Influence Of Meteorological Conditions Research Articles

Impact of Meteorological Conditions on Overhead Transmission Line Outages in Lithuania

This study investigates the impact of meteorological conditions on unplanned outages of overhead transmission lines (OHTL) in Lithuania’s 0.4–35 kV power grid from January 2013 to March 2023. Data from the Lithuanian electricity distribution network operator and the Lithuanian Hydrometeorological Service were integrated to attribute outage events with weather conditions. A Bayesian change point analysis identified thresholds for these meteorological factors, indicating points at which the probability of outages increases sharply. The analysis reveals that wind gust speeds, particularly those exceeding 21 m/s, are significant predictors of increased outage rates. Precipitation also plays a critical role, with a 15-fold increase in the relative number of outages observed when 3 h accumulated rainfall exceeds 32 mm, and a more than 50-fold increase for 12 h snowfall exceeding 22 mm. This study underscores the substantial contribution of lightning discharges to the number of outages. In forested areas, the influence of meteorological conditions is more significant. Furthermore, the research emphasizes that combined meteorological factors, such as strong winds accompanied by rain or snow, significantly increase the risk of outages, particularly in these forested regions. These findings emphasize the need for enhanced infrastructure resilience and targeted preventive measures to mitigate the impact of extreme weather events on Lithuania’s power grid.

The influence of meteorological conditions of the Non-Chernozem zone on the phytosanitary condition of grain bean crops

There is a tendency to expand the area of bean cultivation in the northern direction, which is due to climatic changes and breeding achievements. There are 191 varieties of beans registered in the state register of breeding achievements approved for use, of which 31 varieties are recommended for cultivation in the belt between 50° and 60° latitude. The relevance of bean cultivation in the Non-Chernozem zone is increasing. This is due to the increased demand for beans, which are characterized by high nutritional value. Also, this crop is promising as an alternative in grain crop rotations. The study of the phytosanitary condition of bean plants in potentially new cultivation regions in order to develop a crop protection system is of interest. The promotion of crops to new territories is inevitably accompanied by phytosanitary risks, which have been little studied due to insufficient information about bean diseases. The literature provides an assessment of potential threats. However, their manifestation is closely related to the climatic and weather conditions of the growing season. The analysis of the interrelationships of the phytosanitary condition of crops with the temperature and humidity regimes of the bean growing period is relevant.

Analysis of the interrelationship between precipitation and confirmed dengue cases in the city of Recife (Brazil) covering climate and public health information.

Large-scale epidemics of arboviruses, such as dengue, have heightened societal awareness regarding the necessity of combating the primary transmission vectors. Equally critical is the identification of environmental conditions and variables that influence vector population dynamics. Aedes aegypti, the primary vector of arboviruses such as dengue and Zika in Brazil, is closely associated with the climatic and geographical conditions of urban environments. This study examines the relationship between precipitation and confirmed dengue cases in Recife (Brazil), employing regression and quantile analyses to evaluate the influence of meteorological conditions on the disease's spread. The findings reveal a direct correlation between monthly averages of precipitation and confirmed cases, although this is apparent only when excluding years of epidemic peaks. The highest number of cases generally aligns with the rainy season, and the lowest with the dry season, with weak, moderate and strong precipitation events being closely linked to increased dengue incidence. However, notable discrepancies were identified: four out of six major outbreaks occurred in drier months, challenging the assumption of a straightforward relationship between rainfall and dengue incidence. These findings underscore the multifaceted nature of dengue dynamics, suggesting that while precipitation plays a significant role, other factors, including serotype circulation and broader climatic phenomena, are equally critical in driving outbreaks. This complexity highlights the need for a more comprehensive understanding of the mechanisms influencing dengue epidemics.

