Meteorological Conditions Research Articles

Low Hygroscopicity of Newly Formed Particles on the North China Plain and Its Implications for Nanoparticle Growth

AbstractThe growth of newly formed particles through new particle formation (NPF) contributes a significant fraction to the cloud condensation nuclei, yet the driving mechanisms remain unclear, especially for polluted environments. To investigate the potential species contributing for nanoparticle growth in environments with significant anthropogenic influences, we measured the hygroscopicity of newly formed particles at 20–40 nm at a rural observational site in the North China Plain during winter 2018. Our results demonstrate that these particles were not very hygroscopic, with the mean hygroscopicity parameter κ of 0.13 ± 0.09. Clear differences in the inferred κ of the growing material responsible for the growth were observed among different events, indicating that even at the same region, the compounds driving particle growth may not be identical. This may be synergistically influenced by the NPF precursors, oxidants and meteorological conditions, suggesting complex mechanisms might co‐exist behind nanoparticle growth in polluted environments.

Lightning response to temperature and aerosols

Abstract Lightning has profound social implications for public safety and usually causes casualties and significant damage to infrastructure. Due to the advancement of both ground-based and spaceborne detection technology, lightning has been monitored globally in recent decades as an essential vari¬able of climate change and an indicator of severe weather. This article reviews recent progress in the study of lightning variations and their response to temperature and aerosols, based on both spaceborne and ground-based lightning data. The responses of lightning to temperature and aerosol show large spatial and temporal heterogeneity, with relation to the meteorological and environmental conditions. The latest research show that lightning exhibited significant increase in some high altitude or high latitude regions, such as the Tibetan Plateau and Arctic regions, where undergone fast warming during the recent decades and the ecosystems there is fragile. Aerosol particles play an important role in modulating lightning variations under certain dynamical and thermodynamic conditions in some regions, even on a global scale. The projected lightning activity will generally increase in the future but may with very few exceptions. Continuous long-term lightning observations with consistent spatial and temporal detection efficiency remain crucial for tracking the response of lightning to climate change in the coming decades.

Dust Emissions on Mars From Phoenix Lidar Measurements in Relation to Local Meteorological Conditions

AbstractThe diurnal cycle of dust aerosols on Mars is studied by analyzing lidar observations at the Phoenix landing site under cloud‐ and fog‐free conditions and in the absence of elevated, long‐range transported dust layers. There is a pronounced diurnal cycle in the dust‐layer height with minimum heights of 4–6 km occurring between 11:00 and 17:00 local time. The ratio of the aerosol optical depth (AOD) within the lowermost 2 km to the total AOD reaches peak values at the same time. This can be explained by local dust emissions driven by the diurnal cycle of heating and cooling in the boundary layer. Analysis of wind and pressure measurements show that the gustiness of surface winds and the frequency of convective vortices undergo diurnal variations resembling those of AOD, indicating that these processes are the main drivers for local dust emissions.

Marginal effects of economical development and university education on China’s regular exercise population

ObjectiveAlthough the regular exercise population is a key metric for gaging the success of China’s fitness-for-all activities, effective policy approaches to increase mass sports participation remain unclear. Previous research suggests that GDP, educational attainment, sports resources, and meteorological conditions could influence regular exercise participation. Therefore, this study first analyzed the macro-level correlates influencing China’s regular exercise population.MethodsWe utilize ordinary least squares (OLS) regression and geographical weighted regression (GWR) to theorize the relationship. The analysis encompasses data from the 31 administrative regions of Mainland China, as reported at the end of the 13th Five-Year Plan period. The log–log model enables us to quantify the marginal effect (elasticity) of the explanatory variables.ResultsThe OLS regression showed that regional GDP and the proportion of the population with a university education were significant predictors. In the global model, the marginal effects of regional GDP and university education were 0.048 and 0.173, respectively. Furthermore, the GWR revealed a distinct geographic pattern that corresponds to the classic Hu Line.ConclusionWhile regional GDP was also a significant correlate in our model, the elasticity demonstrates that university education had an asymmetric effect on China’s regular exercise population. Therefore, this paper sheds light on a policy priority for the upcoming 15th Five-Year Plan, emphasizing the strategic importance of expanding university education to enhance mass sports participation. In turn, a better-educated populace may yield significant secondary effects on public health and contribute to the high-quality development of the Chinese path to modernization.

