Robust data-driven approach evolution for multi-factor driving effect understanding of nutrient loading variations at reservoir-basin scale.

Macroscale spatiotemporal variability of Beryllium-7 and Lead-212 in surface air driven by meteorological factors.

Integrated assessment of fibrous vs. non-fibrous microplastic deposition patterns in subtropical urban atmospheres: From morphotypes to risk vectors.

The impact of certain meteorological phenomena may have a limiting effect on tourist activity. Temporal in situ measurements are valuable for understanding local climate characteristics when publicly available data provides generalized information at a large spatial scale. The study was conducted to determine and analyze the main weather extreme events that may disrupt tourism activity, as well as assess their spatial pattern across the Charyn State National Natural Park. The results of an assessment of climate-related limiting factors for the tourism sector were detected based on monitoring data collected by temporarily installed automatic meteorological stations. Archival data from the National Meteorological Service (Kazhydromet), along with remote sensing data, were also used to complement the analysis. The current research holds value by enhancing the general understanding of regional vulnerability disparities. The objective of the climatic impact-drivers concept is taken as a basis. In this context, the frequency of meteorological events was determined. Based on a weighted assessment of 11 meteorological parameters, considering their significance, an integrated hazard map was developed. The data indicates that the most vulnerable area is located along the Ulken Bugyty mountains and in the southern part of the Natural Park. The most frequently occurring event is very strong winds (24-29 m/s), particularly in the Ulken Bugyty mountains. A high recurrence of strong winds (15-23 m/s) is also observed on the “Valley of Castles” plateau, where the visitor center is located. The presence of the Ulken Bugyty mountains as an orographic barrier largely determines the spatial distribution of hazardous phenomena across the study area. The third decade of July is typically characterized by periods of 4 - 6 days with temperatures exceeding +35°C. In winter, fog is common in the northern part of the Charyn State National Natural Park. Conditions conducive to the formation of blizzards are rarely observed in the study area. The study also considers stakeholder parties in the tourism sector who are vulnerable to these hazardous phenomena.

This study investigated the association of meteorological parameters with the different periods of urban mobility related to COVID-19, on the PM10 concentrations in a small town in Southern Brazil. The particulate matter was monitored during a two-year period from 2019 to 2021, which covered the pre-lockdown, lockdown, and relaxation time phases. The meteorological data and PM10 concentrations, monitored using a high-volume air sampler installed in the centre of the town, were implemented in a Generalized Additive Model (GAM) as predictive air pollutant concentration variables. The results indicated that the wind speed and direction did not affect the variation in PM10 concentrations. However, the seasons showed a significant influence, with greater PM10 concentrations in the winter, due to worse pollutant dispersion conditions. There was a mean reduction of 40% in the PM10 concentrations during the lockdown period, corroborating the efficacy of mobility restrictions on air quality. The study concluded that although rainfall and relative humidity interfered significantly with particulate matter concentrations, social mobility and seasons were also preponderant factors with respect to air quality improvements. The study also emphasized the importance of monitoring air pollution in small towns (with no air quality monitoring stations), where the impact of air pollutants on public health can also be significant.

The transport of heavy metals (HMs) (excluding Hg) between soil and the atmosphere significantly influences human production and life. This review systematically summarizes the processes involved in the wind erosion-driven transport of HMs from soil to the atmosphere and the partitioning of atmospheric HMs via atmospheric deposition, drawing on relevant literature analysis and synthesis. The results reveal that both soil and the atmosphere are sinks of HMs, influencing each other significantly. The transport of soil HMs to the atmosphere along with soil fugitive dust by wind force incorporates three pathways: the direct suspension of suspension-size aggregates, the collision and abrasion of creep-size and saltation-size aggregates, and the breakage or decomposition of creep-size aggregates. Conventional farming practices, elevated HM concentrations, and high wind speeds exacerbate soil HM emissions. However, the effects of soil organic matter (SOM) and clay on soil HM emission demonstrate dual characteristics. Atmospheric deposition has emerged as a significant source of soil HMs, with wet deposition predominating, except in arid and semiarid regions. Dry deposition is influenced by meteorological parameters and topographic profiles, whereas preceding weather and precipitation duration are other factors affecting for wet deposition. This process increases the exposure possibility and consequent exposure dosage of HMs to humans and crops, thereby amplifying the potential risks of HMs. Moreover, the capacity of atmospheric HMs for long-range wind-driven dispersal may leave remote and sensitive ecosystems that are increasingly vulnerable. Moreover, it concludes with a synthesis of the current challenges and discusses recommendations for future directions. Therefore, this review will have significant reference and guiding value for research in this field and is intended for researchers engaged in the migration of HMs in soil and atmosphere, the safe utilization of heavy metal contaminated soil, and regional background values of soil HMs.