Statistical modeling of the effects of wind speed, air temperature and relative humidity on the concentration of carbon monoxide in the urban atmosphere

The high carbon monoxide content in the urban atmosphere is one of the most important indicators of poor air quality in megacities such as Moscow. This study is to evaluate the importance of wind speed, air temperature, and relative air humidity for predicting the concentrations of carbon monoxide for the day ahead using a simplified one-dimensional quasistationary statistical model. It is shown that the concentration of carbon monoxide in the Moscow atmosphere is determined by a combination of internal (previous days CO concentration) and external (meteorological conditions) factors. The variation of carbon monoxide concentration at one station differs from the variation at another station due to the differences in local conditions. Taking into account wind speed and air temperature increases the predictive value of the onedimensional quasi-stationary statistical model for most of the stations. In contrast to wind, relative air humidity decreases the predictive value of the model for most of the stations. This means that meteorological factors considered in this study could have different effects on predicting carbon monoxide concentration in the case of Moscow. The data from the Balchug weather station, located in the city center, offers a more accurate CO concentration forecast for most Moscow stations compared to the VDNKh weather station. For a more complete description of the influence of meteorological conditions on the predicted low concentration of gases, it is useful to take into account the model wind direction, surface air pressure, and the intensity of mixing in the urban boundary layer.

The Impact of Chinese New Year on Air Quality in North China Based on Machine Learning

The reduced economic and social activities during the Chinese New Year offer a unique opportunity to assess declines in anthropogenic emissions. However, limited research quantifies changes in PM2.5, NO2, and O3 concentrations during this period while accounting for meteorological conditions. This study utilized machine learning and the Time Warping-based K-Means method to evaluate the effectiveness of firework bans, the impact of emission reductions on pollutants during the Chinese New Year holiday, and the influence of meteorological conditions on pollutant concentrations during this period. Our findings reveal a significant reduction in emissions, with PM2.5 and NO2 concentrations decreasing by up to 24.76% and 33.39%, respectively, while O3 concentrations increased by up to 45%. Regions without firework bans saw peak PM2.5 levels on New Year’s Eve. The ban has been effective, though signs of relaxation appeared in 2023. It is worth noting that pollution during the 2018 Chinese New Year holiday was more severe than before the holiday because the meteorological conditions before the holiday were favorable for pollutant dispersion, while unfavorable meteorological conditions during the holiday masked the emission reductions that occurred due to the holiday period. These results emphasize the significant role of meteorological conditions and the need for stricter emission controls beyond traffic restrictions or factory shutdowns to mitigate haze pollution during adverse weather.

METHODS OF FORECASTING GRAIN CROP YIELD INDICATORS TAKING INTO ACCOUNT THE INFLUENCE OF METEOROLOGICAL CONDITIONS IN THE INFORMATION-ANALYTICAL SUBSYSTEM

Forecasting crop yields is one of the key challenges for the agricultural sector, especially in the context of a changing climate and unstable weather conditions. Kazakhstan, possessing significant territories suitable for growing grain crops, faces many challenges related to the effective management of agricultural activities. In this regard, yield forecasting becomes an integral part of planning and decision-making processes in agriculture. Information and analytical subsystems that integrate yield forecasting methods allow agribusinesses to estimate future production more accurately, minimise risks associated with climate change and optimise resource use. An important component of such systems is the consideration of weather conditions, as weather factors have a direct impact on crop growth and development. The purpose of this article is to develop and evaluate modern methods of forecasting grain yields taking into account the influence of weather conditions, as well as their integration into information-analytical subsystems to improve the accuracy of agricultural forecasting. To achieve this goal, the article addresses the following tasks: to analyse existing methods of yield forecasting and identify their advantages and disadvantages, to develop forecasting models, including machine learning methods such as gradient bousting and recurrent neural networks, to validate the developed models on the basis of historical data using cross-validation methods, to evaluate the effectiveness of the proposed methods and compare them with basic models such as linear regression and simple average, to evaluate the effectiveness of the proposed methods and to compare them with the basic models such as linear regression and simple average. This article reviews modern methods of forecasting grain crop yields in Kazakhstan, as well as technologies used in information-analytical subsystems. Particular attention is paid to the analysis of the influence of meteorological conditions on yields and the development of models that take this factor into account. The presented review and research results are aimed at improving the existing approaches to the management of agricultural processes under conditions of growing uncertainty caused by climate change. The article explores an important scientific task related to the development of methods for step-by-step forecasting of agrometeorological factors and grain yields, relying on the principle of analogy.

