Meteorological factors and climate change impact on asthma: a systematic review of epidemiological evidence.

OBJECTIVE: This systematic review aimed to investigate the epidemiological data about meteorological factors and climate change (CC) impact on asthma. DATA SOURCES: A search was performed using three databases (Web of Science, Science Direct, and MEDLINE) for all relevant studies published from January 1, 2018, to December 31, 2022. STUDY SELECTIONS: This systematic review complied with the PRISMA document's requirements, including studies related to meteorological factors and CC impact on asthma. The search included studies published in English or French language, and was based on title, abstract, and complete text. Documents not meeting inclusion requirements were excluded. RESULTS: We identified 18 studies published in the last five years that were eligible for inclusion in this review. We found that these studies concerned European, Asian, American, and Oceanic cities. Extreme variations in temperature, humidity, wind speed, exceptional incidents like hurricanes, cold and heat waves, and seasonal shifts were strongly correlated with the worsening of asthmatic symptoms, particularly in childhood. In addition, excessive concentrations of air pollutants and aeroallergens were linked to pediatric asthma emergency hospital admissions. CONCLUSIONS: A significant association between the consequences of CC and asthma in adults particularly in children has been demonstrated. Future research should quantify the impact of global change in climate regarding the aeroallergens' distribution in terms of geography and time. It is also necessary to research the impact of air pollution on asthmatic health, like sulfur dioxide (SO2), nitrogen dioxide (NO2), ozone (O3), and particles having an aerodynamic diameter lower than 2.5 µm (PM2.5).

Comparative analysis of different Karnal bunt disease prediction models developed by machine learning techniques for Punjab conditions.

Timely prediction of pathogen is important key factor to reduce the quality and yield losses. Wheat is major crop in northern part of India. In Punjab, wheat face challenge by different diseases so the study was conducted for two locations viz. Ludhiana and Bathinda. The information regarding the occurrence of Karnal bunt in 12 consecutive crop seasons (from 2009-10 to 2020-21) in Ludhiana district and in 9 crop seasons (from 2010-11 to 2018-19) in Bathinda district, was collected from the Wheat Section of the Department of Plant Breeding and Genetics at Punjab Agricultural University (PAU), located in Ludhiana. The study aims to investigate the adequacy of various methods of machine learning for prediction of Karnal bunt using meteorological data for different time period viz. February, March, 15 February to 15 March and overall period obtained from Department of Climate Change and Agricultural Meteorology, PAU, Ludhiana. The most intriguing outcome is that for each period, different disease prediction models performed well. The random forest regression (RF) for February month, support vector regression (SVR) for March month, SVR and BLASSO for 15 February to 15 March period and random forest for overall period surpassed the performance than other models. The Taylor diagram was created to assess the effectiveness of intricate models by comparing various metrics such as root mean square error (RMSE), root relative square error (RRSE), correlation coefficient (r), relative mean absolute error (MAE), modified D-index, and modified NSE. It allows for a comprehensive evaluation of these models' performance.

Air pollution trend in Chandigarh during 2019-2022: status and influence of meteorological factors.

The degradation of ambient air quality is a pressing global concern, and India, as a developing nation, has witnessed a rapid surge in industrial activities in recent decades. This surge has resulted in numerous Indian cities ranking among the world's most polluted urban areas. Chandigarh, strategically positioned within the Indo-Gangetic plains (IGP), has not escaped this distressing trend, experiencing a significant spike in air pollution levels. This study focuses on comprehending and addressing the air quality issues in Chandigarh, shedding light on the evolution of air pollution trends and their dependence on meteorological factors. Notably, the study reveals that, with the exception of O3, pollutant concentrations surge during the rice stubble burning season. These pollutants persist in the atmosphere for prolonged periods, exacerbating the situation during winter due to lower temperatures and heightened use of fossil fuels for heating by low-income households. In contrast, the wheat stubble burning period does not significantly impact pollutant concentrations. The study also identifies a spring peak in surface O3 concentrations, attributed to favorable high temperatures that promote the photochemical reactions responsible for this phenomenon, a distinctive feature in South Asia and the Himalayas. An examination of the connection between pollutant concentrations and meteorological parameters underscores that elevated pollutant levels, except for CO, are linked to lower relative humidity and temperatures. This suggests that current development patterns have contributed to the escalation of air pollution in Chandigarh, necessitating urgent interventions to preserve the city's aesthetics and the health of its residents. Furthermore, to model and monitor pollutant behavior in Chandigarh, more extensive and extended studies are imperative. Both short-term and long-term investigations into the environmental and health impacts of air pollutants, including primary and secondary pollutants, are of paramount importance. These endeavors are essential for the well-being of both the environment and the population of Chandigarh.