Contrasting extremely warm and long-lasting cold air anomalies in the North Atlantic sector of the Arctic during the HALO-(𝒜 𝒞)3 campaign

Abstract. How air masses transform during meridional transport into and out of the Arctic is not well represented by numerical models. The airborne field campaign HALO-(𝒜𝒞)3 applied the High Altitude and Long-range Research Aircraft (HALO) within the framework of the collaborative research project on Arctic amplification (𝒜𝒞)3 to address this question by providing a comprehensive observational basis. The campaign took place from 7 March to 12 April 2022 in the North Atlantic sector of the Arctic, a main gateway of atmospheric transport into and out of the Arctic. Here, we investigate to which degree the meteorological and sea ice conditions during the campaign align with the long-term climatology (1979–2022). For this purpose, we use the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis v5 (ERA5), satellite data, and measurements at Ny-Ålesund, including atmospheric soundings. The observations and reanalysis data revealed two distinct periods with different weather conditions during HALO-(𝒜𝒞)3: the campaign started with a warm period (11–20 March 2022) where strong southerly winds prevailed that caused poleward transport of warm and moist air masses, so-called moist and warm air intrusions (WAIs). Two WAI events were identified as atmospheric rivers (ARs), which are narrow bands of strong moisture transport. These warm and moist air masses caused the highest measured 2 m temperatures (5.5 °C) and daily precipitation rates (42 mm d−1) at Ny-Ålesund for March since the beginning of the record (1993). Over the sea ice northwest of Svalbard, ERA5 indicated record-breaking rainfall. After the passage of a strong cyclone on 21 March 2022, a cold period followed. Northerly winds advected cold air into the Fram Strait, causing marine cold air outbreaks (MCAOs) until the end of the campaign. This second phase included one of the longest MCAO events found in the ERA5 record (19 d). On average, the entire campaign period was warmer than the climatological mean due to the strong influence of the ARs. In the Fram Strait, the sea ice concentration was well within the climatological variability over the entire campaign duration. However, during the warm period, a large polynya opened northeast of Svalbard, untypical for this season. Compared to previous airborne field campaigns focusing on the evolution of (mixed-phase) clouds, a larger variety of MCAO conditions was observed during HALO-(𝒜𝒞)3. In summary, air mass transport into and out of the Arctic was more pronounced than usual, providing exciting prospects for studying air mass transformation using HALO-(𝒜𝒞)3.

Application of uncertainty quantification techniques in the framework of process safety studies: Advanced dispersion simulations

AbstractIn the framework of process safety studies, consequence assessment of accidental scenarios is a crucial step affecting the eventual risk profile associated with the facilities under analysis. Conventional models used for consequence assessment are based on integral models, and may not be adequate to cope with the dynamic evolution of accidental scenarios and their three‐dimensional features. On the other hand, consequence assessment models based on computational fluid dynamics (CFD) approaches are promising to cope with complex scenarios and environments, but setting the simulation introduces relevant uncertainties associated with both the input data, assumptions, and with the modelling of physical effects involved. In the present study, uncertainty quantification (UQ) techniques are applied to support advanced safety studies based on CFD simulations of hazardous gas dispersion. Firstly, the accidental scenarios are characterized by defining release scenarios and conditions and quantifying source terms using integral models. At the same time, input meteorological data are gathered. This enables the development of high‐fidelity CFD simulations of gas dispersion based on different input sets and eventually the implementation of UQ techniques. The generalized polynomial chaos (gPC) expansion is employed to obtain hazardous gas concentration based on the variation of wind direction and speed. The present method is applied for the analysis of a real plant featuring a complex layout. The results show the advantages of the present approach by quantifying the influence of meteorological conditions and providing indications for supporting the development of protection systems and emergency measures.

