Influence Of Meteorological Factors Research Articles

Influence of meteorological factors on intraocular pressure variability using a large-scale cohort

The effects of meteorological conditions on IOP using a large-scale health examination cohort were investigated. There were a total of 811,854 measurements from 126,630 eyes of 63,839 subjects in 9 years from a health checkup cohort followed up annually for age, sex, body height, body mass index (BMI), systolic blood pressure (SBP), diastolic blood pressure (DBP), and IOP. The effects of these variables and the meteorological data of daily average temperature (TP), daily average local atmospheric pressure (AP), daily average volumetric humidity (VH), and daily amount of rainfall (RF) on the day of IOP measurement on IOP were investigated. Several variables were significantly associated with IOP, including sex, age, body height, BMI, SBP, DBP, average TP, average AP, average VH, RF, white blood cell count, red blood cell count, hemoglobin, aspartate aminotransferase, alanine aminotransferase, guanosine triphosphate, calcium, and HbA1c. This study indicated a correlation between meteorological factors and IOP. Higher AP and RF were associated with elevated IOP, whereas higher TP and VH were associated with decreased IOP.

Spatiotemporal Characterization and Driving Factors of Fine Particulate Matter and Its Chemical Components in the Huaihe River Basin

According to the data sets of fine particulate matter （PM2.5） and its components in 35 cities in the Huaihe River Basin from 2015 to 2021, the temporal and spatial distribution patterns of pollutants were analyzed. The influence of meteorological factors on PM2.5 concentrations was examined using a random forest model. The original series of PM2.5, sulfate （SO42-）, nitrate （NO3-）, ammonium salt （NH4+）, organic matter （OM）, and black carbon （BC） were rebuilt using KZ （Kolmogorov-Zurbenko） filtering and multiple linear regression （MLR） to quantify the effects of meteorological conditions. The results demonstrated that from 2015 to 2021, the declining rates of PM2.5, SO42-, NO3-, NH4+, OM, and BC in the Huaihe River Basin were 4.71, 0.99, 1.05, 0.77, 1.01, and 0.19 μg·（m3·a）-1, respectively. The high mass concentrations of PM2.5 and its components were concentrated in the central and western regions of the HRB, whereas those in coastal and southern cities were lower. The variance contributions of the short-term, seasonal, and long-term components of PM2.5 to the original PM2.5 sequences in 35 cities were 51.6%, 35.9%, and 7.0%, respectively. The PM2.5 in coastal cities were more affected by the short-term components. The meteorological conditions were unfavorable for PM2.5 reduction in the HRB from 2015 to 2018, whereas the meteorological conditions supported the PM2.5 decrease from 2019 to 2021. From 2015 to 2021, the contribution rates of meteorological conditions to the long-term component reductions of PM2.5, SO42-, NO3-, NH4+, OM, and BC were 28.3%, 29.1%, 31.0%, 29.3%, 27.8%, and 28.6%, respectively. The contribution rates of meteorological conditions to the long-term PM2.5 reduction were 43.4%, 25.6%, 25.5%, and 20.6% in the HRB cities in Anhui, Shandong, Jiangsu, and Henan Provinces, respectively. With the decrease in PM2.5 concentration in the HRB, the sulfur oxidation rate （SOR） increased significantly, while the nitrogen oxide oxidation rate （NOR） changed little.

Wind Power Curve Model Combining Smoothed Spline with First-Order Moments and Density-Adjusted Wind Speed Strategy

Given the variety of potential application scenarios, it is crucial to develop wind power curves that are more accurate, smoother, efficient, and applicable across a wider range of contexts. To this end, a wind power curve model combining smoothed spline with first-order moments (FOM) and density-adjusted wind speed (DAWS) strategy is proposed in this paper. First, the DAWS strategy is employed to consider the influence of meteorological factors on the wind power curve by adjusting wind speed. This strategy reduces the root mean square error (RMSE) by 5.81%-6.17% and the mean absolute error (MAE) by 5.84%-6.44% without adding complexity to the model. Secondly, FOM is proposed as a substitute for the original data during the modeling process. This approach reduces the number of operations on the similar data, resulting in a reduction of modeling time by 48.11%-99.89%. Furthermore, the impact on model accuracy is minimal. Finally, a the wind power curve model based on smooth spline is constructed, which exhibits superior smoothness, a broader range of generalizability, enhanced model accuracy, and reductions in RMSE by 5.67%-23.87% and MAE by 4.79%-22.35% in comparison to the control method.

