Influence Of Meteorological Conditions Research Articles

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.

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.

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.

Exploring the effects and interactions of meteorological factors on the incidence of scrub typhus in Ganzhou City, 2008-2021.

Scrub typhus poses a substantial risk to human life and wellbeing as it is transmitted by vectors. Although the correlation between climate and vector-borne diseases has been investigated, the impact of climate on scrub typhus remains inadequately comprehended. The objective of this study is to investigate the influence of meteorological conditions on the occurrence of scrub typhus in Ganzhou City, Jiangxi Province. METHODS: From January 1, 2008 to December 31, 2021, we gathered weekly records of scrub typhus prevalence alongside meteorological data in Ganzhou city. In order to investigate the correlation between meteorological factors and scrub typhus incidence, we utilized distributional lag nonlinear models and generalized additive models for our analysis. Between 2008 and 2021, a total of 5942 cases of scrub typhus were recorded in Ganzhou City. The number of females affected exceeded that of males, with a male-to-female ratio of 1:1.86. Based on the median values of these meteorological factors, the highest relative risk for scrub typhus occurrence was observed when the weekly average temperature reached 26°C, the weekly average relative humidity was 75%, the weekly average sunshine duration lasted for 2h, and the weekly mean wind speed measured 2m/s. The respective relative risks for these factors were calculated as 3.816 (95% CI: 1.395-10.438), 1.107 (95% CI: 1.008-1.217), 2.063 (95% CI: 1.022-4.165), and 1.284 (95% CI: 1.01-1.632). Interaction analyses showed that the risk of scrub typhus infection in Ganzhou city escalates with higher weekly average temperature and sunshine duration. The findings of our investigation provide evidence of a correlation between environmental factors and the occurrence of scrub typhus. As a suggestion, utilizing environmental factors as early indicators could be recommended for initiating control measures and response strategies.

Influence of meteorological conditions of the year on the biochemical composition of sorghum grain

Relevance. Currently, the study of the interaction of these traits with each other is a very important task of breeding. In the work, 15 varieties of grain sorghum were studied, differing in morphological parameters, growing season, degree of grain ripening, biochemical composition and yield of grain and green mass. The purpose of the study is to study the interaction of environmental conditions on the yield and biochemical composition of sorghum grain and to assess the significance of these relationships.Methods. The studies were conducted in 2020, 2021 and 2022. The years of research differed in meteorological conditions (hydrothermal Selyaninov coefficient (SCC) was 0.56–0.76). To assess the biochemical composition in sorghum grain, the spectroscopy method was used using an infrared analyzer Spectra Star XT. The grain yield was determined according to the generally accepted method. Results. The experimental results were processed using the “Agros 2.09” software package by the method of correlation analysis. Based on the results of three years of research, a correlation was established between biochemical composition and the meteorological conditions of the year (sum of temperatures, total precipitation, SCC), as well as a significant effect of these traits on the yield of sorghum grain.

Task-Driven Path Planning for Unmanned Aerial Vehicle-Based Bridge Inspection in Wind Fields

Using unmanned aerial vehicles (UAVs) for bridge inspection is becoming increasingly popular due to its ability to improve efficiency and ensure the safety of monitoring personnel. Compared to traditional manual monitoring methods, UAV inspections are a safer and more efficient alternative. This paper examines the impact of meteorological conditions on UAV-based bridge monitoring during specific tasks, with the aim of enhancing the safety of the UAV’s costly components. The wake vortex behind a bridge structure can vary over time due to airflow, which can have a direct impact on the safety of UAV flights. To assess this impact, numerical analysis is conducted based on monitoring requirements specific to different tasks, taking into account wind speed, wind direction, and air temperature. In order to optimize UAV trajectory, it is important to consider the wake vortex intensity and its associated influence region, which can pose a potential danger to UAV flight. Additionally, the analysis should take into account the aerodynamic effects of different types of bridge columns on the wake vortex. An optimization algorithm was utilized to optimize the trajectory of a UAV during bridge inspections within the safe region affected by wind fields. This resulted in the determination of an effective and safe flight path. The study reveals that varying wind speeds have an impact on the safe flight zone of UAVs, even if they are below the operational requirements. Therefore, when monitoring bridges using UAVs, it is important to take into account the influence of meteorological conditions. Furthermore, it was observed that the flight path of UAVs during square cylinder column monitoring is longer and more time-consuming than round cylinder column monitoring. Determining an effective UAV inspection path is crucial for completing bridge monitoring tasks in windy conditions, establishing bridge inspection standards, and developing the Intelligent Bridge Inspection System (IBIS).

