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.

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.

Influence of Temperature and Relative Humidity on PM2.5 Concentration over Delhi

The present study is an attempt to establish relationship between the concentrations of particulate matter especially (PM2.5) and background meteorological parameters over Delhi, India with the help of statistical and correlative analysis. This work presents the evaluation of air quality in three different locations of Delhi. These locations were selected to fulfil the characteristics as residential, industrial and background locations and performed the analysis for pre and post covid-19, i.e. for 2019 and 2021. The outcome of the study shows that the meteorological parameters have significant influence on the PM2.5 concentration. It was also found that it has a seasonality with low concentration in the monsoon season, moderate in the pre-monsoon season and high during the winters and post-monsoon seasons. However, the statistical and correlative study shows a negative relation with the temperature during the winter, pre-monsoon and post-monsoon and has a positive correlation during the monsoon season. Similarly, it also has been observed that the concentration of PM2.5 shows strong negative correlation with temperature during the high humid conditions, i.e. when the relative humidity is above 50%. However, a weak correlation with ambient temperature has been established during the low humidity condition, i.e. below 50%. The overall study showed that the highest PM2.5 pollution has been observed at residential location followed by industrial and background. The study also concluded that the seasonal meteorology has a complex role in the PM2.5 concentration of the selected areas.

Estimating atmospheric radon deviation using statistical coefficients: Sulaymaniyah city, Iraq, as a case of study

The authors studied the atmospheric radon concentration with associated meteorological parameters variation during the dust events from July to November 2017. We obtained the meteorological parameters data in weather station of Sulaymaniyah city, Iraq. In the environmental monitoring plan, the atmospheric radon fluctuated from 15 to 48 Bq m^-3 around the mean value of 31.5 ± 7 Bq m^-3 within the summer. In autumn, varied from 22 to 46 Bq m^-3 with a mean value of 34 ± 12 Bq m^-3. We employed this to determine the radon level anomalously. Using the modified statistical coefficients, such as the residual deviation (RD), residual fluctuation ratio (RFR), F-test, and p-value coefficients. Among the atmospheric radon fluctuation values, particularly one anomalous (42 Bq m^-3) on 25 July was determined because the excessive value of the RD was 1.9 σ, and the RFR value was 66 %. Corresponding to our coefficients criteria, the minimum level of atmospheric radon (22 Bq m^-3) does not consider anomalous because of increasing wind speed. Based on this, our method for determining the atmospheric radon anomalies that are influenced by the missed factors beyond the mentioned meteorological parameters is accurate.

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.

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.

The Impact of Meteorological Parameters and Seasonal Changes on Reporting Patients with Selected Cardiovascular Diseases to Hospital Emergency Departments: A Pilot Study

(1) Background: So far, research results have confirmed the relationship between heat and cold stress, the fluctuations in atmospheric pressure and high relative humidity, and the vulnerability of patients with so-called "weather-dependent" diseases which could lead to death. This study aimed to determine the meteorological parameters, their interactions, and the seasonal changes of the most significant factors in predicting the number of patients reporting to the Emergency Departments (EDs) in Poznań (Poland) during 2019. (2) Methods: The analysis included the meteorological parameters and data of 3606 patients diagnosed with essential or complicated arterial hypertension, myocardial infarction, chronic ischemic heart disease, and ischemic or unspecified stroke by the International Classification of Diseases (ICD-10). The meteorological data (days per week and seasonal data) were used to build a linear regression model to assess the changes in the daily number of reporting patients. The input data for the final model were selected based on the principal component analysis (PCA), and built for each delay and acceleration (reporting up to 3 days before the change or up to 3 days after the change of the meteorological parameter). (3) Results: A significantly lower number of reports was observed during weekends compared to working days (standardised b = -0.254, p-value < 0.0001) and three days before the maximum daily air temperature in the spring and summer period (standardised b = -0.748, p-value < 0.0001), while two days after the increase in the daily amplitude of atmospheric pressure (standardised b = 0.116, p-value = 0.0267), and also on the day of occurrence of the unfavourable interdiurnal air temperature change, an increase in the number of patients was noted (standardised b = 0.115, p-value = 0.0186). The changes in the last two parameters were statistically insignificant. Based on the obtained results, the negative impact of the changes in the meteorological conditions on the number of reports to the EDs in Poznań was determined.