Analisis Pengaruh Faktor Meteorologi Terhadap Konsentrasi Gas SO2 dan NO2 dari Cerobong Ketel Pabrik Gula X

The process of burning bagasse in a steam boiler produces pollutants in the form of exhaust gas emissions through the chimney, which can pollute air quality and harm health. The aim of this study is to determine the great influence of meteorological factors including exhaust gas temperature, air temperature, and flow speed in 4 areas of the boiler pipe that can affect the load of SO2 and NO2 gas pollutants through statistical data analysis. The ANOVA test used in this study was the ANOVA One Way test. The results of the study showed that there was a significant influence of the air temperature factor on SO2 in Boiler 4 / Cheng Chen with a value of F value 14,553 and a sig value of 0,032, as well as the influence that exists on the exhaust gas temperature factor and air temperature on NO2 in Stork Boiler 2 with an F value of 15,358 ; 17,889 and a sig value of 0.030 ; 0.024. The results of the analysis can be used as monitoring to evaluate the success rate and operational efficiency of the boiler under the management that has been carried out.

Spatiotemporal evolution of dry and wet and quantitative analysis of the influence of meteorological factors based on MI and the FAO P–M model

The frequent occurrence of extreme climate events disrupts the regional water budget balance and leads to changes in the dry and wet conditions of the surface, making the water surplus and deficit more complex and variable. To explore the quantitative relationship between the spatiotemporal evolution of dry and wet conditions and meteorological factors in the Hexi Corridor under changing environmental conditions, the relative moisture index (MI) and FAO Penman–Monteith (FAO P–M) model were combined to construct a partial differential quantitative attribution model for dry and wet variations affected by climate factors in the Hexi Corridor. The results show that: (1) MI in the Hexi Corridor increased significantly (Z = 2.341) during 1960–2019, showing a wet-trend change, and the degree of drought increased from southeast to northwest in the Hexi Corridor. (2) The order of drought degree in four seasons is as follows: winter (− 0.95), spring (− 0.93), autumn (− 0.89) and summer (− 0.83). (3) The frequency of extreme drought, severe drought, moderate drought, and mild drought within 60 years of 21 meteorological stations accounted for 28.38%, 50.48%, 8.85%, and 7.38%, respectively, and the frequency above severe drought was the highest. (4) The sensitivity of meteorological factors gradually increased from northwest to southeast, and MI was the most sensitive to the change of precipitation (P), followed by net radiation (Rn), wind speed (u2), mean temperature (Tmean), relative humidity (RH) and maximum temperature (Tmax). MI was the least sensitive to the change of minimum temperature (Tmin). P is the most important meteorological variable that contributes to the increase of MI, followed by u2, Tmean, and Tmin. Rn, Tmax, and RH have the least influence, and the total contribution of the seven meteorological factors is 85.59%. Compared with the reference evapotranspiration, P is the main factor affecting the dry and wet variations in Hexi Corridor.

The Reconstruction of FY-4A and FY-4B Cloudless Top-of-Atmosphere Radiation and Full-Coverage Particulate Matter Products Reveals the Influence of Meteorological Factors in Pollution Events

The Reconstruction of FY-4A and FY-4B Cloudless Top-of-Atmosphere Radiation and Full-Coverage Particulate Matter Products Reveals the Influence of Meteorological Factors in Pollution Events

Prediction of PM10 Concentration in Dry Bulk Ports Using a Combined Deep Learning Model Considering Feature Meteorological Factors

Accurate prediction of PM10 concentration is important for effectively managing PM10 exposure and mitigating health and economic risks posed to humans in dry bulk ports. However, accurately capturing the time-series nonlinear variation characteristics of PM10 concentration is challenging owing to the specific intensity of port operation activities and the influence of meteorological factors. To address such challenges, a novel combined deep learning model （CLAF） was proposed, merging cascaded convolutional neural networks （CNN）, long short-term memory （LSTM）, and an attention mechanism （AM）. This integrated model aimed to forecast hourly PM10 concentration in dry bulk ports. First, using the random forest characteristic importance algorithm, the distinct meteorological factors were identified among the selected five meteorological factors. These selected factors were incorporated into the prediction model along with the PM10 concentration. Subsequently, the CNN layer was employed to extract high-dimensional time-varying features from the input variables, while the LSTM layer captured sequential features and long-term dependencies. In the AM layer, different weights were assigned to the output components of the LSTM layer to amplify the effects of important information. Finally, three evaluation metrics were applied to compare the performance of the CLAF model with three basic models and three commonly used prediction models. Real-case data was collected and used in this study. Comparison results demonstrated that considering the meteorological factors could improve the prediction accuracy and fitting performance of PM10 concentration in ports. The CLAF model reduced the mean absolute error statistic by 13.92%-56.9%, decreased the mean square error statistic by 45.99%-81.02%, and improved the goodness-of-fit statistic by 3.2%-15.5%.