Spatial and temporal heterogeneity of land surface phenology in Shanxi Province from 2001 to 2020

AbstractLand surface phenology encompasses variations in the life cycle events of plants induced by seasonal changes in environmental factors, primarily meteorological conditions. This study leverages Google Earth Engine to extract a comprehensive time series of two‐band Enhanced Vegetation Index (EVI 2) from Landsat images. Utilizing relatively sparse data spanning from 2001 to 2020, a Bayesian hierarchical model is applied at a 30 m resolution to capture the continuous temporal evolution of phenology. The fitting results of this study demonstrate excellent performance, with annual correlation coefficients consistently exceeding 0.89. The findings indicate that between 2001 and 2020, the Start of Season in Shanxi advanced by an average of 0.79 days per year, the End of Season was delayed by an average of 0.83 days per year, and the Length of Season (LOS) extended by an average of 0.80 days per year. Spatial disparities in phenological periods in Shanxi are evident, with an average LOS of 192 days on 35–36° N and only 122 days on 40–41° N. Below 1200 m, phenological periods exhibit significant changes influenced by human activities, while between 1200 m and 2600 m, LOS shows a weak trend of shortening. Above 2600 m, there is a noticeable reduction in LOS. With an increasing slope, LOS increases from an average of 175 days to 187 days (>25°). This study, utilizing Shanxi as a case study, explores the spatiotemporal evolution characteristics of vegetation phenology, aiming to support fine land management and enhance agricultural productivity.

Inter-annual changes in zooplankton in the Volga delta kultuk zone in summer (low-water period)

The paper presents the results of our studies of inter-annual changes in the structure of summer zooplankton in the kultuk zone of the Volga delta depending on hydrological and meteorological conditions. The study shows the main trends in zooplankton differences in a series of low-water years. It was found that in low-water years in the kultuk zone with low runoff, short floods and weak watering of floodplain territories, zooplankton was characterized by low species richness, reduced average individual mass of crustaceans and the number of Rotifera, but a high proportion of the number of juvenile Copepoda. In the year when the water level decreased before the flood during the period of temperature increase, and then a rapid rise with the inflow of cold waters and the removal of fish larvae, zooplankton was characterized by a minimum species richness and quantitative values of Rotifera, but increased biomass of Cladocera and the Shannon index. In every low-water year, with a stable and relatively high water level before the flood, fish spawning in the kultuk zone, biomass of zooplankton reduced due to Cladocera, and the number of species in the sample. In every mid-water year, the flood had the largest runoff and prolonged, increased flooding of the floodplain, zooplankton was characterized by the maximum total and specific species richness, abundance of Rotifera, total biomass on account of Cladocera, and the individual mass of crustaceans, the Shannon index, but the minimum proportion of juvenile Copepoda.

Deep causal inference for understanding the impact of meteorological variations on traffic

Understanding the causal impact of meteorological variations on traffic conditions (e.g., traffic flow and speed) is crucial for effective traffic prediction and management, as well as the mitigation of adverse weather effects on traffic. However, many existing studies focused on establishing associations between meteorological situations and traffic, rather than delving into causal relationships, especially with deep learning techniques. Consequently, the ability to identify specific meteorological conditions that significantly contribute to traffic congestion or delays is still limited. To address this issue, this study proposes the Meteorological-Traffic Causal Inference Variational Auto-Encoder Model (MT-CIVAE) to estimate the causal impact of fine-grained meteorological variations (e.g., rain and temperature) on traffic. Specifically, MT-CIVAE is based on the Variational Auto-Encoder and consists of an encoder to recover the distribution of latent confounders and a decoder to estimate the conditional probabilities of treatments. Transformer encoder layers are incorporated to analyze the spatial and temporal correlations of historical traffic data to further enhance the inference capability. To evaluate the effectiveness of the proposed approach for causal inference, real-world traffic flow and speed datasets collected from California, along with corresponding fine-grained meteorological datasets, are employed. The counterfactual analysis is conducted using artificially generated meteorological conditions as treatments, which allows for the simulation of hypothetical meteorological scenarios and the evaluation of their potential impact on traffic conditions. This study develops deep learning methods for assessing the causal impact of meteorological variations on traffic dynamics, offering explanations and insights that can assist transportation institutions in guiding post-meteorology traffic management strategies.