Influence of weather conditions on the duration of the growing season and economically valuable traits and properties of soybean varieties of domestic and foreign selection

The influence of meteorological conditions of the region on the duration of the vegetation period and production process of domestic and foreign soybean breeding was determined. The research was conducted in the period 2021–2023 in the Bryansk region. 18 soybean varieties included in the Register of breeding achievements admitted to use were taken as an object of research. According to the duration of the vegetation period varieties are divided into three groups of ripeness: fast-ripening – Vita, Lira, Leader 10, Amadea, Lisbon, Vera, early-maturing – Protina, Volma, Kora, Zusha, Mezenka, Osmon, Sultana, medium-maturing – Sculptor, Pripyat, Puma, Sirelia, Ros. It was revealed that, on average, plant height increased with the increasing vegetation period: fast-ripening – 75.8 cm, early-maturing – 77.3, medium-maturing – 83.0 cm. It was revealed that, on average, with an increase in the growing season, the height of plants increased: precocious – 75.8 cm, early-maturing – 77.3 cm, middle-maturing – 83.0 cm. 4 genotypes (22 %) with low attachment of the lower beans were identified: Vera, Kora, Pripyat, Sirelia, 11 varieties (61 %) with medium attachment of the lower beans: Vita, Lira, Leader 10, Amadea, Lisbon, Volma, Zusha, Osmon, Sultana, Sculptor, Puma, 3 varieties (17 %) with a high attachment: Protina, Mezenka, Ros. The weight of 1000 seeds was calculated – varieties with larger filled seeds: Sultana (176), Ros (177), Lisbon (162), Vita (158), Vera (158), Sirelia (158). Varieties with the highest seed yield were identified: Ros (25.3 c/ha), Zusha (22.8), Sculptor (22.2), Pripyat (22.2), Lisbon (22.1 c/ha). Crude protein was determined by the Kjeldahl method, crude fat by the Soxhlet. The high content of crude protein was formed by the varieties of domestic breeding: Puma (36.66%), Mezenka (35.95%), Osmon (35.75%), Vera (35.34%); foreign varieties: Sirelia (39.05%), Sultana (38.27%), Pripyat (36.17%). The crude fat content in the seeds varied both by year and by variety. The maximum fat value was noted in the genotypes: Ros (23.78%), Mezenka (23.72%), Sculptor (23.18%), Zusha (22.45 %), Sultana (22.36%).

Numerical study for atmospheric transport of radioactive materials for hypothetical severe nuclear accident under different meteorological conditions

A study for atmospheric transport is essential for the consequence assessment of severe nuclear accidents since radionuclides could be released from the nuclear facility into the atmosphere and cause radioactive pollution in the environment. Atmospheric transport behaviors are strongly related with meteorological conditions, which can obviously influence the transport and diffusion characteristics of radioactive materials in the atmosphere; thus, it is meaningful to investigate the coupling effects between meteorological processes and transport behaviors of radioactive materials. To evaluate the influence of meteorological conditions on atmospheric transport, meteorological parameters for different seasons were first acquired by the weather research forecast model. Furthermore, atmospheric transport behaviors of radioactive materials were simulated by the meso-scale numerical model under different meteorological conditions, and numerical analyses were conducted toward transport and deposition behaviors of radioactive materials. In addition, the influence of FDDA (four-dimensional data assimilation) on meteorological parameters and atmospheric transport behaviors was researched. The present study is important for strengthening consequence assessment for severe nuclear accident and made it possible to apply the data assimilation technology in further research works.