Ultra-short-term forecasting of agricultural intelligent greenhouse power load based on ISDS and MDUS-LSTM

The agricultural smart greenhouse electric load differs significantly from the traditional building electric load. It is more susceptible to the influence of meteorological conditions, featuring poor regularity and significant random fluctuations, so load forecasting faces new challenges. To address this challenge, this paper focuses on a variety of meteorological and historical similar feature extraction, innovates model updating strategies, and proposes a new ultra-short-term forecasting method for agricultural smart greenhouse electric loads. Firstly, an improved similar day selection (ISDS) method is designed. This method considers both trend similarity and magnitude similarity of time series. It sets weights according to the degree of influence of different meteorological features on load, thus improving the learning efficiency of the model. Next, a model dynamic update strategy (MDUS) is designed. This strategy consists of initial training of forecasting model parameters based on historical similar daily loads and online updating of forecasting model parameters based on adjacent daily load data. Then, the forecasting model is trained online and fine-tuned with the parameters based on the adjacent daily load data. The dynamically updated forecasting model is used to achieve ultra-short-term forecasting of the electric load to improve the forecasting accuracy and adaptability of the model. Finally, the effectiveness of the proposed method is verified by actual electrical load data, real-time meteorological data, and NWP data collected in an agricultural smart greenhouse in Shouguang, China.

The Impact of Meteorological Conditions and Emissions on Tropospheric Column Ozone Trends in Recent Years

Based on OMI/MLS data (2005–2020) and Community Earth System Model (CESM2) simulated results (2001–2020), annual variation trends of tropospheric column ozone (TCO) in the recent two decades are explored, and the separate impacts of meteorological conditions and emissions on TCO are quantified. The stratospheric ozone tracer (O3S) is used to quantify the contribution of stratospheric ozone to the trend of TCO. The evaluation shows that the simulated results capture the spatial-temporal distributions and the trends of tropospheric column ozone well. Over the East Asia and Southeast Asia regions, TCO is increasing, with a rate of ~0.2 DU/yr, which is primarily attributed to the emission changes in ozone precursors, nitrogen oxide (NOx) and volatile organic chemicals (VOCs). But the changes in meteorological conditions weaken the increase in TCO, even leading to a decrease in East Asia in spring and summer. TCO is decreasing in the middle and high latitudes of the southern hemisphere, which is mainly attributed to the changes in meteorological conditions. The increasing rates are the highest in autumn, especially over North America, East Asia, Europe and South of East Asia, with rate values of 0.20, 0.31, 0.17, and 0.32 DU/yr, respectively. Over the equatorial region, the contribution of stratospheric ozone to TCO is below 10 DU, and shows a weak positive trend of ~0.2 DU/yr. In the latitude of ~30°N/S, the stratospheric contribution is high, ~25 DU, and is affected by the sinking branch of the Brewer–Dobson circulation and stratosphere–troposphere exchange in the vicinity of tropical jet stream. The stratospheric contribution to TCO in the north of 30°N is significantly decreasing (~0.6 DU/yr) under the influence of meteorological conditions. Changes in emissions weaken the decrease in stratospheric contributions in the north of 30°N and enhance the increase in 30°S–30°N significantly. The trends of stratospheric contributions on TCO partly explain the trends of TCO which are mostly affected by the change in emissions. To control the increasing TCO, actions to reduce emissions are urgently needed.