Dependence of Pinus sylvestris (Pinaceae) Radial Growth on Meteorological Conditions and Anthropogenic Air Pollution: Data from Northwestern Part of Murmansk Oblast.

The influence of meteorological factors and anthropogenic air pollution on the radial growth of the Scots pine Pinus sylvestris L. was studied as dependent on the distance from the Pechenganickel mining and metallurgical plant (Nikel, Murmansk region). Three (control, buffer, and impact) zones of the pollution gradient were identified based on the contents of main polluting elements (S, Ni, and Cu) in the forest litter. Asignificant weakening of pine stands was observed in the impact zone and attributed to the combined effect of long-term anthropogenic pollution of the 1970s and unfavorable weather events of the mid-1980s. As the emission decreased from 1988 to 2018, the radial increment of P. sylvestris was observed to increase significantly (by up to 44%) in the impact zone and to remain much the same in the control and buffer zones. More recently, the radial increment of trees in the impact zone reached and even exceeded the values observed in the control zone, although the trees examined were relatively old. The finding demonstrated again the high adaptive capacity of P. sylvestris.

Multi-time Scale Variation of Atmospheric Ammonia Concentration and Dry Deposition in a Paddy Rice Region in Subtropical China

Meteorological factors and anthropogenic activities significantly affect atmospheric ammonia （NH3） concentration and its dry deposition. Former studies have examined the spatial and temporal variability in atmospheric NH3 concentrations at monthly scales. However, the characteristics of atmospheric concentrations at finer time scales such as hourly and daily scales and the influencing factors remain unclear. In this study, atmospheric NH3 concentration and related meteorological factors were continuously monitored online for one year in a double cropping rice region in subtropical China, and atmospheric NH3 concentration and its meteorological influencing factors as well as dry deposition were analyzed at different time scales （hourly, daily, and monthly）. The main results were as follows： The annual average daily concentration of NH3 in the rice area varied from 0.01 to 58.0 μg·m-3 （in N, same below）, and the annual average concentration was 5.3 μg·m-3. On the hourly scale, the 24-hour dynamics of atmospheric NH3 concentration showed a unimodal pattern, and the time of the NH3 peak appearance in different seasons was different； the time of the peak that appeared in winter lagged behind that in the other seasons. From the perspective of daily scale, NH3 concentration was mainly affected by fertilization in the paddy fields, peaking at 1-3 days after fertilization and then gradually decreasing. On the monthly scale, NH3 concentration peaked at 12.8 μg·m-3 in July and was the lowest in October at 1.6 μg·m-3. On the hourly scale, NH3 concentration varied seasonally due to the influences of meteorological factors, mainly as follows： NH3 concentration showed significant positive correlations with air temperature and solar radiation in all four seasons and with wind speed in spring and summer, whereas it showed significant negative correlations with relative humidity except in winter. On the daily scale, NH3 concentration showed a significant positive correlation with air temperature, rainfall, and solar radiation, whereas it showed a significant negative correlation with relative humidity. On the monthly scale, no significant correlation existed between each meteorological factor and NH3 concentration. The annual dry deposition flux （in N） calculated from the hourly average NH3 concentration was 8.5 kg·（hm2·a）-1, which was 11.6% higher than the annual flux calculated from the daily average and 12.4% higher than the annual flux calculated from the monthly average. In summary, there were significant daily and seasonal variations in atmospheric NH3 concentration in the paddy rice region in subtropical China, and conducting hourly-scale observations of NH3 concentration can help to reveal the multi-time scale variations in NH3 concentration and to quantify NH3 dry deposition more accurately.