Response of PM2.5 chemical composition to the emission reduction and meteorological variation during the COVID-19 lockdown

PM2.5 is a main atmospheric pollutant with various sources and complex chemical compositions, which are influenced by various factors, such as anthropogenic emissions (AE) and meteorological conditions (MC). MC have a significant impacts on variations in atmospheric pollutant; therefore, emission reduction policies and ambient air quality are non-linearly correlated, which hinders the accurate assessment of the effectiveness of control measures. In this study, we conducted online observations of PM2.5 and its chemical composition in Hohhot, China, from December 1, 2019, to February 29, 2020, to investigate how the chemical compositions of PM2.5 respond to the variations in AE and MC. Moreover, the random forest (RF) model was used to quantify the contributions of AE and MC to PM2.5 and its chemical composition during severe hazes and the COVID-19 pandemic lockdown period. During the clean period, MC reduced PM2.5 concentrations by 124%, while MC incresed PM2.5 concentrations by 49% during severe pollution episode. Inorganic aerosols (SO42-, NO3-, and NH4+) showed the strongest response to MC. MC significantly contributed to PM2.5 (36%), SO42- (32%), NO3- (29%), NH4+ (28%), OC (22%), and SOC (17%) levels during pollution episodes. From the pre-lockdown to lockdown period, AE (MC) contributed 52% (48%), 81% (19%), 48% (52%), 68% (32%), 59% (41%), and 288% (-188%) to the PM2.5, SO42-, NO3-, NH4+, OC, and SOC reductions, respectively. The variations in MC (especially the increase in relative humidity) rapidly generated meteorologically sensitive species (SO42-, NO3-, and NH4+), which led to severe winter pollution. This study provides a reference for assessing the net benefits of emission reduction measures for PM2.5 and its chemical compositions.

Solar Radiation Measurement Tools and Their Impact on In Situ Testing—A Portuguese Case Study

Accurate knowledge of solar radiation data or its estimation is crucial to maximize the benefits derived from the Sun. In this context, many sectors are re-evaluating their investments and plans to increase profit margins in line with sustainable development based on knowledge and estimation of solar radiation. This scenario has drawn the attention of researchers to the estimation and measurement of solar radiation with a low level of error. Various types of models, such as empirical models, time series, artificial intelligence algorithms and hybrid models, for estimating and measuring solar radiation have been continuously developed in the literature. In general, these models require atmospheric, geographical, climatic and historical solar radiation data from a specific region for accurate estimation. Each analysis model has its advantages and disadvantages when it comes to estimating solar radiation and, depending on the model, the results for one region may be better or worse than for another. Furthermore, it has been observed that an input parameter that significantly improves the model’s performance in one region can make it difficult to succeed in another. The research gaps, challenges and future directions in terms of solar radiation estimation have substantial impacts, but regardless of the model, in situ measurements and commercially available equipment consistently influence solar radiation calculations and, subsequently, simulations or estimates. This article aims to exemplify, through a case study in a multi-family residential building located in Viana do Castelo, a city in the north of Portugal, the difficulties of capturing the spectrum of radiations that make up the total radiation that reaches the measuring equipment or site. Three pieces of equipment are used—a silicon pyranometer, a thermopile pyranometer and a solar meter—on the same day, in the same place, under the same meteorological conditions and with the same measurement method. It is found that the thermopile pyranometer has superior behavior, as it does not oscillate as much with external factors such as the ambient temperature, which influence the other two pieces of equipment. However, due to the different assumptions of the measurement models, the various components of the measurement site make it difficult to obtain the most accurate and reliable results in most studies. Despite the advantages of each model, measurement models have gained prominence in terms of the ease of use and low operating costs rather than the rigor of their results.