Characteristics of Water-soluble Ion Pollution in PM2.5 and the Causes of High Acidity of PM2.5 in Dalian

To gain a deeper understanding of the pollution status and influencing factors of fine particles （PM2.5） and their water-soluble ions （WSI） in Dalian and to implement precise control of pollution events such as haze and acid rain, PM2.5 samples were collected in Dalian from June 2021 to May 2022. Then, the mass concentrations of PM2.5 and WSI were determined using the weight method and ion chromatography, respectively, and the pollution characteristics and sources were analyzed. Furthermore, the causes of the high acidity of PM2.5 in spring were discussed. The results showed that the annual average mass concentrations of PM2.5 and WSI in Dalian during the sampling period were （33.24 ±28.87） μg·m-3 and （18.66 ±20.52） μg·m-3, respectively, and the secondary ions （SNA, including SO42-, NO3-, and NH4+） accounted for the highest proportion of WSI [（86.2 ±9.3）%]. The order of ion concentration levels from highest to lowest was： NO3->SO42->NH4+>Cl->K+>Ca2+>Na+>Mg2+>F-. Due to the influence of meteorological conditions and coal combustion emissions during the concentrated heating period from late autumn to early spring, the seasonal variation in PM2.5 and WSI was winter>spring>autumn>summer, whereas SNA was the highest in spring and the lowest in summer. The results of correlation and principal component analysis showed that WSI in PM2.5 was mainly from the secondary transformation of atmospheric SO2 and NO2 （contributing to the majority of SNA）, mixed sources of combustion and dust （characterized by K+, Mg2+, Cl-, and F-）, and sources of sand and sea salt （characterized by Na+, Ca2+, and Mg2+）. In summer, the main combustion source was biomass burning, whereas in autumn, winter, and spring, coal combustion emissions were predominant. The change in wind direction from autumn to winter brought by a shift from the source of sea salt to soil dust； additionally, the external pollution transported by northwest winds contributed to the complexity of the sources of WSI in PM2.5 during spring in Dalian. ISORROPIA-II model simulations suggested NH4NO3 as the most present solid aerosol form in PM2.5 in Dalian, followed by CaSO4 and （NH4）2SO4； compared to that in solid aerosols, more SNA existed in liquid aerosols. The annual average pH of PM2.5 in Dalian was 5.65 ±3.00, with pH values close to neutral in summer, autumn, and winter but significantly acidic in spring （2.03 ±3.18）. The high acidity observed in spring was attributed to the combination of low temperature, high humidity, and high SNA concentrations. These conditions resulted in higher aerosol water content and increased gas-to-particle conversion rates, ultimately leading to an ammonia-deficient environment. The backward trajectory and PSCF results indicated that the external transport of high acidity PM2.5 in spring mainly came from the northwest （45.0%） and southwest （40.8%） directions. Mobile source emissions made the most significant contribution to the transportation of pollutants in the former, forming high-pollution source areas in the Beijing-Tianjin-Hebei Region, which may have been mainly related to urban motor vehicle and port vessel emissions； the latter was influenced by relatively strong stationary sources and showed higher SO2 emissions in the southern part of Henan Province and the central part of Jiangsu Province.

Exploration of radical chemistry, precursor sensitivity and O3 control strategies in a provincial capital city, northwestern China

As the core city in northwestern China, Xi'an has suffered from serious O3 pollution in recent years. An observational model, coupled with the Master Chemical Mechanism (V3.3.1) and comprehensive data, analyzed atmospheric oxidation capacity (AOC), O3 formation mechanism, precursor sensitivity and control strategies in Xi'an. The study examined two periods (period I and II), with notable temperature and humidity differences. Despite lower precursor levels in period I, an O3 episode (peak value: 102.68 ppb) occurred only in period I, highlighting the crucial influence of meteorological conditions. Higher removal rate of primary pollutants occurs in period I, but meteorological conditions may not alter the dominant factor of AOC. Period I exhibited markedly high OH reactivity, with aromatics, alkenes, and NOx being the main contributors. HONO photolysis (50.78%) in the morning and O3 photolysis (34.13%) in the afternoon were the main HOx sources in period I, while HONO photolysis (62.86–80.68%) was significant at daytime for period II. Higher temperatures in period I accelerated all reaction rates involved in radical cycling, especially emphasizing RO2 + NO → RO + NO2. The results of sensitivity analysis showed that O3 sensitivity regime is VOC-limited in Xi'an, but the specific reactive species target VOC reductions emissions exhibited discrepancies for two distinct periods. A 20% decrease in VOCs and a 50% decrease in NOx (VOCs/NOx = 0.4) was proposed to be the optimal solution to achieve the O3 control target in period I. This study enhances our understanding of O3 formation under different meteorological conditions and provides scientific evidence for O3 pollution abatement policies in northwestern China, with potential applicability to other cities in this region and similar countries.