Abstract This study investigates the temporal variation and prediction of fine particulate matter (PM2.5) concentrations in Ho Chi Minh City (HCM City), a major Vietnamese metropolis with a tropical monsoon climate, from 2018 to 2023. The results indicated no statistically significant differences in the annual average PM2.5 concentrations throughout the study period, with values ranging from 21.7 to 26.5 μg/m³. However, concentrations were consistently higher during the dry season (November to April) (mean ± SD: 27.4 ± 7.3 μg/m³) compared to the rainy season (May to November) (mean ± SD: 21.5 ± 6.2 μg/m³). PM2.5 concentrations were strongly negatively correlated with meteorological parameters such as rainfall intensity, ambient air temperature, and wind speed, suggesting removal via wet deposition and enhanced dispersion under stronger winds and higher air temperatures. A bidirectional long short-term memory network with an attention mechanism (BiLSTM+Attention) was proposed to predict PM2.5 concentrations, incorporating auxiliary variables such as meteorological parameters and the leaf area index from the most recent preceding hours. The model’s best performance was achieved when including both auxiliary variables and PM2.5 concentrations from the previous 24 hours, yielding a coefficient of determination (R²) of 0.944, a mean absolute error of 2.142 μg/m³, and a root mean square error of 2.957 μg/m³. Multi-horizon forecasting was also conducted to evaluate the model's applicability, revealing a decline in prediction accuracy as the forecast horizon increased. SHAP (SHapley Additive exPlanations) was employed to evaluate the contribution of input variables to the model’s outputs, showing that PM2.5 concentrations from prior hours (e.g, less than 4 hours) were the most influential predictors. This study offers new insights into PM2.5 pollution in HCM City and highlights the potential of advanced deep learning techniques for air quality prediction in tropical monsoon urban environments.

Prolonged daytime presence and oxidative impact of nitryl chloride, ClNO2, in winter urban environment.

Ensuring a healthy indoor environment for children is crucial, as they are particularly vulnerable to environmental hazards. Indoor radon exposure is a significant concern due to its carcinogenic effects. This study assessed radon concentrations in primary schools and kindergartens in Kermanshah, Iran, estimated the annual effective dose, and evaluated potential health risks. This cross-sectional study randomly selected 24 primary schools and kindergartens across Kermanshah. Radon concentrations were measured seasonally using a radon meter (SARAD, RTM1688). Air samples were collected at breathing height and at least 40cm from walls. Building characteristics were recorded using a checklist, and meteorological parameters were measured during sampling. Statistical analysis was conducted using SPSS version 25. The average annual radon concentration was 37.1 ± 6.1Bq/m2 in kindergartens and 30.9 ± 6.5Bq/m2 in primary schools. The highest concentration was recorded in winter (39.4 ± 4.9Bq/m2) and the lowest in summer (30.6 ± 6.1Bq/m2). Radon concentrations correlated significantly with classroom size and relative humidity (p ≤ 0.01) but not with building age. Ground-floor rooms had higher concentrations, particularly in winter. Rooms with granite walls exhibited the highest radon concentrations (39.15Bq/m2), while those with plaster and paint had the lowest (28.5Bq/m2). The estimated annual effective lung dose was 0.28 mSv/y in kindergartens and 0.23 mSv/y in primary schools, both below the UNSCEAR recommended limit of 1.15 mSv/y. Indoor radon concentrations in Kermanshah's primary schools and kindergartens were below WHO and ICRP safety thresholds. However, as radon's effects build up over time, even low levels can contribute to a higher lifetime dose of radiation, increasing health risks over decades and the World Health Organization (WHO) recommend mitigation even at relatively low levels. Therefore, efforts should be made to increase public awareness, and policymakers must implement radon-resistant building codes while maintaining adequate ventilation systems. Further research is needed to identify influencing factors and develop mitigation strategies.