Influence of Weather Conditions and the Aphid Population on the Potato Virus Y Infection of Tobacco in the Field

Potato virus Y (PVY) is a major tobacco (Nicotiana tabacum L.) pathogen that causes severe crop losses. We studied the influence of meteorological factors and a population of twelve aphid species on the development of PVY in field-grown tobacco from 1996 to 2010 in Poland. Three PVY-susceptible tobacco varieties were used in the study. The mean virus incidence ranged from 18% in 2010 to almost 99% in 1996, 2004, and 2009. For determining the relationship between tobacco plant infection and meteorological conditions and aphid populations, logistic regression analysis was used. It was found that the probability of PVY infection is significantly dependent on the average air temperature, relative humidity, number of days with an average temperature of at least 25 °C, and the abundance of Aphis fabae and Brachycaudus helichrysi. The probability of infection of tobacco plants with potato virus Y decreased with increasing air temperature and relative humidity. In addition, with each subsequent day with a temperature of at least 25 °C, the risk of infection decreased by 24%. Furthermore, it was often observed that high populations of Aphis fabae and Brachycaudus helichrysi were associated with a high incidence of virus infection in tobacco plants.

МОНИТОРИНГ КАЧЕСТВА АТМОСФЕРНОГО ВОЗДУХА ЗАПАДНО-КАЗАХСТАНСКОГО РЕГИОНА: ПРИНЦИПЫ, МЕТОДЫ, ПОДХОДЫ

The main approaches and methods of studying the characteristics and conditions of atmospheric pollution on the example of Western Kazakhstan are considered. The classification and grouping of applied approaches by topics, methods, time intervals and other relevant criteria was conducted. An analysis of the available information on the sources and volumes of emissions into the atmosphere, as well as on the systems for monitoring the pollution of the air basin, was carried out. It is shown that to increase the effectiveness of the atmospheric air quality management system, it is expedient to use a complex approach taking into account the influence of meteorological factors and synoptic conditions that determine different levels of pollution. An analytical review of modern methods of modeling the spread of pollutants in atmospheric air showed the feasibility of using statistical methods integrated with deep machine learning and the Eulerian continuum model of turbulent diffusion. The obtained conclusions will allow further use of an integrated approach to improve the atmospheric air quality management system of the studied region.

Quantitative impacts of dominant large-scale circulation systems on surface ozone pollution in China

Tropospheric ozone pollution has been worsened over most regions of China, adversely affecting human health and ecosystems. The long-term ozone concentration depends largely upon atmospheric circulations. Here, we conducted meteorological adjustment to quantitatively assess the influences of meteorological factors on the ozone evolution in China's seven city clusters during the warm season (April to October) from 2013 to 2020. Our analysis indicated that northern and eastern regions experienced ozone increases driven by emission changes. Southern regions, particularly the Pearl River Delta (PRD), exhibited ozone rise primarily due to meteorological conditions despite emission changes. In the Sichuan Basin (SCB) and Central Yangtze River Plain (CYP), where ozone levels decreased, meteorological conditions played a significant role in suppressing the ascent of ozone. Empirical orthogonal functions (EOF) analyses suggested that the spatiotemporal trend of meteorology-associated ozone was strongly correlated with the variation of East Asian Trough (EAT), South Asian High (SAH) and the western Pacific subtropical high (WPSH). The top three EOF patterns explained 33.4 %, 21.8 %, and 16.0 % of the total variance in meteorology-associated ozone. Absolute principal component scores-multiple linear regression (APCS-MLR) analyse quantitatively identified that enhanced EAT and SAH with a northward location of WPSH were favourable to surface ozone formation in central and eastern regions, but unfavourable to ozone formation in edge regions such as SCB.

Observing and identifying fouled ballast bed: On-site testing with infrared thermography (IRT) and uncovering thermodynamic transfer mechanisms within the ballast bed