Substantial Changes in Selected Volatile Organic Compounds (VOCs) and Associations with Health Risk Assessments in Industrial Areas during the COVID-19 Pandemic

During the COVID-19 pandemic, governments in many countries worldwide, including India, imposed several restriction measures, including lockdowns, to prevent the spread of the infection. COVID-19 lockdowns led to a reduction in gaseous and particulate pollutants in ambient air. In the present study, we investigated the substantial changes in selected volatile organic compounds (VOCs) after the outbreak of the coronavirus pandemic and associations with health risk assessments in industrial areas. VOC data from 1 January 2019 to 31 December 2021 were collected from the Central Pollution Control Board (CPCB) website, to identify percentage changes in VOC levels before, during, and after COVID-19. The mean TVOC levels at all monitoring stations were 47.22 ± 30.15, 37.19 ± 37.19, and 32.81 ± 32.81 µg/m³ for 2019, 2020, and 2021, respectively. As a result, the TVOC levels gradually declined in consecutive years due to the pandemic in India. The mean TVOC levels at all monitoring stations declined from 9 to 61% during the pandemic period as compared with the pre-pandemic period. In the current study, the T/B ratio values ranged from 2.16 (PG) to 26.38 (NL), which indicated that the major pollutant contributors were traffic and non-traffic sources during the pre-pandemic period. The present findings indicated that TVOC levels had positive but low correlations with SR, BP, RF, and WD, with correlation coefficients (r) of 0.034, 0.118, 0.012, and 0.007, respectively, whereas negative correlations were observed with AT and WS, with correlation coefficients (r) of -0.168 and -0.150, respectively. The lifetime cancer risk (LCR) value for benzene was reported to be higher in children, followed by females and males, for the pre-pandemic, pandemic, and post-pandemic periods. A nationwide scale-up of this study's findings might be useful in formulating future air pollution reduction policies associated with a reduction in health risk factors. Furthermore, the present study provides baseline data for future studies on the impacts of anthropogenic activities on the air quality of a region.

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.

Impact of Different Air Pollutants (PM10, PM2.5, NO2, and Bacterial Aerosols) on COVID-19 Cases in Gliwice, Southern Poland

Many studies have shown that air pollution may be closely associated with increased morbidity and mortality due to COVID-19. It has been observed that exposure to air pollution leads to reduced immune response, thereby facilitating viral penetration and replication. In our study, we combined information on confirmed COVID-19 daily new cases (DNCs) in one of the most polluted regions in the European Union (EU) with air-quality monitoring data, including meteorological parameters (temperature, relative humidity, atmospheric pressure, wind speed, and direction) and concentrations of particulate matter (PM₁₀ and PM_2.5), sulfur dioxide (SO₂), nitrogen oxides (NO and NO₂), ozone (O₃), and carbon monoxide (CO). Additionally, the relationship between bacterial aerosol (BA) concentration and COVID-19 spread was analyzed. We confirmed a significant positive correlation (p < 0.05) between NO₂ concentrations and numbers of confirmed DNCs and observed positive correlations (p < 0.05) between BA concentrations and DNCs, which may point to coronavirus air transmission by surface deposits on bioaerosol particles. In addition, wind direction information was used to show that the highest numbers of DNCs were associated with the dominant wind directions in the region (southern and southwestern parts).

Environmental Factors in Northern Italy and Sickle Cell Disease Acute Complications: A Multicentric Study

BACKGROUND

Environmental factors seem to influence clinical manifestations of sickle cell disease (SCD), but few studies have shown consistent findings. We conducted a retrospective multicentric observational study to investigate the influence of environmental parameters on hospitalization for vaso-occlusive crises (VOC) or acute chest syndrome (ACS) in children with SCD.

METHODS

Hospital admissions were correlated with daily meteorological and air-quality data obtained from Environmental Regional Agencies in the period 2011-2015. The effect of different parameters was assessed on the day preceding the crisis up to ten days before. Statistical analysis was performed using a quasi-likelihood Poisson regression in a generalized linear model.

RESULTS

The risk of hospitalization was increased for low maximum temperature, low minimum relative humidity, and low atmospheric pressure and weakly for mean wind speed. The diurnal temperature range and temperature difference between two consecutive days were determined to be important causes of hospitalization. For air quality parameters, we found a correlation only for high levels of ozone and for low values at the tail corresponding to the lowest concentration of this pollutant.

CONCLUSIONS

Temperature, atmospheric pressure, humidity and ozone levels influence acute complications of SCD. Patients' education and the knowledge of the modes of actions of these factors could reduce hospitalizations.

Decentralised ventilation efficiency for indoor radon reduction considering different environmental parameters

ABSTRACTRadon-222 contributes to half of the natural radiation exposure of humans and is one of the main causes of lung cancer. Of particular importance for humans is the exposure to radon-222 indoors, which enters living and working areas from the soil air, e.g. through cracks in the foundations of buildings. An easy and efficient way to minimise indoor radon in dwellings can be achieved through ventilation. How meteorological parameters and the geological background can influence ventilation efficiency in reducing indoor radon has not yet been fully investigated. Therefore, a decentralised ventilation system was installed in an unoccupied flat located in a former uranium mining region to analyse the effect of already existing ventilation modes on indoor radon activity concentration. It is aimed to assess 22 different ventilation experiments that were performed within the time period of one year. Even with a strong seasonal trend with significantly lower indoor radon activity concentrations in summer compared to winter, the decentralised ventilation system was able to reduce indoor radon by up to 83 %. Thereby, strong dependencies on the experimental parameters such as ventilation type or performance level of the fans were found.