Analysis of secondary inorganic aerosols over the greater Athens area using the EPISODE–CityChem source dispersion and photochemistry model

Abstract. Secondary inorganic aerosols (SIAs) are major components of fine particulate matter (PM2.5), having substantial implications for climate and air quality in an urban environment. In this study, a state-of-the-art thermodynamic model has been coupled to the source dispersion and photochemistry city-scale chemistry transport model EPISODE–CityChem, which is able to simulate pollutants at a horizontal resolution of 100 m×100 m, to determine the equilibrium between the inorganic gas and aerosol phases over the greater Athens area, Greece, for the year 2019. In agreement with in situ observations, sulfate (SO42-) is calculated to have the highest annual mean surface concentration (2.15 ± 0.88 µg m−3) among SIAs in the model domain, followed by ammonium (NH4+; 0.58 ± 0.14 µg m−3) and fine nitrate (NO3-; 0.24 ± 0.22 µg m−3). Simulations denote that NO3- formation strongly depends on the local nitrogen oxide emissions, along with the ambient temperature, the relative humidity, and the photochemical activity. Additionally, we show that anthropogenic combustion sources may have an important impact on the NO3- formation in an urban area. During the cold period, the combined effect of decreased temperature in the presence of non-sea-salt potassium favors the partitioning of HNO3 in the aerosol phase in the model, raising the NO3- formation in the area. Overall, this work highlights the significance of atmospheric composition and the local meteorological conditions for the equilibrium distribution of nitrogen-containing semi-volatile compounds and the acidity of inorganic aerosols, especially in urban areas where atmospheric trace elements from natural and anthropogenic sources coexist.

Current benefits of wildfire smoke for yields in the US Midwest may dissipate by 2050

Wildfires throughout western North America produce smoke plumes that can stretch across the agricultural regions of the American Midwest. Climate change may increase the number and size of these fires and subsequent smoke plumes. These smoke plumes change solar radiation, meteorological conditions, and surface pollutant concentrations during the crop growing season and consequently influence yields of both corn and soybeans. We use a twelve-year panel of county-level yields from all counties east of the 100th meridian combined with measures of exposure to smoke plumes of low and high-density during the growing season to show that low-density plumes enhance yields while high-density plumes decrease yields. These effects appear to be driven by different changes in solar radiation induced by each type of plume but we observe changes in surface pollutants and precipitation as well. Because there are more low-density plumes today, the net effect is a slight increase in yields on average. As climate change makes wildfires larger and more frequent, the overall impact of smoke on yields would be be substantially more negative.

Current benefits of wildfire smoke for yields in the US Midwest may dissipate by 2050

Wildfires throughout western North America produce smoke plumes that can stretch across the agricultural regions of the American Midwest. Climate change may increase the number and size of these fires and subsequent smoke plumes. These smoke plumes change solar radiation, meteorological conditions, and surface pollutant concentrations during the crop growing season and consequently influence yields of both corn and soybeans. We use a twelve-year panel of county-level yields from all counties east of the 100th meridian combined with measures of exposure to smoke plumes of low and high-density during the growing season to show that low-density plumes enhance yields while high-density plumes decrease yields. These effects appear to be driven by different changes in solar radiation induced by each type of plume but we observe changes in surface pollutants and precipitation as well. Because there are more low-density plumes today, the net effect is a slight increase in yields on average. As climate change makes wildfires larger and more frequent, the overall impact of smoke on yields would be be substantially more negative.