The ballast bed serves as the foundation of the ballasted track, and its performance is maintained through periodic ballast cleaning. Early detection of fouled ballast bed significantly reduces maintenance workload and capital investment. Some scholars have studied the feasibility of utilizing infrared thermography (IRT) for detecting fouled ballast bed (DBF) and have made some progress. Existing studies have predominantly employed simulated boxes to simulate the ballast bed. To better reflect real-world conditions, this study established two sections of ballast bed on a newly constructed line: one with clean ballast and the other fouled, with a volumetric fouling rate (VFR) of 27.6 % (FI ≈ 21.5 %). Moreover, this paper takes a pivotal step in exploring the thermodynamic transfer mechanisms within the ballast bed, the influences of meteorological factors on the detection effectiveness of IRT, and other detection indicators that could be used for DBF.The results demonstrate that the different void fractions and composition substances of the clean and fouled ballast beds (CFB) contribute to their distinct thermodynamic properties. Furthermore, the high specific heat capacity of water exacerbates the thermodynamic property difference between the CFB. In terms of meteorological factors, both the solar radiation intensity (S) and air temperature (T) have a significant positive impact on the temperature of the ballasted structure (STT) and the temperature difference between the CFB (CF-S). Throughout the day, as the S and T increase, the ballast bed surface absorbs more solar heat than it loses, leading to an increase in its surface temperature. When it exceeds the soil temperature (S-S), heat is transferred downward. Since the poor heat conduction of the clean ballast bed, it has a higher surface temperature. As the S and T decrease, heat convection and conduction become dominant, leading to a decrease in the surface temperature of the ballast bed (BT-S). When the BT-S is lower than the S-S, heat is transferred upward, causing the surface temperature of the fouled ballast bed (F-S) to potentially exceed that of the clean ballast bed (C-S). Furthermore, the humidity (H) has a strong negative impact on the STT, while on sunny days following rain, it has a significantly positive impact on the CF-S. The effects of wind speed (W) on the STT and the CF-S are not prominently observed due to its low values during the experiment. Without considering rainfall, higher S and T, combined with reduced W, result in a greater CF-S and are more conducive to advancing fouling detection. Hence, the CF-S can reach up to about 3 °C on a sunny day and may even rise to about 5 °C after rainfall. Nonetheless, the CF-S is only around 0.71 °C on a cloudy day and 0.25 °C at night. Unexpectedly, there is a significant temperature difference between the sleeper and the ballast bed or the steel rail. These indicators could potentially be used for DBF on cloudy days. Overall, these findings demonstrate the feasibility of using IRT for DBF in the field, provding a broader theoretical support for its advancement and implementation.

The association between the scarlet fever and meteorological factors, air pollutants and their interactions in children in northwest China.

Scarlet fever (SF) is an acute respiratory transmitted disease that primarily affects children. The influence of meteorological factors and air pollutants on SF in children has been proved, but the relevant evidence in Northwest China is still lacking. Based on the weekly reported cases of SF in children in Lanzhou, northwest China, from 2014 to 2018, we used geographical detectors, distributed lag nonlinear models (DLNM), and bivariate response models to explore the influence of meteorological factors and air pollutants with SF. It was found that ozone (O3), carbon monoxide (CO), sulfur dioxide (SO2), temperature, pressure, water vapor pressure and wind speed were significantly correlated with SF based on geographical detectors. With the median as reference, the influence of high temperature, low pressure and high pressure on SF has a risk effect (relative risk (RR) > 1), and under extreme conditions, the dangerous effect was still significant. High O3 had the strongest effect at a 6-week delay, with an RR of 5.43 (95%CI: 1.74,16.96). The risk effect of high SO2 was strongest in the week of exposure, and the maximum risk effect was 1.37 (95%CI: 1.08,1.73). The interactions showed synergistic effects between high temperatures and O3, high pressure and high SO2, high nitrogen dioxide (NO2) and high particulate matter with diameter of less than 10μm (PM10), respectively. In conclusion, high temperature, pressure, high O3 and SO2 were the most important factors affecting the occurrence of SF in children, which will provide theoretical support for follow-up research and disease prevention policy formulation.

Revealing the Eco-Environmental Quality of the Yellow River Basin: Trends and Drivers

The Yellow River Basin (YB) acts as a key barrier to ecological security and is an important experimental region for high-quality development in China. There is a growing demand to assess the ecological status in order to promote the sustainable development of the YB. The eco-environmental quality (EEQ) of the YB was assessed at both the regional and provincial scales utilizing the remote sensing-based ecological index (RSEI) with Landsat images from 2000 to 2020. Then, the Theil–Sen (T-S) estimator and Mann–Kendall (M-K) test were utilized to evaluate its variation trend. Next, the optimal parameter-based geodetector (OPGD) model was used to examine the drivers influencing the EEQ in the YB. Finally, the geographically weighted regression (GWR) model was utilized to further explore the responses of the drivers to RSEI changes. The results suggest that (1) a lower RSEI value was found in the north, while a higher RSEI value was found in the south of the YB. Sichuan (SC) and Inner Mongolia (IM) had the highest and the lowest EEQ, respectively, among the YB provinces. (2) Throughout the research period, the EEQ of the YB improved, whereas it deteriorated in both Henan (HA) and Shandong (SD) provinces. (3) The soil-available water content (AWC), annual precipitation (PRE), and distance from impervious surfaces (IMD) were the main factors affecting the spatial differentiation of RSEI in the YB. (4) The influence of meteorological factors (PRE and TMP) on RSEI changes was greater than that of IMD, and the influence of IMD on RSEI changes showed a significant increasing trend. The research results provide valuable information for application in local ecological construction and regional development planning.