Influence of meteorological conditions on the variability of indoor and outdoor particulate matter concentrations in a selected Polish health resort

The article evaluates air pollution by particulate matter (PM) in indoor and outdoor air in one of the Polish health resorts, where children and adults with respiratory diseases are treated. The highest indoor PM concentrations were recorded during the winter season. Therefore, the maximum average daily concentration values in indoor air for the PM10, PM2.5, and PM1 fractions were 50, 42 and 23 µg/m3, respectively. In the case of outdoor air, the highest average daily concentrations of PM2.5 reached a value of 40 µg/m3. The analyses and backward trajectories of episodes of high PM concentrations showed the impact of supra-regional sources and the influx of pollutants from North Africa on the variability of PM concentrations. The correlation between selected meteorological parameters and PM concentrations shows the relationship between PM concentrations and wind speed. For example, the correlation coefficients between PM1(I) and PM1(O) concentrations and wind speed were − 0.8 and − 0.7 respectively. These factors determined episodes of high PM concentrations during winter periods in the outdoor air, which were then transferred to the indoor air. Elevated concentrations in indoor air during summer were also influenced by chimney/gravity ventilation and the appearance of reverse chimney effect.

Influences of meteorological conditions, runoff, and bathymetry on summer thermal regime of a Great Lakes estuary

To better understand the primary drivers of the thermal regime in a Great Lakes estuary, and their implications for local biota, water temperature variations in the Milwaukee Estuary of Lake Michigan were studied between July and October of 2019 using a network of 25 sensors at 18 locations. Like Lake Michigan, the estuary was thermally stratified July to October, and historically dredged channels with water depths greater than 6 m allowed for the free movement of cold lake water into the estuary. However, temperatures in the estuary fluctuated rapidly both spatially and temporally, reflecting lake temperature fluctuations driven by changing meteorological conditions. Lake-driven upwelling and downwelling events influenced water temperature more than tributary contributions. Periodic upwelling and downwelling events caused temperature changes by up to 15 °C in less than 24 h. Nearshore upwelling events occasionally allowed cold, hypolimnetic lake water to reach tributary portions of the estuary beyond dredged areas, while downwelling events disrupted thermal stratification and caused the deep, dredged portions of the estuary to exceed 20 °C. Thermal impacts from these events were especially noticeable in transition zones between dredged and not dredged channels less than 2 m deep. The warming effects from downwelling persisted longer inside the estuary – up to two weeks – than cooling effects from upwelling, which typically lasted a few days. The predominantly lake-driven, rapid summer water temperature fluctuations observed in the Milwaukee Estuary serve as an important consideration in habitat restoration activities happening in Great Lakes estuaries.

Сравнительная оценка урожайности зерновых культур в зависимости от лимитирующих факторов в условиях почвозащитного земледелия