Comparative measurement of CO2, CH4 and CO at two traffic interjunctions having inflated vehicular flow in Delhi.

Vehicular emissions are considered one of the major anthropogenic sources of greenhouse gases and poor air quality in metropolitan cities. This study aims to see the correlation of CO2, CH4, and CO through monitoring over a period from December 2020 to October 2021 covering three seasons' winter, summer, and monsoon at two different traffic locations of Delhi having different traffic volumes, road patterns, and traffic management. The annual average morning concentration of CO2, CH4 and CO was found (533 ± 105), (7.3 ± 3.1), (10.7 ± 3.0) ppm at Najafgarh and (480 ± 70), (5.2 ± 1.8), (7.8 ± 2.8) ppm at Rajendra Place, respectively. A relationship between concentration of all three gases and meteorological parameters such as temperature, humidity, wind speed and wind direction has also been investigated using Pearson correlation coefficient and pollution rose diagram. A comparable pattern in concentration was observed for all three gases in spatial (location) and temporal (diurnal) distribution. The concentration trend of CO2 in different seasons is winter > summer > monsoon, while in the case of CH4 winter = summer > monsoon but not any seasonal trend was noted in CO case. It is observed that CO2 has a good relation with CO (a tracer for vehicular emission) in terms of diurnal variation, whereas, CH4 does not represent a relation with CO and CO2 diurnally, suggesting that vehicles are the source of CO2 but not much contributing to other greenhouse gases like CH4.

Integrative toolbox to assess the quality of freshwater sediments contaminated with potentially toxic metals.

The Guadiana Basin is a transnational basin, presenting historical contamination with potentially toxic metals (PTM), which origin can be both natural and anthropogenic. This study explores the use of a set of observational, chemical and ecotoxicological assays with Heterocypris incongruens, Vibrio fischeri, Pseudokirchneriella subcapitata, Thamnocephalus platyurus, identifying the most sensitive to be included in a toolbox to analyze the quality of freshwater sediments related to this type of contamination. The study included the analysis of a reservoir and streams sediments of Guadiana basin, in two consecutive years with different climate conditions 2017 (dry year) and 2018 (normal year). The results showed high chemical variability along the basin, with greater contamination with PTM in the reservoir sediments. The calculated Enrichment Factors (EF) indicated high anthropogenic contamination by Cd, followed by Pb (EF > 1.5). The geoaccumulation index (Igeo) revealed that the sediments were severely polluted with Cd, and slightly polluted with Pb and Cu, inducing a higher sublethal toxicity to Heterocypris incongruens. Among the parameters evaluated, and after the use of multivariate statistical techniques, the toolbox for assessing sediments quality, in similar climate and geological conditions, should include the analysis of: meteorology, land use/cover in the area, granulometry, organic matter content, PTM concentrations, contamination indices (e.g., Igeo and EF), and sublethal bioassays with H. incongruens (total sediment analysis) and Vibrio fisheri luminescence inhibition (pore water analysis).

Long Short-term Memory-Based Prediction of the Spread of Influenza-Like Illness Leveraging Surveillance, Weather, and Twitter Data: Model Development and Validation.