Air pollution in coastal urban environments is a complex interplay of emission sources and meteorological conditions, often inadequately captured by traditional horizontal monitoring. This study investigates the vertical distribution of major air pollutants PM2.5, PM10, SO₂, NO2, NO and CO across five high-rise multi-storey buildings in Rushikonda, Visakhapatnam, during summer and winter seasons. Over 30 days of continuous monitoring with a distinct vertical gradient, where noticeable variations were observed, particularly for particulate matter, with PM2.5 and PM10 concentrations decreasing by up to 10.2% and 15.4%, respectively, from ground to elevated levels. However, statistical data analysis and 3-D visualization of the relationship between the pollutants and the meteorological parameters revealed critical thresholds for temperature, relative humidity (RH), and height influencing pollutant stratification. 3D surface visualizations further emphasized RH's role in enhancing particulate concentrations via hygroscopic growth and suppressing vertical dispersion, besides the long-range transport of air mass could also contribute to the high concentration values of particulate matter. The findings highlight the utility of vertical monitoring using existing urban infrastructure and underscore its relevance in refining air quality management in coastal cities.

The article examines the history of opening first automatic meteorological stations (AMS) for the Arctic in the Soviet Union in the 1930s, which measured meteorological parameters and transmitted them by radio. The idea of opening the stations belongs to the aerologist P.A. Molchanov. He invented the world’s first radiosonde for studying the atmosphere and in 1927 patented its coceptual scheme. In 1928, he proposed using a similar method of transmitting weather data over a distance by radio in the design of a ground-based AMS. Prototypes of the Molchanov AMS system were manufactured and tested as part of the work of the 2nd International Polar Year in the Pamirs and the Tikhaya Bay polar station (Franz Josef Land) in 1933–1934 and were the first in the world. This demonstrated the fundamental possibility of such devices operating in high- altitude and Arctic conditions, despite major testing problems. In 1935–1937, an improved AMS prototype was put into trial operation at the Tiksi polar station. P.A. Molchanov also worked on the creation of a drifting AMS, although these plans could not be realized. To date it has been a practically unknown fact that automatic weather stations were developed in the design department of the Leningrad State Factory of Meteorological Instruments “Metpribor”. Several samples of stationary and parachute AMS were made there in 1934–1936. The documents preserved in the collection of the Arctic and Antarctic Research Institute (AARI) and the state archives of St. Petersburg, periodicals, research literature and a number of other sources made it possible to reconstruct the history of developing and improving the design of the first AMS in our country. Much of the information is provided for the first time.

A hybrid deep learning model for O3 forecasting and explaining in the Yangtze River Delta Region of China.

Occupancy, defined as the count of occupants, plays an important role in building design and operation stages. Obtaining reliable occupancy data for public buildings remains a challenging problem due to the lack of available on-site data. With the development of information technologies, the widespread use of smartphones and social networks provides a source for collecting building occupancy data. In this paper, we collect occupancy data of 56 public buildings from social networks. Based on this database, an interpretable occupancy model is proposed, incorporating the effects of trend, day types, months, meteorological parameters, and special events, such as the COVID-19 period, discount days, etc. The modeling process includes following four steps: (1) extracting typical occupancy data (TOD), (2) extracting key factors through the CatBoost model and SHAP method, (3) model fitting, and (4) model transfer application. The proposed method quantifies the influence of different factors on occupancy and can be applied to simulate occupancy in public buildings without on-site data. Its performance is evaluated through a case study on four public buildings in this paper.

Weather conditions, particularly temperature, exert a profound influence on the physiological performance and fruit quality of Kinnow mandarin. Variations in maximum and minimum temperatures significantly affect key physiological processes such as chlorophyll retention, leaf water balance, and metabolic activity, which in turn determine the accumulation of sugars, acids, and antioxidants in the fruit. However, the stage-specific impacts of these climatic factors across different phenological phases remain insufficiently understood. A two-year field experiment (2014–2015) was conducted on eight-year-old, drip-irrigated Kinnow mandarin trees under subtropical Himalayan foothill conditions. Meteorological parameters including maximum temperature (Tmax), and minimum temperature temperature (Tmin), relative humidity (RH), rainfall, and evaporation were correlated with physiological traits such as chlorophyll content, relative leaf water content, and leaf water concentration, as well as fruit quality parameters including total, reducing, and non-reducing sugars, acidity, ascorbic acid, total phenols, and flavonoids. Results revealed that Tmax, Tmin, and evaporation during the First fruit set to maximum fruit set stage (K3), and evaporation during the flowering to first fruit set (K2), positively influenced leaf water content in Kinnow mandarin. Furthermore, fruit quality parameters such as total sugars, reducing sugars, non-reducing sugars, and ascorbic acid were positively affected by Tmax and Tmin at the First fruit set to maximum fruit set stage (K3), and by Tmax and RH at the maximum fruit set to fruit harvest stage (K4) stage. In contrast, acidity, total phenols, and total flavonoids were positively influenced by rainfall and Tmin at the First fruit set to maximum fruit set stage (K3), and by rainfall at the maximum fruit set to fruit harvest stage (K4) stage. These findings indicate that specific climatic factors at particular developmental stages play a decisive role in regulating physiological performance and fruit quality, emphasizing the importance of stage-specific, weather-based orchard management to produce high-quality Kinnow mandarin fruits.