Abstract. The aim of work – to identify the influence of limiting factors on increasing the yield of grain crops in crop rotations and soil fertility in a sloping landscape. The following methods were used during the study: field, Kosyrev, thermostatic-weight, ionometric, Machigin, Tyurin, quantitative, balance calculations of yield. Scientific novelty. For the first time, the yield of field crops in grain-steam crop rotations was estimated from the influence of meteorological conditions, moisture, mobile forms of nutrients, humus, seedlings and weed infestation in the arid conditions of the Orenburg Trans-Urals. Results. As a result of research (2021–2023), it was revealed that during the growing season (May-August), the average monthly air temperature (18.6 ℃) and the number of dry days (76) adversely affect (55.85 and 67.50 %) the yield (1.00 t/ha) of barley in the lower part of the slope. In the upper and middle parts of the slope, dry days (56.75 and 64.45 %) negatively affect the yield (1.44 and 1.20 t/ha) of barley. Precipitation in the form of rain (116 mm) during the growing season has a beneficial effect (53.05 %) on the yield (1.34 t/ha) of durum wheat in the middle part of the slope. Spring moisture reserves (152.1 mm) in a meter layer of soil have a positive effect (75.15 %) on the yield (1.30 t/ha) of soft wheat in the upper part of the slope. The increase in yield (1.45 t/ha) of durum wheat and (1.44 t/ha) of barley in the upper part of the slope is due to the influence of (72.30 and 56.05 %) phosphorus content (4.7 and 4.1 mg / 100 g) in the arable soil layer by the end of the growing season. In the middle and lower parts of the slope, the yield of soft wheat (1.20 and 1.10 t/ha) depends on the effect (65.50 and 54.01 %) of the residual content (4.7 and 4.4 mg / 100 g) of phosphorus. The use of agrotechnical soil protection techniques increases the yield of grain crops in crop rotations and soil fertility on the slope, especially on the upper part.

Assessment of the status of PM2.5 and ground-level O3 pollution variations and meteorological factor effects: A case study in Long An Province, Vietnam

Abstract Long An Province has a favorable geographical position in socio-economic development exchanges and is a fundamental bridge between the area of Ho Chi Minh City (HCMC) and the Southeast localities with the Southwestern provinces. Along with the master plan for socio-economic development with a vision for 2030, the province’s air quality issues, especially PM2.5 and surface O3 pollution, will be complex and pose many challenges to effective air pollution management and control. This study aims to investigate the effects of major meteorological characteristics (temperature–T, relative humidity–RH, surface pressure–P, and wind speed–WS) on temporal changes in the correlation between PM2.5 and surface O3 concentrations in Long An province in the 2018-2020 period (computed by a system of coupled WRF/CMAQ models) through trend evaluation using by Mann-Kendall (MK) Test and Theil-Sen Slope, and Pearson correlation analysis. The study results reported that in terms of temporal variations, both value levels of PM2.5 and O3 in the dry season tended to surpass that in the rainy season. Moreover, PM2.5 levels tended to exceed the 24-hour mean threshold (between 1.001 and 3.14 times) and the annual mean threshold (from 1.81 to 2.03 times) of the Vietnamese National Ambient Air Quality Standard. Based on the Pearson correlation analysis, significant influences of meteorological conditions on the pollution status of PM2.5 and surface O3 were also demonstrated, such as the different positive and negative correlations of each meteorological factor on concentration. These outstanding results serve as an essential reference for developing air quality targets and proposing future air pollutant control measures in Long An Province.

Short-Term Photovoltaic Power Prediction Based on a Digital Twin Model

Due to the influence of meteorological conditions, shipboard photovoltaic (PV) systems have problems such as large fluctuation and inaccurate prediction of the output power. In this paper, a short-term PV power prediction method based on a novel digital twin (DT) model and BiLSTM is proposed. Firstly, a PV mechanism model and a data-driven model were established, in which the data-driven model was updated iteratively in real time using the sliding time window update method; then, these two models were converged to construct a PV DT model according to the DS evidence theory. Secondly, a BiLSTM model was built to make short-term predictions of the PV power using the augmented dataset of the DT model as an input. Finally, the method was tested and verified by experiments and further compared with main PV prediction methods. The research results indicate the following: firstly, the absolute error of the DT model was smaller than that of the mechanism model and the data-driven model, being as low as 5.62 W after the data update of the data-driven model; thus, the DT model realized data augmentation and high fidelity. Secondly, compared to several main PV prediction models, the PV DT model combined with BiLSTM had the lowest RMSE, MAE, and MAPE; the best followability; and the smallest absolute error under different weather conditions, which was especially obvious under cloudy weather conditions. In summary, the method can accurately predict the shipboard PV power, which has great theoretical significance and application value for improving the economy and reliability of solar ship operation.