BACKGROUND: The potential to harness the plurality of available data in real time along with advanced data analytics for the accurate prediction of influenza-like illness (ILI) outbreaks has gained significant scientific interest. Different methodologies based on the use of machine learning techniques and traditional and alternative data sources, such as ILI surveillance reports, weather reports, search engine queries, and social media, have been explored with the ultimate goal of being used in the development of electronic surveillance systems that could complement existing monitoring resources. OBJECTIVE: The scope of this study was to investigate for the first time the combined use of ILI surveillance data, weather data, and Twitter data along with deep learning techniques toward the development of prediction models able to nowcast and forecast weekly ILI cases. By assessing the predictive power of both traditional and alternative data sources on the use case of ILI, this study aimed to provide a novel approach for corroborating evidence and enhancing accuracy and reliability in the surveillance of infectious diseases. METHODS: The model's input space consisted of information related to weekly ILI surveillance, web-based social (eg, Twitter) behavior, and weather conditions. For the design and development of the model, relevant data corresponding to the period of 2010 to 2019 and focusing on the Greek population and weather were collected. Long short-term memory (LSTM) neural networks were leveraged to efficiently handle the sequential and nonlinear nature of the multitude of collected data. The 3 data categories were first used separately for training 3 LSTM-based primary models. Subsequently, different transfer learning (TL) approaches were explored with the aim of creating various feature spaces combining the features extracted from the corresponding primary models' LSTM layers for the latter to feed a dense layer. RESULTS: The primary model that learned from weather data yielded better forecast accuracy (root mean square error [RMSE]=0.144; Pearson correlation coefficient [PCC]=0.801) than the model trained with ILI historical data (RMSE=0.159; PCC=0.794). The best performance was achieved by the TL-based model leveraging the combination of the 3 data categories (RMSE=0.128; PCC=0.822). CONCLUSIONS: The superiority of the TL-based model, which considers Twitter data, weather data, and ILI surveillance data, reflects the potential of alternative public sources to enhance accurate and reliable prediction of ILI spread. Despite its focus on the use case of Greece, the proposed approach can be generalized to other locations, populations, and social media platforms to support the surveillance of infectious diseases with the ultimate goal of reinforcing preparedness for future epidemics.

Association of transient mitochondrial functional impairment with acute heat exposure in children from Muzaffarpur region of Bihar, India.

Over the past several years, the Muzaffarpur district of Bihar (India) has witnessed recurrent outbreaks of acute encephalitis illness of unknown etiology, called acute encephalitis syndrome (AES) among young children, especially during the peak-summer season. Pesticide exposure, viral encephalitis, and litchi toxin intake have all been postulated as potential sources of the ailment. However, no conclusive etiology for AES has been identified in the affected children. During recent rounds of the outbreak, metabolic abnormalities have been documented in these children, and a direct correlation was observed between higher environmental temperature during the peak-summer month and AES caseload. The clinical and metabolic profiles of these children suggested the possible involvement of mitochondrial dysfunction during heat stress as one of the several contributory factors leading to multisystem metabolic derangement. The present study observed that mitochondrial function parameters such as cell death, mitochondrial membrane potential, oxidative stress, and mitochondrial pathway-related gene expression in peripheral blood mononuclear cells (PBMCs) isolated from children were affected in peak-summer when compared to post-summer months. Similar observations of mitochondrial function parameters along with impaired bioenergetic parameters were demonstrated in the heat-exposed model of PBMCs isolated from healthy adult individuals. In conclusion, the results suggested that there is an association of transient mitochondrial dysfunction when exposed to sustained heat during the summer months. One may consider mitochondrial dysfunction as one of the important factors leading to an outbreak of AES among the children from affected regions though this needs to be substantiated with further studies.

Sensitivity Analysis of Parameters Affecting Wetland Water Levels: A Study of Flood Detention Basin, Colombo, Sri Lanka.

Wetlands play a vital role in ecosystems. They help in flood accumulation, water purification, groundwater recharge, shoreline stabilization, provision of habitats for flora and fauna, and facilitation of recreation activities. Although wetlands are hot spots of biodiversity, they are one of the most endangered ecosystems on the Earth. This is not only due to anthropogenic activities but also due to changing climate. Many studies can be found in the literature to understand the water levels of wetlands with respect to the climate; however, there is a lack of identification of the major meteorological parameters affecting the water levels, which are much localized. Therefore, this study, for the first time in Sri Lanka, was carried out to understand the most important parameters affecting the water depth of the Colombo flood detention basin. The temporal behavior of water level fluctuations was tested among various combinations of hydro-meteorological parameters with the help of Artificial Neural Networks (ANN). As expected, rainfall was found to be the most impacting parameter; however, apart from that, some interesting combinations of meteorological parameters were found as the second layer of impacting parameters. The rainfall-nighttime relative humidity, rainfall-evaporation, daytime relative humidity-evaporation, and rainfall-nighttime relative humidity-evaporation combinations were highly impactful toward the water level fluctuations. The findings of this study help to sustainably manage the available wetlands in Colombo, Sri Lanka. In addition, the study emphasizes the importance of high-resolution on-site data availability for higher prediction accuracy.