Spatial Trends and Anomalies in Aerosol Levels Over Indian Peninsular Region and Their Relationship with Regional Meteorological Parameters

Investigation on tritium (3H) washout process in a tropical region at Kakrapar Atomic Power Station (KAPS), Gujarat, India was carried out. 3H concentration in air as well as that in rainwater is estimated near KAPS Site having Pressurized Heavy Water Reactors (PHWRs) operational. Samples were collected covering the four rainy seasons from 2016 to 2019. The corresponding meteorological parameters of relative humidity, ambient temperature, wind speed, wind direction, and atmospheric stability were measured. The rain spectral characteristics such as raindrop diameter, fall velocity, liquid water content (LWC) in raindrops and average rain rate are also studied. Site-specific wet deposition rate (Bqm-2s-1) and washout coefficient for 3H (s-1) is observed to be in the range of 1.4E-05 to 4.8E-01 (Geometric Mean = 5.3E-03) and 1.1E-07 to 3.6E-02 (Geometric Mean = 4.1E-05) respectively. Significant and positive correlation was observed between average rain rate and washout coefficient (R2 = 0.73). Significant and negative correlation was observed between raindrop diameter of different rain events and 3H activity in rainwater (R2 = 0.70).

IntroductionNontuberculous mycobacteria (NTM) are opportunistic pathogens primarily found in soil and water. Previous studies have linked higher population density and tropical/subtropical climates with increased NTM pulmonary disease (NTM-PD). However, the effects of meteorological parameters and population density on NTM incidence in European countries remain unclear. This study aimed to assess whether climate conditions (temperature, humidity, wind speed, precipitation) and urbanisation influence the occurrence of NTM-PD.MethodsWe conducted a case–control study evaluating meteorological conditions (in the 12 months before the first NTM isolate) and urbanisation in the municipalities of residence of 1061 adults with NTM-PD enrolled in an Italian multicentre observational study (2017–2023), compared with a random sample of 10 000 adults from the Italian population.ResultsThe mean age of patients was 63.1 years, and 67.2% were female. Less densely populated areas were at lower risk for NTM-PD, even after adjusting for age, sex and meteorological parameters in a multivariable mixed-effects logistic regression model (OR 0.66 (95% CI: 0.56–0.78) and 0.68 (95% CI: 0.52–0.89) for semi-urban and rural areas, respectively, versus urban areas). Hotter climates were found to be at higher risk for NTM infection (OR 1.21 (95% CI: 1.10–1.33) for each °C increase in mean annual temperature). More ventilated climates with higher precipitation were at lower risk for NTM infection (OR 0.90 (95% CI: 0.86–0.95) for each km·h−1 increase in mean annual wind speed; OR 0.99 (95% CI: 0.99–0.99) for each 10 mm increase in mean annual precipitation).ConclusionsClimate change, particularly global warming and urbanisation, may increase NTM infections in regions with Mediterranean and continental climates.