Conditionally Background Level of Aerosol Pollution of Near-Surface Air in Moscow and One of Its Suburbs: Seasonal Variations

The data of continuous observations of aerosol composition in the near-surface atmosphere in Moscow (in the city center) and in Moscow region (near Zvenigorod, Moscow region) for three years, from autumn 2019 to the end of 2022, are analyzed. The obtained data were compared with the results of observations on the Moscow network stations “Mosecomonitoring”. The concept of conditionally background aerosol pollution of the atmosphere in Moscow is introduced for those days when the average daily concentration of PM10 is less than the MPC value (60 µg/m3). Previously, the authors found that all episodes of increased aerosol pollution in Moscow with daily average PM10 concentration higher than the MPC value, are associated either with the presence of a close local source in the city itself, or with the long-range transport of fire aerosols and/or dust from other territories to the Moscow region. The average daily PM2.5 concentration in the city and the suburb is lower than the MPC (35 µg/m3) all year round. The days corresponding to the introduced conditional background make up more than 91% over three years in the center of Moscow. Such a conditional background is formed by both natural and anthropogenic sources of aerosols, and not only of local, but also of remote origin. It implicitly takes into account the influence of meteorological conditions on sources and sinks of aerosols, as well as advective air mass transport of aerosol to and from the city. Seasonal variations in mass concentration of PM10, PM2.5 particles and individual chemical elements, as well as in the distribution of chemical elements by the size of aerosol particles in near-surface atmosphere under conditionally background pollution are analyzed. The emphasis is placed on the similarity and difference in the conditionally background near-surface aerosol for the city and the suburb in different seasons.

Online monitoring of carbonaceous aerosols in a northern Chinese city: Temporal variations, main drivers, and health risks

This study examined the variability and source of carbonaceous aerosols, encompassing total carbon (TC), organic carbon (OC), and secondary organic carbon (SOC) for the years 2019–2020, as well as equivalent black carbon (eBC) and equivalent ultraviolet BC (eUVBC) data spanning 2019–2022, in the context of a typical northern Chinese city: Yanzhou. Averaged concentrations of TC, OC, SOC, eBC, eUVBC, and the ratio (OC/elemental carbon (EC)) reached 11.1 ± 6.7, 8.9 ± 5.1, 3.9 ± 2.0, 3.1 ± 1.3, and 4.3 ± 2.4 μg/m3, and 5.0 ± 2.2, respectively. The concentrations of TC, OC, eBC, and eUVBC were higher in winter, followed by spring and autumn, and summer, while SOC presented the opposite seasonal patterns. The diurnal variations of TC, OC, eBC, and eUVBC exhibited a bimodal pattern with peaks in the early morning (08:00–09:00 LT) and late evening (00:00–01:00 LT) and a trough in the afternoon (14:00–16:00 LT), pointing to vehicular emission and meteorological dispersion as major drivers of the hourly variability. The results obtained from the EC tracer method and minimum R squared (MRS) revealed that r(SOC/OC) were highest in summer (60%) and lowest in winter (26%), showing a fast summer photochemical oxidation of volatile organic compounds (VOCs) that generate SOC. In this study, the influence of meteorological conditions on the weighting of diverse carbonaceous aerosols was quantified using a machine learning method. Results showed that the main drivers of carbonaceous aerosols were height of the planetary boundary layer (HPBL), ambient temperature (AT), relative humidity (RH), and atmospheric pressure (AP) in all seasons. Additionally, the potential health risks of eBC based on the equivalent passive smoking of cigarettes (PSC) suggested that there was a certain level of human health risk in this city. The obtained results will provide more in-depth and comprehensive understanding of carbonaceous aerosol pollution and management strategies.

АГРОКЛИМАТИЧЕСКИЕ ХАРАКТЕРИСТИКИ ЗЕМЕЛЬ СЕЛЬСКОХОЗЯЙСТВЕННОГО НАЗНАЧЕНИЯ НА ТЕРРИТОРИИ СИБИРСКОГО ФЕДЕРАЛЬНОГО ОКРУГА В УСЛОВИЯХ ИЗМЕНЕНИЯ КЛИМАТА

The analysis of the agroclimatic resources of the Siberian Federal District in the warming climate has revealed statistically significant (most pronounced in the last two decades) increasing trends in the sum of active temperatures and the growing-season length due to the earlier (by 5-7 days) dates of the stable 5°C crossing in spring and the later ones (by 2-4 days) in autumn. In most of the territory, the changes in total precipitation are statistically insignificant, except for a few stations. The frequency of late and early frosts has not changed and remained within the climatological normal. The maximum growth of green biomass, which was estimated by the NDVI (Normalized Difference Vegetation Index) values, is facilitated by an early beginning of the growing season and a rapid transition to the period of active vegetation. The influence of meteorological conditions on the growth is significant at the beginning of the growing season and is quickly leveled by mid-summer.