Agricultural Drought Model Based on Machine Learning Cubist Algorithm and its Evaluation

Soil moisture is the most direct evaluation index for agricultural drought. It is not only directly affected by meteorological conditions such as precipitation and temperature but is also indirectly influenced by environmental factors such as climate zone, surface vegetation type, soil type, elevation, and irrigation conditions. These influencing factors have a complex, nonlinear relationship with soil moisture. It is difficult to accurately describe this non-linear relationship using a single indicator constructed from meteorological data, remote sensing data, and other data. It is also difficult to fully consider environmental factors using a single drought index on a large scale. Machine learning (ML) models provide new technology for nonlinear problems such as soil moisture retrieval. Based on the multi-source drought indexes calculated by meteorological, remote sensing, and land surface model data, and environmental factors, and using the Cubist algorithm based on a classification decision tree (CART), a comprehensive agricultural drought monitoring model at 10 cm, 20 cm, and 50 cm depth in Gansu Province is established. The influence of environmental factors and meteorological factors on the accuracy of the comprehensive model is discussed, and the accuracy of the comprehensive model is evaluated. The results show that the comprehensive model has a significant improvement in accuracy compared to the single variable model, which is a decrease of about 26% and 28% in RMSE and MAPE, respectively, compared to the best MCI model. Environmental factors such as season, DEM, and climate zone, especially the DEM, play a crucial role in improving the accuracy of the integrated model. These three environmental factors can comprehensively reduce the average RMSE of the comprehensive model by about 25%. Compared to environmental factors, meteorological factors have a slightly weaker effect on improving the accuracy of comprehensive models, which is a decrease of about 6.5% in RMSE. The fitting accuracy of the comprehensive model in humid and semi-humid areas, as well as semi-arid and semi-humid areas, is significantly higher than that in arid and semi-arid areas. These research results have important guiding significance for improving the accuracy of agricultural drought monitoring in Gansu Province.

Agricultural Drought Model Based on Machine Learning Cubist Algorithm and its Evaluation

Soil moisture is the most direct evaluation index for agricultural drought. It is not only directly affected by meteorological conditions such as precipitation and temperature but is also indirectly influenced by environmental factors such as climate zone, surface vegetation type, soil type, elevation, and irrigation conditions. These influencing factors have a complex, nonlinear relationship with soil moisture. It is difficult to accurately describe this non-linear relationship using a single indicator constructed from meteorological data, remote sensing data, and other data. It is also difficult to fully consider environmental factors using a single drought index on a large scale. Machine learning (ML) models provide new technology for nonlinear problems such as soil moisture retrieval. Based on the multi-source drought indexes calculated by meteorological, remote sensing, and land surface model data, and environmental factors, and using the Cubist algorithm based on a classification decision tree (CART), a comprehensive agricultural drought monitoring model at 10 cm, 20 cm, and 50 cm depth in Gansu Province is established. The influence of environmental factors and meteorological factors on the accuracy of the comprehensive model is discussed, and the accuracy of the comprehensive model is evaluated. The results show that the comprehensive model has a significant improvement in accuracy compared to the single variable model, which is a decrease of about 26% and 28% in RMSE and MAPE, respectively, compared to the best MCI model. Environmental factors such as season, DEM, and climate zone, especially the DEM, play a crucial role in improving the accuracy of the integrated model. These three environmental factors can comprehensively reduce the average RMSE of the comprehensive model by about 25%. Compared to environmental factors, meteorological factors have a slightly weaker effect on improving the accuracy of comprehensive models, which is a decrease of about 6.5% in RMSE. The fitting accuracy of the comprehensive model in humid and semi-humid areas, as well as semi-arid and semi-humid areas, is significantly higher than that in arid and semi-arid areas. These research results have important guiding significance for improving the accuracy of agricultural drought monitoring in Gansu Province.

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）.

