–atmosphere interactions, but is challenging on the regional scales. The water balance approach, including terrestrial and atmospheric water balance (TWB and AWB), provides a simple tool for estimating regional ET. This study estimated ET values based on the AWB approach (ETAWB) over the continental United States (CONUS) for the period 1979–2021. Validations using TWB-based ET estimates (ETTWB) suggest that ETAWB is accurate. ETAWB demonstrates comparable interannual variability with the other three long-term ET products over the CONUS and is consistent with ETTWB in the majority of basins. The 43-year CONUS averaged ETAWB is 548 ± 26 mm/year, with higher values in the eastern region and the coastal regions in the western CONUS, and lower values in the western arid regions. During 1979–2021, increasing trends of ETAWB are observed in the eastern CONUS, while decreasing trends occurred in the western regions. The intercomparison between ETAWB, GLEAM, Noah, and ERA5 illustrates similar spatial patterns and linear trends. In the water-limited arid basins, ETAWB anomalies over time show strong agreements with precipitation anomalies. The results of this study indicate that the AWB approach provides reasonable regional-to-continental terrestrial ET estimates over the CONUS, serving as a reference for hydrological and climatic modeling.

Effect of knock on piston thermal load of a high compression ratio natural gas engine based on stepwise decoupling calculation

High compression ratio natural gas engine (NGE) is prone to knock at high load, which aggravates the thermal load of piston. Therefore, accurately evaluating the piston thermal load in the practical design is essential to enhance engine reliability and improve performance. The typical piston thermal load simulation adopts uniform wall boundary conditions, making it difficult to reveal the influence of turbulent flame and knock on the piston thermal load. In this paper, a stepwise decoupling method is applied to calculate the piston thermal load. The advancement of this method lies in its integration of chemical reaction mechanism with computational fluid dynamics (CFD) to calculate the spatial variations in heat transfer coefficient (HTC) and near-wall gas temperature at the piston top. Moreover, the conjugate heat transfer (CHT) method is adopted to solve the piston thermal load by coupling the thermal boundary conditions (TBC) obtained in the first step with the cooling oil boundary conditions. Based on this method, the effect of knock on piston thermal load of a high compression ratio NGE under different knock intensities is explored in this paper. It is found that turbulent combustion causes uneven distribution of thermal load at piston top. The decrease in knock intensity significantly reduces the gas temperature, heat flux and thermal load at piston top. Particularly under severe knock, the average temperature of piston is 22.9 K and 32.4 K higher than that of light knock and normal combustion, respectively. Furthermore, the main factor affecting the piston temperature field distribution is the gas temperature rather than the HTC. The gas temperature at piston top is overall the highest on the side near the inlet valve pit, which leads to a higher temperature on this side. Compared to light knock and normal combustion, when severe knock occurs, the thermal load on the exhaust valve pit increases significantly. This study provides an effective method for analyzing the heat transfer of the piston under knocking combustion, which is of guiding significance for controlling the thermal load of the high-temperature components in the combustion chamber.

Monitoring deforestation, forest health, and environmental criticality in a protected area periphery using Geospatial Techniques.

Protected areas in South Asia face significant challenges due to human disturbance and deforestation. The ongoing debate surrounds the recent surge in illegal encroachment of forest buffer zones in the Musali divisional secretariat division (DSD), which has led to a significant loss of forest cover over the past three decades. In this context, detecting changes in forest cover, assessing forest health, and evaluating environmental quality are crucial for sustainable forest management. As such, our efforts focused on assessing forest cover dynamics, forest health, and environmental conditions in the DSD from 1988 to 2022. We employed standardized image processing techniques, utilizing Landsat-5 (TM) and Landsat-8 (OLI) images. However, the forest area in the DSD has shown minimal changes, and environmental conditions and forest health have illustrated considerable spatial-temporal variations over the 34 years. The results indicated that 8.5 km2 (1.9%) of forest cover in the DSD has been converted to other land use classes. Overall, the Normalized Difference Vegetation Index (NDVI) has declined over time, while Land Surface Temperature (LST) exhibits an increasing trend. The regression results demonstrated a robust inverse relationship between LST and NDVI. The declining vegetation conditions and the increasing LST contribute to an increase in environmental criticality. The derived maps and indices will be beneficial for forest authorities in identifying highly sensitive locations. Additionally, they could enable land use planners to develop sustainable land management strategies.