ОЦЕНКА РАДИАЦИОННОЙ ОБСТАНОВКИ С ВОЗДУХА ПРИ НЕИЗВЕСТНОМ ИЗОТОПНОМ СОСТАВЕ ЗАГРЯЗНЕНИЯ И ИЗМЕНЯЮЩИХСЯ МЕТЕОРОЛОГИЧЕСКИХ ПАРАМЕТРАХ

The influence of meteorological conditions and the initial energy of gamma quanta on the results of the aerial radiation reconnaissance of a radioactively contaminated area is considered. The statistical distribution of atmospheric pressure, air temperature, and air moisture content is analyzed. Based on the results of mathematical simulations, the values of the coefficients inversely proportional to a decrease in the gamma radiation absorbed dose rate caused by the air layer between a flight level and an analyzed point on the ground were calculated for the aviation survey of a radioactively contaminated area with an initial energy of gamma quanta ranging from 50 to 3000 keV and an air density ranging from 0.9 to 1.7 kg/m³. It is shown that it is necessary to take into account the meteorological parameters and the energy composition of gamma radiation when performing an aerial radiation survey of an area.

A marked interannual variability of haze linked to particulate sources and meteorological conditions in Tehran (Iran), 1990–2020

This research assessed the spatio-temporal changes of haze pollution (NHAZEs) and its relationship with levels of gaseous pollutants and meteorological conditions over Tehran metropolis (Iran) for 1990–2020. The results showed a significant decreasing trend of NHAZEs annually and in winter, along with a significant increasing trend in the horizontal visibility. However, a marked interannual variability linked to changes in PM2.5 concentrations and the influence of meteorological conditions was detected, which explained 65% and 30% of the NHAZEs variances, respectively. We found that the increasing trend of wind speed annually and in winter is the principal driver behind the decrease in NHAZEs and the increase in visibility; as winds control the movement and dispersion of air pollution. In relation to gaseous pollutants, a case study showed that the highest concentrations of PM2.5 and NHAZEs were recorded under high levels of SO2, CO, and NO2, and low levels of O3, which mainly occurred under stable anticyclonic circulations. Spatially, the NHAZEs mostly affected the western, southwestern, central, and some parts of the northern of Tehran metropolis, because of the location of industries, traffic, and lack of green areas.

Atmospheric mercury in a developed region of eastern China: Interannual variation and gas-particle partitioning

Atmospheric mercury plays a crucial role in the biogeochemical cycle of mercury. This study conducted an intensive measurement of atmospheric mercury from 2015 to 2018 at a regional site in eastern China. During this period, the concentration of particle-bound mercury (PBM) decreased by 13%, which was much lower than those of gaseous elemenral mercury (GEM, 30%) and reactive gaseous mercury (GOM, 62%). The gradual decrease in the correlation between PBM and CO, K, and Pb indicates that the influence of primary emissions on PBM concentration was weakening. Moreover, the value of the partitioning coefficient (Kp) increased gradually from 0.05 ± 0.076 m3/μg in 2015 to 0.16 ± 0.37 m3/μg in 2018, indicating that GOM was increasingly inclined to adsorb onto particulate matter. Excluding the influence of meteorological conditions and the primary emissions, the change in aerosol composition is designated as the main trigger factor for the increasing gas-particle partitioning of reactive mercury (RM). The increasing ratio of Cl−, NO3−, and organics (Org) in the chemical composition of particle matters (PM2.5), as well as the decrease in the proportion of SO42−, NH4+, and K+, are conducive to the adsorption of GOM onto particles, forming PBM, which led to an increase of Kp and a lag of PBM reduction compared to GEM and GOM under the continuous control measures of anthropogenic mercury emissions. The evolution of aerosol compositions in recent years affects the migration and transformation of atmospheric mercury, which in turn can affect the biogeochemical cycle of mercury.