DETERMINING SHALLOW WATER BATHYMETRY BY STEREO PHOTOGRAMMETRY TECHNIQUE USING WORLDVIEW-2 IMAGERY

Bathymetry in shallow-water areas plays an important role in marine environment management, marine economic resource development, and national defense and security. The echo sounding method is currently highly accurate; however, it has to deal with a variety of challenges as a result of the high cost, terrain influence, and meteorological conditions at sea. As remote sensing technology progresses, estimating bathymetry using satellite imagery will help lower measuring costs and expand monitoring coverage, particularly in shallow water, offshore areas, and difficult-to-access areas. In this article, the authors present a technique for determining bathymetry in shallow water regions by utilizing WorldView-2 stereoscopic images. The accuracy of stereoscopic depth measurement using WorldView-2 images is assessed by field depth measurement using the single-beam echo sounder. The study area is Nam Yet Island. The coefficient of determination (R2) between the two datasets (field measurement data and using satellite images) is 0.9. The stereoscopic satellite image method of depth measurement is highly accurate for regions with depths below 5 meters. The accuracy of the stereoscopic measurement decreases as the depth increases by more than 5 meters.

Meteorological Influences on Annual Variation and Trend of Autumn Surface Ozone in the Pearl River Delta

Based on the ozone （O3） monitoring data of the Pearl River Delta （PRD） from 2015 to 2022 and the reanalysis of meteorological data, the impact of meteorological conditions on the annual variation and trends of the maximum daily 8-hour average O3 concentration （MDA8-O3） were quantified using multiple linear regression （MLR） and LMG methods. The results indicated that the MLR model constructed using meteorological parameters from individual months in autumn better simulated the variation in MDA8-O3 compared to that in the model built using meteorological parameters from the entire autumn season. The combined influence of total cloud cover, relative humidity, 2 m maximum temperature, and 850 hPa zonal wind led to a reduction of 34.1 μg·m-3 in MAD8-O3 in 2020 compared to that in 2019, with contributions of 31.3%, 45.2%, 15.8%, and 6.7%, respectively. The observed trends of MDA8-O3 in the PRD for September, October, November, and the autumn season during 2015-2022 were 7.3, 5.2, 4.8, and 5.8 μg·（m3·a）-1, respectively. Among these, the trends driven by meteorological factors were 3.6, 2.4, 2.4, and 3.1 μg·（m3·a）-1. Overall, meteorological conditions contributed 53.4% to the variations in autumn MDA8-O3 in the PRD from 2015 to 2022.

A Study of Seasonal and Temporal Variances in Ambient Air Quality of Highly Polluted Cities in Rajasthan

The quality of the urban environment in tropical and subtropical densely populated cities is a complicated subject that has garnered a lot of attention in the current setting. Some of the most polluted cities in Rajasthan are Bhiwadi, Jaipur, Kota, and Udaipur, where the air quality has drastically declined over the previous ten years, according to an IQAir report. In order to determine the seasonal and temporal fluctuations in the concentrations of major air pollutants, such as carbon monoxide (CO), ozone (O3), nitrogen dioxide (NO2), sulphur dioxide (SO2), and particulate matter (PM10 and PM2.5), in an urban setting in Rajasthan, this study will examine the ambient air quality in severely polluted cities throughout the state. A comprehensive investigation of the seasonal and temporal variations in ambient air quality throughout Rajasthan's extremely polluted cities was made possible by the application of PCA and the K-Means Clustering Algorithm. We interpreted the intricate patterns of pollution oscillations by means of rigorous time-series analysis, providing insight into the dynamic interactions among meteorological conditions, sources of pollution, and regulatory actions. The results indicate that there were more seasonal variations during the summer, and that levels of particulate matter (PM10 and PM2.5) and nitrogen dioxide (NO2) in places like Jaipur, Bhiwadi, Kota, and Udaipur alarmingly rose above pre-pandemic levels. This highlights the significance of identifying and addressing the various challenges caused by air pollution at different times of the year and in different seasons. Furthermore, identifying the main sources of pollution and assessing the effectiveness of current legislation offer insightful information for focused actions.