Spatial-temporal characterization of cropland abandonment and its driving mechanisms in the Karst Plateau in Eastern Yunnan, China, 2001-2020.

The karst plateau is dominated by mountainous and hilly landforms, with low mechanization level of cropland, high difficulty of cultivation, and obvious phenomenon of cropland abandonment, which threatens regional food security. This study aims to analyze the spatial-temporal variation and its driving mechanisms of abandoned cropland in the Karst Plateau in Eastern Yunnan, China (KPEYC) between 2001 and 2020. To achieve this goal, 18 key factors from population, economic environment, cropland attributes, and farming conditions are selected. Moreover, correlation analysis, geodetector, and regression analysis methods are applied from three perspectives: temporal change, spatial distribution and spatial-temporal change. The results show that: (i) The cropland abandonment rate (CAR) in the KPEYC shows a fluctuating trend, with an average value of 9.78%, and the spatial distribution shows a pattern of "high in the center and low in the south and north". (ii) From the perspective of temporal change, gross value of agricultural production, and gross value of industrial production have the largest correlation coefficients with CAR. (iii) The explanatory power of gross tertiary industrial production, gross value of industrial production, followed by soil thickness. (iv) Gross value of agricultural production, and gross tertiary industrial production are the core driving forces for the spatial-temporal change of CAR. The higher the gross value of agricultural production and gross tertiary industrial production, the lower the CAR. elevation, soil thickness, and traffic mileage are the main driving factors for the spatial-temporal change of CAR. The study indicates that economic factors are decisive for cropland abandonment in the KPEYC. Based on the results, this study can provide decision-making support for local prevention and control of cropland abandonment, and the local community needs to promote land transfer and concentration and local urbanization according to local conditions, improve agricultural policies, improve farming conditions, etc. in order to increase farmers' enthusiasm for production, promote the rational use of cropland, and solidly push forward ecological restoration and management, optimize ecological spatial patterns, manage serious areas of rocky desertification, and appropriately alleviate the contradiction between people and land.

Experimental and numerical simulation studies of electrical resistance of cementitious materials under varying temperature history

Many in-service concrete structures are normally exposed to varying temperature condition. The time-dependent and non-uniform temperature distribution in concrete structures could affect the accurate assessment of their electrical properties. Therefore, the influences of temperature change over time and the spatial variation in temperature on the measured electrical resistance of cementitious materials are still in need of investigation. The coupled thermal-electrical responses of two types of cementitious materials under varying temperature history were investigated by a series of thermal and electrical tests in this study. Then a coupled thermal-electrical finite element (FE) model was developed to simulate the coupled thermal-electrical responses. The results show that the input direct current has a minor effect on the measured electrical resistance by embedded four-probe method and the specimen temperature when it is no greater than 3 mA. The relationship between the measured temperature at 10-mm depth of the specimen and the apparent resistance is well captured by a linear equation. The coupled thermal-electrical finite element model can predict well the developments of specimen temperature and apparent electrical resistance under varying temperature history. The FE results reveal that the non-uniform temperature variation results in the redistribution of electrical current within the tested specimen. However, electrical field between the two inner electrodes is always uniform when the embedded four-probe method is used, which is not affected by the non-uniform temperature variation.

Spatial-temporal Variations and Driving Factors of Surface Ozone over the Qinghai-Xizang Plateau from 2015 to 2021

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·（m3·a）-1）.