Designing a Low-Cost System to Monitor the Structural Behavior of Street Lighting Poles in Smart Cities.

The structural collapse of a street lighting pole represents an aspect that is often underestimated and unpredictable, but of relevant importance for the safety of people and things. These events are complex to evaluate since several sources of damage are involved. In addition, traditional inspection methods are ineffective, do not correctly quantify the residual life of poles, and are inefficient, requiring enormous costs associated with the vastness of elements to be investigated. An advantageous alternative is to adopt a distributed type of Structural Health Monitoring (SHM) technique based on the Internet of Things (IoT). This paper proposes the design of a low-cost system, which is also easy to integrate in current infrastructures, for monitoring the structural behavior of street lighting poles in Smart Cities. At the same time, this device collects previous structural information and offers some secondary functionalities related to its application, such as meteorological information. Furthermore, this paper intends to lay the foundations for the development of a method that is able to avoid the collapse of the poles. Specifically, the implementation phase is described in the aspects concerning low-cost devices and sensors for data acquisition and transmission and the strategies of information technologies (ITs), such as Cloud/Edge approaches, for storing, processing and presenting the achieved measurements. Finally, an experimental evaluation of the metrological performance of the sensing features of this system is reported. The main results highlight that the employment of low-cost equipment and open-source software has a double implication. On one hand, they entail advantages such as limited costs and flexibility to accommodate the specific necessities of the interested user. On the other hand, the used sensors require an indispensable metrological evaluation of their performance due to encountered issues relating to calibration, reliability and uncertainty.

Meteorological parameters and hospitalizations of patients with sickle cell anemia: a 20-year retrospective study in Campinas, São Paulo, Brazil.

To investigate the influence of climate on hospitalizations of sickle cell anemia (SCA) adults and children, we analyzed the health and meteorological parameters from a metropolis (1999-2018). 1462 hospitalizations were coded for SCA patients in crisis (M:F = 715:747) and 1354 hospitalizations for SCA patients without crisis (M:F = 698:656) [age = 22.9 vs 15.2 years and duration of hospitalization (DoH) = 5.7 vs 4.4 days, respectively,]. More hospitalizations were for adults than children in crisis, and for children than adults without crisis. More children and adults were hospitalized in winter andspring than in summer and autumn Hospitalizations correlated positively with humidity (lag -5), maximum pressure (lag -2), mean pressure (lag -2), and thermal amplitude (lag -2), and negatively with maximum temperature (lag -3). DoH positively correlated with minimum temperature (lag -4). Understanding these complex associations would induce attitudinal/behavioral modifications among patients and their caregivers.

An urban air quality assessment based on a meteorological perspective.

An integrated approach to understanding all measured pollutants with multi-discipline in different time scales and understanding the mechanisms hidden under low air quality (AQ) conditions is essential for tackling potential air pollution issues. In this study, the air pollution of Sivas province was analyzed with meteorological and PM2.5 data over six years to assess the city's AQ in terms of PM2.5 pollution and analyze the effect of meteorological factors on it. It was found that the winter period (January-February-November-December) of every year except 2019-which has missing data-is the period with the highest air pollution in the province. In addition, the days exceeding the daily PM2.5 limit values in 2016, 2017, 2020, and 2021 were also seen in the spring and summer months, which inclined the study to focus on additional pollutant sources such as long-range dust transport and road vehicles. The year 2017 has the highest values and was analyzed in detail. Pollution periods with the most increased episodes in 2018 were analyzed with the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) and Dust Regional Atmospheric Model (DREAM) models. As a result of the study, the average PM2.5 values in 2017 were 31.66 ± 19.2 µg/m3 and a correlation of -0.49 between temperature and PM2.5. As a result of model outputs, it was found that the inversion is intensely observed in the province, which is associated with an increase of PM2.5 during the episodes. Dust transport from northwestern Iraq and northeastern Syria is observed, especially on days with daily average PM2.5 values above 100 µg/m3. Additionally, planetary boundary layer (PBL) data analysis with PM pollution revealed a significant negative correlation (r = -0.61). Air pollutants, particularly PM2.5, were found to be higher during lower PBL levels.

Spatiotemporal distribution of pollutants and impact of local meteorology on source influence on pollutants' level in a traffic air-shed in Lagos megacity, Nigeria.