Natural Factors of Microplastics Distribution and Migration in Water: A Review

Microplastics are widely present worldwide and are of great concern to scientists and governments due to their toxicity and ability to serve as carriers of other environmental pollutants. The abundance of microplastics in different water bodies varied significantly, mainly attributed to the initial emission concentration of pollutants and the migration ability of pollutants. The migration process of microplastics determines the abundance, fate, and bioavailability of microplastics in water. Previous studies have proved that the physicochemical properties of water bodies and the properties of microplastics themselves are important factors affecting their migration, but the change in external environmental conditions is also one of the main factors controlling the migration of microplastics. In this paper, we focus on the effects of meteorological factors (rainfall, light, and wind) on the distribution and migration of microplastics and conclude that the influence of meteorological factors on microplastics mainly affects the inflow abundance of microplastics, the physical and chemical properties of water, and the dynamics of water. At the same time, we briefly summarized the effects of aquatic organisms, water substrates, and water topography on microplastics. It is believed that aquatic organisms can affect the physical and chemical properties of microplastics through the physical adsorption and in vivo transmission of aquatic plants, through the feeding behavior, swimming, and metabolism of animals, and through the extracellular polymers formed by microorganisms, and can change their original environmental processes in water bodies. A full understanding of the influence and mechanism of external environmental factors on the migration of microplastics is of great theoretical significance for understanding the migration law of microplastics in water and comprehensively assessing the pollution load and safety risk of microplastics in water.

An Evaluation of the Influence of Meteorological Factors and a Pollutant Emission Inventory on PM2.5 Prediction in the Beijing–Tianjin–Hebei Region Based on a Deep Learning Method

In this study, a Long Short-Term Memory (LSTM) network approach is employed to evaluate the prediction performance of PM2.5 in the Beijing–Tianjin–Hebei region (BTH). The proposed method is evaluated using the hourly air quality datasets from the China National Environmental Monitoring Center, European Center for Medium-range Weather Forecasts ERA5 (ECMWF-ERA5), and Multi-resolution Emission Inventory for China (MEIC) for the years 2016 and 2017. The predicted PM2.5 concentrations demonstrate a strong correlation with the observed values (R2 = 0.871–0.940) in the air quality dataset. Furthermore, the model exhibited the best performance in situations of heavy pollution (PM2.5 > 150 μg/m3) and during the winter season, with respective R2 values of 0.689 and 0.915. In addition, the influence of ECMWF-ERA5’s hourly meteorological factors was assessed, and the results revealed regional heterogeneity on a large scale. Further evaluation was conducted by analyzing the chemical components of the MEIC inventory on the prediction performance. We concluded that the same temporal profile may not be suitable for addressing emission inventories in a large area with a deep learning method.

Examining the relationship between atmospheric pollutants and meteorological factors in Asansol city, West Bengal, India, using statistical modelling.

Meteorological conditions significantly impact ambient air quality in urban environments. This study focuses on Asansol, known as the "Coal City" and the "Industrial Heart of West Bengal," a notable hotspot for air pollution. Despite its significance, limited research has addressed the influence of meteorological factors on key air pollutants in this urban area. From January 2019 to December 2023, this investigation explores the relationships between meteorological parameters (including atmospheric temperature, relative humidity, rainfall, wind speed) and the concentrations of crucial air pollutants (PM2.5, PM10, NO2, SO2). Temporal trends in air pollutant concentrations are also analysed. The Spearman correlation method is used to establish associations between pollutant concentrations and meteorological variables, while multiple linear regression (MLR) models are employed to assess meteorological factors and potential impact on pollutant concentrations. The analysis reveals a decreasing trend in pollutant concentrations in Asansol. Temperature exhibits negative correlations with all pollutants in all seasons except for a positive correlation during the monsoon. Rainfall consistently displays significant negative correlations with pollutants in all seasons. Relative humidity is negatively correlated with pollutants in all seasons, and wind speed, except during the post-monsoon season, shows negative correlations with all pollutants. Linear models excel in predicting particulate matter concentrations but perform poorly in predicting gaseous contaminants. Accounting for seasonal fluctuations and meteorological parameters, this research enhances the accuracy of air pollution forecasting, contributing to a better understanding of air quality dynamics in Asansol and similar urban areas.