Influence of meteorological conditions on grain yield in Northern Kazakhstan

Influence of meteorological conditions on grain yield in Northern Kazakhstan

Numerical Investigation on the influence of meteorological conditions on wind turbine icing

Keeping the blade structure and shape in the design condition is the premise and guarantee for efficient operation of wing turbine, but wet and cold climate will lead to icing blade, then the blade shapes were changed and the output power of the wind turbine is reduced. A numerical frame for wind turbine icing was developed in present work, which consists of a numerical method for impingement of water droplets based on the multiple frames of reference and Euler method and a 3D heat and mass transferring method for wind turbine icing, then the effects of several meteorological parameters, including the temperature, Liquid Water Content (LWC), Mean Volume Diameter (MVD) and the inflow velocity, on ice shape of the blade were investigated in the numerical frame. The present method can provide supports for prediction of icing process and aerodynamic characteristic after icing of wind turbine blades.

Sequential Interaction of Biogenic Volatile Organic Compounds and SOAs in Urban Forests Revealed Using Toeplitz Inverse Covariance-Based Clustering and Causal Inference

Urban forests play a crucial role in both emitting and absorbing atmospheric pollutants. Understanding the ecological processes of biogenic volatile organic compounds (BVOCs) and secondary organic aerosols (SOAs) and their interactions in urban forests can help to assess how they influence air quality. Additionally, exploring the adaptation and feedback mechanisms between urban forests and their surrounding environments can identify new pollutants and potential risks in urban forests. However, the relationship between BVOC emissions and SOA formation is complex due to the influence of meteorological conditions, photochemical reactions, and other factors. This complexity makes it challenging to accurately describe this relationship. In this study, we used time-of-flight mass spectrometry and aerosol particle size spectrometry to monitor concentrations of BVOCs and particulate matter with a diameter less than 1 µm (PM1; representing SOAs) at a frequency of 10–12 times per min in an urban forest near Shanghai. We then analyzed the temporal changes in concentrations of BVOCs, SOAs, and other chemical pollutants in different periods of the day by using subsequence clustering and causal inference methods. The results showed that after using this method for diurnal segmentation, PM1 prediction accuracy was improved by 26.77%–47.51%, and the interaction rules of BVOCs and SOAs had sequential interaction characteristics. During the day, BVOCs are an important source of SOAs and have a negative feedback relationship with O3. From night to early morning, BVOCs have a positive, balanced relationship with O3, SOAs are affected by wind speed or deposition, BVOCs have no obvious relationship with O3, and SOAs are affected by temperature or humidity. This study is the first to apply Toeplitz inverse covariance-based clustering and causal inference methods for the high-frequency monitoring of BVOCs and SOAs, revealing the temporal effects and characteristics of BVOCs and SOAs and providing a scientific basis and new methods for understanding the dynamic effects of urban forest communities on the environment.

Predict the effect of meteorological factors on haze using BP neural network

Rapid urbanization and economic growth in China have resulted in severe haze. PM2.5 is a key component of haze. Using machine learning to predict PM2.5 concentrations has become a research hotspot. There are few previous studies on the correlation between PM2.5 and meteorological factors, and the accuracy of other detection results is not high. This study investigates the correlation between six meteorological factors (temperature, dew point, humidity, barometric pressure, wind speed, and visibility) and hourly PM2.5 concentration in Beijing. Pearson correlation analysis was used to analyze and found that meteorological factors had a stable non-linear effect on the change in PM2.5 concentration. Additionally, a BP neural network model was employed to examine the influence of meteorological conditions on hazy weather, considering both univariate and multifactorial aspects. Experimental results reveal a strong correlation between PM2.5 concentration and the four seasons, exhibiting minimal deviation and varying prediction errors throughout the seasons, confirming the seasonal association with haze weather. Consequently, the utilization of the BP neural network model for air quality index prediction is considered feasible. This research serves as a valuable reference for understanding the formation mechanism, implementing control methods, and establishing a haze prediction system to effectively manage urban air quality.