Pollution from vehicular emissions is a major cause of poor air quality observed in many urban and semi-urban towns and cities. As such, this study was conducted to assess air quality and the spatiotemporal distribution of vehicular and traffic-related pollutants in several air sheds of Lagos megacity, the economic nerve centre of Nigeria. A setup of low-cost air quality sensors comprising five (5) units was deployed between November 2018 and February 2019 within traffic corridors in the heart of the city. Diurnal variation of pollutants indicated that carbon dioxide (CO2) peaked during the early hours of the day, total oxide (Ox = NO2+O3) peaked at mid-day while carbon monoxide (CO) had two distinct peaks which correspond to morning and evening rush hours. Nitrogen dioxide (NO2) concentration peaked during evening hours. Average concentrations are NO2 (97.1 ± 9.7) ppb, Ox (78.6 ± 27.2) ppb, CO2 (450.1 ± 31.2) ppm, and CO (2285.63 ± 743.7) ppb. Average concentrations of pollutants were above thresholds set by the World Health Organization (WHO) except for NO2 which was within the range permissible limits. The implication of this is that the atmosphere is polluted due to elevated concentrations of airborne pollutants, an indication which is of both health and environmental concern. The air quality index (AQI) indicates that the quality of ambient air varies from good to very unhealthy for Ox, and unhealthy to very unhealthy for CO, while AQI for PM2.5 and PM10 showed hazardous at all the sampling locations except at UNILAG where it is unhealthy for the sensitive group. For all of the sampling sites, conditional bivariate probability function (CBPF) plots show a significant agreement with the location of known pollution sources.

Geospatial alternatives for quantification of bio-thermal influence zone in the vicinity of a solid waste dump.

Owing to the release of toxic gases, leachate and thermal emissions that originate from waste dumps, these sites significantly impact environmental sustainability. The study attempts to assess the deleterious impact of municipal solid waste (MSW) dump on surrounding forested landscape by employing geospatial technologies, which are cost and time-effective. For this purpose, temporal period ranging from 2015 to 2020, having 41 valid satellite observations has been selected for study. Firstly, the radii of intense hazardous zone and hazardous zone have been measured, as two separate parameters, which are 580 ± 30 m and 1260 ± 30 m, respectively. Secondly, average spatial extent of bio-influence zone is measured to be 1262 m while the average thermal influence zone extends up to 530 m around the MSW dumping site. A detailed analysis of influence zone variations reveals that the bio-influence zone depends on multitude of meteorological parameters, whereas the thermal influence zone relies mainly on seasonal temperature fluctuations. Moreover, the level of severity of emissions from MSW decomposition directly depends upon temperature. The long-term variability analysis of these hazardous zones reveals the stationarity of their spatial extents, signifying forest resilience. This study has proved significance of geospatial techniques as an alternate of expensive and time intensive assessment methods involving in situ measurements. So the proposed technique is beneficial for environmentalists, decision-makers and municipal authorities for analysing the extent and severity of MSW pollutants for forest community to address the problem of ecological degradation.

Air pollution impact on the Covid-19 mortality in Iran considering the comorbidity (obesity, diabetes, and hypertension) correlations.

Since the rise of the Covid-19 pandemic, several researchers stated the possibility of a positive relationship between Covid-19 spread and climatic parameters. An ecological study in 12 Iranian cities using the report of daily deaths from Covid-19 (March to August 2020) and validated data on air pollutants, considering average concentrations in each city in the last year used to analyze the association between chronic exposure to air pollutants and the death rate from Covid-19 in Iran. Poisson regression models were used, with generalized additive models and adjustment variables. A significant increase of 2.7% (IC(95%) 2.6-4.4) was found in the mortality rate due to Covid-19 due to an increase of 1 µg/m3 of NO2. The results suggest an association between Covid-19 mortality and NO2 exposure. As a risk approximation associated with air pollution, more precise analysis is done. The results also show a good consistency with studies from other regions; this paper's results can be useful for the public health policymakers and decision-making to control the Covid-19 spread.

Sustained high atmospheric Hg level in Beijing during wet seasons suggests that anthropogenic pollution is continuing: Identification of potential sources.