The effects of meteorological factors and air pollutants on the incidence of tuberculosis in people living with HIV/AIDS in subtropical Guangxi, China.

Previous studies have shown the association between tuberculosis (TB) and meteorological factors/air pollutants. However, little information is available for people living with HIV/AIDS (PLWHA), who are highly susceptible to TB. Data regarding TB cases in PLWHA from 2014 to2020 were collected from the HIV antiviral therapy cohort in Guangxi, China. Meteorological and air pollutants data for the same period were obtained from the China Meteorological Science Data Sharing Service Network and Department of Ecology and Environment of Guangxi. A distribution lag non-linear model (DLNM) was used to evaluate the effects of meteorological factors and air pollutant exposure on the risk of TB in PLWHA. A total of 2087 new or re-active TB cases were collected, which had a significant seasonal and periodic distribution. Compared with the median values, the maximum cumulative relative risk (RR) for TB in PLWHA was 0.663 (95% confidence interval [CI]: 0.507-0.866, lag 4 weeks) for a 5-unit increase in temperature, and 1.478 (95% CI: 1.116-1.957, lag 4 weeks) for a 2-unit increase in precipitation. However, neither wind speed nor PM10 had a significant cumulative lag effect. Extreme analysis demonstrated that the hot effect (RR = 0.638, 95%CI: 0.425-0.958, lag 4 weeks), the rainy effect (RR = 0.285, 95%CI: 0.135-0.599, lag 4 weeks), and the rainless effect (RR = 0.552, 95%CI: 0.322-0.947, lag 4 weeks) reduced the risk of TB. Furthermore, in the CD4(+) T cells < 200 cells/µL subgroup, temperature, precipitation, and PM10 had a significant hysteretic effect on TB incidence, while temperature and precipitation had a significant cumulative lag effect. However, these effects were not observed in the CD4(+) T cells ≥ 200 cells/µL subgroup. For PLWHA in subtropical Guangxi, temperature and precipitation had a significant cumulative effect on TB incidence among PLWHA, while air pollutants had little effect. Moreover, the influence of meteorological factors on the incidence of TB also depends on the immune status of PLWHA.

Spatiotemporal Variation, Meteorological Driving Factors, and Statistical Models Study of Lake Surface Area in the Yellow River Basin

The surface area changes of 151 natural lakes over 37 months in the Yellow River Basin, based on remote sensing data and 21 meteorological indicators, employing spatial distribution feature analysis, principal component analysis (PCA), correlation analysis, and multiple regression analysis, identify key meteorological factors influencing these variations and their interrelationships. During the study period, lake area averages were from 0.009 km2 to 506.497 km2, with standard deviations ranging from 0.003 km2 to 184.372 km2. The coefficient of variation spans from 3.043 to 217.436, indicating considerable variability in lake area stability. Six primary meteorological factors were determined to have a significant impact on lake surface area fluctuations: 24 h precipitation, maximum daily precipitation, hours of sunshine, maximum wind speed, minimum relative humidity, and lakes in the source region of the Yellow River generally showed a significant positive correlation. For maximum wind speed (m/s), 28 lakes showed significant correlations, with five positive and twenty-three negative correlations, correlation coefficients ranging from −0.34 to −0.63, average −0.47, indicating an overall negative correlation between lake surface area and maximum wind speed. For maximum daily precipitation (mm), 36 lakes had 21 showing a positive correlation, indicating a positive correlation between lake surface area and daily precipitation in larger lakes. Furthermore, of the 117 lakes with sufficient data to model, the predictive capabilities of various models for lake surface area changes showcased distinct advantages, with the random forest model outperforming others in a dataset of 65 lakes, Ridge regression is best for 28 lakes, Lasso regression performs best for 20 lakes, Linear model is only best for 4 cases. The random forest model provides the best fit due to its ability to handle a large number of feature variables and consider their interactions, thereby offering the best fitting effect. These insights are crucial for understanding the influence of meteorological factors on lake surface area changes within the Yellow River Basin and are instrumental in developing predictive models based on meteorological data.