In a bus station microenvironment, idling buses have the potential to pollute the air, which could have negative health effects on commuters who usually wait a long time. This study aimed to model ambient air pollution in selected three bus station micro-environments in Enugu metropolis with a view to determining the safe distance for commuters in a bus stations. Real time measurements of ambient particulates (PM10 and PM2.5) and gaseous pollutants (SO2, NO2 and CO) were monitored, in conjunction with meteorological parameters (wind speed, temperature and relative humidity) on idling buses in selected three bus stations and a control station in Enugu Metropolis. The study covered both dry and wet seasons from September 2016 to August 2018. Hand- held Aeroqual Series - 200 gas monitor/sensors, particulates laser meter and ambient weather anemometer were used to collect data on gases, particulates and weather respectively. Results indicated that the monthly highest mean concentration of pollutants were as follows: PM10=216.02µg/m3 – ABC; PM2.5=63.98µg/m3 – Young-Shall-Grow; SO2=0.6601ppm – Ifesinachi; NO2= 0.7732ppm – Young-Shall-Grow; CO= 3.9583ppm – Ifesinachi, consistently maintained higher concentrations than those at the control station, implying that commuters are highly at risk in bus stations. Regression analysis was used to derive models. The simple regression models were more robust in predicting concentration levels. Most models are designed for large-scale and medium-scale settings, but the simple Predictive Micro-scale Regression Models that have been developed by this study can help authorities, especially development control department of ministries in approval processes. This study can also assist the relevant authorities to set strict ambient air quality objectives for pollutants in transport micro environments through adherence to regulated safe distance proposed by this study.

Abstract. Success of the Paris Agreement relies on rapid reductions in fossil fuel CO2 (ffCO2) emissions. Atmospheric data can verify the ffCO2 reductions pledged by nations in their nationally determined contributions. However, estimating ffCO2 from atmospheric CO2 is challenging due to natural fluxes and varying backgrounds. One approach is to combine with nitrogen oxides (NOx = NO + NO2), which are co-emitted with CO2 during combustion. A key challenge in using NOx to estimate ffCO2 is the computational cost of modelling atmospheric photochemistry. Additionally, the NO2 : NO column ratio must be well understood to convert model NOx columns to NO2 columns for comparison with satellite data. We use random forest regression to parameterise NOx chemistry, relying only on meteorological parameters and NOx concentration. The regression is trained on outputs from a nested GEOS (Goddard Earth Observing System)-Chem model simulation for mainland Europe in 2019. We develop a monthly NOx chemistry parameterisation that performs well when tested on perturbed emission runs (R2 > 0.95) and on unseen meteorology for 2021 (R2 > 0.79). We also parameterise the NO2 : NO ratio (R2 > 0.99 on perturbed outputs, R2 > 0.92 on unseen meteorology). Additionally, we present an alternative method to predict NOx rates by scaling baseline NOx rates with changes in NOx concentration (R2 = 1.0 on perturbed outputs). Our models reproduce NO2 columns with minimal deviation from full-chemistry models, with reconstruction error smaller than the TROPOspheric Monitoring Instrument (TROPOMI) precision in over 99.9 % of cases, supporting robust ffCO2 inversion efforts. These results provide a robust framework for accurately estimating fossil fuel CO2 emissions from atmospheric data, enabling more reliable monitoring and verification of global emissions reductions.

Artificial intelligence (AI) plays an important role in analyzing air quality, providing new insights that enable informed environmental policy decisions at the local level based on air pollution modeling and forecasting. The aim of this study is to analyze various hybrid AI methods to predict, model, and anticipate hourly ground-level ozone concentrations. Ground-level ozone concentrations impact human health and the environment. The data used in this study was downloaded from the website of the Romanian Agency for Environmental Protection and spans five years (2020–2024). The dataset comprises two categories of data: (i) seven meteorological parameters, including temperature (T), relative humidity, precipitation, air pressure, solar brightness, wind direction, and velocity; (ii) twenty air pollutants, including two types of particulate matter, carbon monoxide, sulfur dioxide, ground-level ozone, three types of nitrogen oxide, ammonia, six volatile organic compounds, and five toxic elements. The study follows a six-stage approach: (1) data preprocessing is conducted to identify and address anomalies, outliers, and missing values, while ozone trends are analyzed; (2) correlations between ozone concentrations and other variables are examined, considering only non-missing values; (3) data splitting is carried out in training and testing sets; (4) a total of 27 hybrid AI-based algorithms are applied to determine the optimal predictive model for ozone concentration based on related variables; (5) fifty feature selection methods are applied to find the most relevant predictors for predicting ozone concentration; (6) a novel deep NARMAX model is employed to model and anticipate hourly ozone levels in Craiova. Using a set of statistical metrics, the results of the models are assessed. This research provides a novel perspective on the robustness of the predictive performance of the proposed model.