Gaseous elemental Hg (GEM), particulate bound Hg (PBM), and gaseous oxidized Hg (GOM) were monitored at an urban site in Beijing, China during wet seasons (July-November) of 2021. The mean (± standard deviation) GEM, PBM, and GOM concentrations were 3.45 ± 1.27 ng m-3, 48.2 ± 88.6 pg m-3, and 13.7 ± 55.0 pg m-3, respectively. GEM level was stable (generally 3.0-4.0 ng m-3) and the average concentration was about twice that of the background level in Beijing, while the occasionally very high PBM and GOM concentrations (>1000 pg m-3) suggest pollution events. Moreover, GEM, CO, and NO2 exhibit a conspicuous similar diurnal trend with lower values during daytime compared to nighttime under the combined influence of anthropogenic emissions and meteorological factors, and the significantly positive relationship between them indicates that they had similar or common sources. However, the diurnal pattern of reactive Hg (i.e., RM = PBM + GOM) was not pronounced. Both cluster and potential source contribution function analyses show that southern Beijing, Tianjin, as well as central and east Hebei provinces were the dominant source regions for elevated GEM at this monitoring site. The dominant reason for the elevated GEM level (generally >3.5 ng m-3) during pollution event is that majority of air masses originated from the southern polluted regions of this sampling site and traveled at low heights, while the long-range transport of upper clean air masses and continuous high traveling heights were attributed to the low GEM level (<2.0 ng m-3) during clean event. Positive matrix factorization results reveal that regional transport of coal fired air pollutants and local vehicle emissions were the dominant contributors to elevated GEM level, while RM mainly originated from local sources.

Amalgamation of Geometry Structure, Meteorological and Thermophysical Parameters for Intelligent Prediction of Temperature Fields in 3D Scenes.

Temperature field calculation is an important step in infrared image simulation. However, the existing solutions, such as heat conduction modelling and pre-generated lookup tables based on temperature calculation tools, are difficult to meet the requirements of high-performance simulation of infrared images based on three-dimensional scenes under multi-environmental conditions in terms of accuracy, timeliness, and flexibility. In recent years, machine learning-based temperature field prediction methods have been proposed, but these methods only consider the influence of meteorological parameters on the temperature value, while not considering the geometric structure and the thermophysical parameters of the object, which results in the low accuracy. In this paper, a multivariate temperature field prediction network based on heterogeneous data (MTPHNet) is proposed. The network fuses geometry structure, meteorological, and thermophysical parameters to predict temperature. First, a Point Cloud Feature Extraction Module and Environmental Data Mapping Module are used to extract geometric information, thermophysical, and meteorological features. The extracted features are fused by the Data Fusion Module for temperature field prediction. Experiment results show that MTPHNet significantly improves the prediction accuracy of the temperature field. Compared with the v-Support Vector Regression and the combined back-propagation neural network, the mean absolute error and root mean square error of MTPHNet are reduced by at least 23.4% and 27.7%, respectively, while the R-square is increased by at least 5.85%. MTPHNet also achieves good results in multi-target and complex target temperature field prediction tasks. These results validate the effectiveness of the proposed method.

Integrative artificial intelligence models for Australian coastal sediment lead prediction: An investigation of in-situ measurements and meteorological parameters effects.

Heavy metals (HMs) such as Lead (Pb) have played a vital role in increasing the sediments of the Australian bay's ecosystem. Several meteorological parameters (i.e., minimum, maximum and average temperature (Tmin, Tmax and TavgoC), rainfall (Rn mm) and their interactions with the other batch HMs, are hypothesized to have high impact for the decision-making strategies to minimize the impacts of Pb. Three feature selection (FS) algorithms namely the Boruta method, genetic algorithm (GA) and extreme gradient boosting (XGBoost) were investigated to select the highly important predictors for Pb concentration in the coastal bay sediments of Australia. These FS algorithms were statistically evaluated using principal component analysis (PCA) Biplot along with the correlation metrics describing the statistical characteristics that exist in the input and output parameter space of the models. To ensure a high accuracy attained by the applied predictive artificial intelligence (AI) models i.e., XGBoost, support vector machine (SVM) and random forest (RF), an auto-hyper-parameter tuning process using a Grid-search approach was also implemented. Cu, Ni, Ce, and Fe were selected by all the three applied FS algorithms whereas the Tavg and Rn inputs remained the essential parameters identified by GA and Boruta. The order of the FS outcome was XGBoost > GA > Boruta based on the applied statistical examination and the PCA Biplot results and the order of applied AI predictive models was XGBoost-SVM > GA-SVM > Boruta-SVM, where the SVM model remained at the top performance among the other statistical metrics. Based on the Taylor diagram for model evaluation, the RF model was reflected only marginally different so overall, the proposed integrative AI model provided an evidence a robust and reliable predictive technique used for coastal sediment Pb prediction.