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.

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.

Identification and threshold analysis of strong winds and heavy rain disaster factors based on frequent-pattern mining

Meteorological disasters pose a significant challenge to megacities, and quantitatively identifying the influence of meteorological conditions on disasters is an urgent issue. Compared to other traditional datasets, disaster reporting data offers significant informational value for urban meteorological risk response and assessment due to its high credibility and timeliness. This study is based on meteorological disaster reporting data for Shanghai over the past 10 years and focuses on the identification and threshold analysis of meteorological disaster risk factors for disaster-affected entities, offering a novel perspective on analyzing disaster-causing risk under the combined influence of meteorological conditions. The findings suggest that disaster reporting data can be used for meteorological risk assessment. There were noticeable differences in the disaster thresholds for affected entities in central urban areas (city center) versus suburban areas (suburbs). The combined effects of strong winds and heavy rain were often significant contributors to the occurrence of disasters. Furthermore, several distinct disaster characteristics were identified through frequent pattern (FP)-growth mining. The results of this study provide technical support for early warning and disaster management under specific weather conditions by constructing a quantitative framework for understanding the impact of hazardous weather.

Temperature Inversion and Particulate Matter Concentration in the Low Troposphere of Cergy-Pontoise (Parisian Region)

This study aims to elucidate the influence of meteorological conditions on particle levels in Cergy-Pontoise. It explores the temporal variability of PM10 pollution days by associating them with the vertical temperature profile derived from conventional radiosondes from 2013 to 2022 (regional station). The results indicate that nearly 80% of exceedance days were associated with thermal inversions, primarily observed in winter and typically lasting 1 to 3 days. Analysis of winter thermal inversion characteristics suggests that those linked to pollution primarily occur near the ground, with higher intensity in December (12.1 °C) and lower in February (10.3 °C). Persistent inversions (extended nocturnal by diurnal inversion) account for 91.4% of the total inversions associated with high concentrations. Captive balloon soundings and temperature measurements at different altitudes were conducted during the winter of 2022/2023 to clarify thermal inversion in the Oise Valley at the center of Cergy-Pontoise. The results highlight three nocturnal wind circulation mechanisms in the valley, including downslope flow, circulation influenced by an urban heat island, and mechanical air evacuation under an inversion layer towards the less steep East side of the valley. Analysis of PM with the temperature gradient in the Oise Valley shows a significant correlation, suggesting an increase in concentrations during locally detected inversions and a decrease during atmospheric disturbance.

Response of organic aerosol in Beijing to emission reductions during the XXIV Olympic Winter Games

Organic aerosol (OA) serves as a crucial component of fine particulate matter. However, the response of OA to changes in anthropogenic emissions remains unclear due to its complexity. The XXIV Olympic Winter Games (OWG) provided real atmospheric experimental conditions on studying the response of OA to substantial emission reductions in winter. Here, we explored the sources and variations of OA based on the observation of aerosol mass spectrometer (AMS) combined with positive matrix factorization (PMF) analysis in urban Beijing during the 2022 Olympic Winter Games. The influences of meteorological conditions on OA concentrations were corrected by CO and verified by deweathered model. The CO-normalized primary OA (POA) concentrations from traffic, cooking, coal and biomass burning during the OWG decreased by 39.8 %, 23.2 % and 65.0 %, respectively. Measures controlling coal and biomass burning were most effective in reducing POA during the OWG. For the CO-normalized concentration of secondary OA (SOA), aqueous-phase related oxygenated OA decreased by 51.8 % due to the lower relative humidity and emission reduction in precursors, while the less oxidized‑oxygenated OA even slightly increased as the enhanced atmospheric oxidation processes may partially offset the efficacy of emission control. Therefore, more targeted reduction of organic precursors shall be enhanced to lower atmospheric oxidation capacity and mitigate SOA pollution.