Evolution of size-segregated aerosol concentration in NW Spain: A two-step classification to identify new particle formation events.

Real-time measurements of particles in the 15-736 nm range have been obtained by a Scanning Mobility Particle Sizer to characterize the evolution of particle size distribution and new particle formation (NPF) events in an urban background area. The annual, weekly and diurnal variations of the modal (nucleation (Nnuc), Aitken (NAit) and accumulation (Nacc)) particle concentrations were characterised. The NAit and Nacc registered their maximums in cold months during rush hours, in the morning (0600-0900 UTC) and in the afternoon (1700-2000 UTC), while the maximums for Nnuc were reached in warm months during midday hours. NAit, Nacc and Ntotal showed a significant negative correlation with wind speed and a different relationship with the planetary boundary layer (PBL) height by periods. In the warm period, a positive significant correlation between PBL and Nnuc was registered, indicating that the higher dispersion promoted by a high PBL causes favourable conditions for the occurrence of NPF events (a low polluted atmosphere). NPF processes are one of the main sources of ultrafine particles (<100 nm) in the warm period. After a visual-based classification, 45 NPF events of type Ia (strong and with a good confidence level) were identified and analysed, occurring primarily between 1100 and 1500 UTC, mainly in spring and summer. In addition, a two-step method was developed for identifying NPF events: cluster analysis followed by discriminant analysis. The application of discriminant analysis to one of the clusters, grouping 93 days, enabled us to identify 55 of the 56 NPF events days included in the cluster. This method is a valuable tool for identifying NPF events quickly and effectively.

Seasonal variation and Incidence of rupture of intracranial aneurysm : A prospective study and Literature review.

OBJECTIVES: Some studies have found an association of incidence of aneurysmal Sub arachnoid hemorrhage (aSAH) seasonal variations and weather patterns but others have refuted this. With conflicting reports in the literature, we tried to find out whether climatic conditions influence the incidence of aSAH. PATIENTS AND METHODS: This was a prospective single centre study involving patients with aSAH operated in a tertiary care hospital over one calendar year. Meteorological parameters like temperature, barometric pressure, humidity and sunshine hours were noted for 2 consecutive days prior to the ictus and on the day of ictus. RESULTS: 392 patients of aSAH who underwent clipping were enrolled. There was no significant difference in the incidence of aSAH across various seasons (p > 0.05). Pre ictus fall in temperature lead to a surge in number of cases. 241 patients (61.5%) reported were from geographical areas which had experienced a fall in temperature over preceding 2 days, with a mean fall in temperature of 1.1(SD 2.1) degree celsius (p less then 0.05). The incidence of aSAH patients in low sunshine hour seasons (1.13 patients/day) was significantly more than that in higher sunshine hour seasons (0.9 patients/day) (p less than 0.05 ). CONCLUSIONS: Seasonal variation had no direct bearing on the incidence of aSAH. Pre ictus fall in temperature lead to a rise in number of cases. Also, higher incidence of aneurysmal subarachnoid haemorrhage was seen in lower sunshine hour seasons.

Diurnal, monthly, and seasonal variations of indoor radon concentrations concerning meteorological parameters.

As reported by the Turkish Atomic Energy Agency (formerly TAEK, newly TENMAK), Izmir province has higher indoor radon concentrations compared to other cities in Turkey. Since modern people spend 92% of their daily time indoors, it is important to know indoor radon levels and long-term variation. However, our knowledge of indoor radon levels of Izmir and its surrounding are limited. Moreover, there is no information about this area's large-term variation of indoor radon. In this study, which was carried out with this motivation, indoor radon concentrations and meteorological parameters were measured in an office of the teaching staff in a university building. Data were collected hourly over 25 months (762 days). Raw data, diurnal, monthly, and seasonal variations of parameters were investigated separately. The results show that the average indoor radon concentration (18 Bq m-3) is relatively lower than national and international reference values. Indoor radon concentrations showed an increasing and decreasing trend throughout the day. Radon concentrations are slightly higher in the morning (downtime and early hours of the day) and then reduced in the afternoon. This can be related to the daily routine usage of the office, which is affected by ventilation of the room, air temperature variations, etc.