Relationship between Meteorological and Air Quality Parameters and COVID-19 in Casablanca Region, Morocco

The aim of this study was to investigate the relationship between meteorological parameters, air quality and daily COVID-19 transmission in Morocco. We collected daily data of confirmed COVID-19 cases in the Casablanca region, as well as meteorological parameters (average temperature, wind, relative humidity, precipitation, duration of insolation) and air quality parameters (CO, NO₂, 03, SO₂, PM10) during the period of 2 March 2020, to 31 December 2020. The General Additive Model (GAM) was used to assess the impact of these parameters on daily cases of COVID-19. A total of 172,746 confirmed cases were reported in the study period. Positive associations were observed between COVID-19 and wind above 20 m/s and humidity above 80%. However, temperatures above 25° were negatively associated with daily cases of COVID-19. PM10 and O₃ had a positive effect on the increase in the number of daily confirmed COVID-19 cases, while precipitation had a borderline effect below 25 mm and a negative effect above this value. The findings in this study suggest that significant associations exist between meteorological factors, air quality pollution (PM10) and the transmission of COVID-19. Our findings may help public health authorities better control the spread of COVID-19.

Contrasting Trends of Surface PM2.5, O3, and NO2 and Their Relationships with Meteorological Parameters in Typical Coastal and Inland Cities in the Yangtze River Delta

The contrasting trends of surface particulate matter (PM_2.5), ozone (O₃), and nitrogen dioxide (NO₂) and their relationships with meteorological parameters from 2015 to 2019 were investigated in the coastal city of Shanghai (SH) and the inland city of Hefei (HF), located in the Yangtze River Delta (YRD). In both cities, PM_2.5 declined substantially, while O₃ and NO₂ showed peak values during 2017 when the most frequent extreme high-temperature events occurred. Wind speed was correlated most negatively with PM_2.5 and NO₂ concentrations, while surface temperature and relative humidity were most closely related to O₃. All of the studied pollutants were reduced by rainfall scavenging, with the greatest reduction seen in PM_2.5, followed by NO₂ and O₃. By contrast, air pollutants in the two cities were moderately strongly correlated, although PM_2.5 concentrations were much lower and O_x (O₃ + NO₂) concentrations were higher in SH. Additionally, complex air pollution hours occurred more frequently in SH. Air pollutant concentrations changed more with wind direction in SH. A more effective washout effect was observed in HF, likely due to the more frequent strong convection and thunderstorms in inland areas. This research suggests pertinent air quality control measures should be designed accordingly for specific geographical locations.

The Influence of Selected Meteorological Factors on the Prevalence and Course of Stroke

Background: The objective of this study was to evaluate the impact of weather factors on stroke parameters. Methods: This retrospective study analyzed the records of stroke patients concerning the influence of meteorological conditions and moon phases on stroke parameters. Results: The study group consisted of 402 patients aged between 20 and 102; women constituted 49.8% of the subjects. Ischaemic stroke was diagnosed in 90.5% of patients and hemorrhagic stroke was diagnosed in 9.5% of patients. The highest number of hospitalizations due to stroke was observed in January (48 events); the lowest number was observed in July (23 events). There was no statistically significant correlation between the meteorological parameters on the day of onset and the preceding day of stroke and the neurological status (NIHSS) of patients. Mean air temperature on the day of stroke and the day preceding stroke was significantly lower in the group of patients discharged with a very good functional status (≤2 points in modified Rankin scale (mRS)) compared to the patients with a bad functional status (>2 points in mRS); respectively: 7.98 ± 8.01 vs. 9.63 ± 7.78; p = 0.041 and 8.13 ± 7.72 vs. 9.70 ± 7.50; p = 0.048). Humidity above 75% on the day of stroke was found to be a factor for excellent functional state (RR 1.61; p = 0.016). The total anterior circulation infarcts (in comparison with stroke in the other localization) were more frequent (70%) during a third quarter moon (p = 0.011). The following parameters had a significant influence on the number of stroke cases in relation to autumn having the lowest number of onsets: mean temperature (OR 1.019 95% CI 1.014–1.024, p < 0.000), humidity (OR 1.028, CI 1.023–1.034, p < 0.0001), wind speed (OR 0.923, 95% CI 0.909–0.937, p < 0.0001), insolation (OR 0.885, 95% CI 0.869–0.902, p < 0.0001), precipitation (OR 0.914, 95% CI 0.884–0.946, p < 0.0001). Conclusion: Air humidity and air temperature on the day of stroke onset as well as air temperature on the day preceding stroke are important for the functional status of patients in the acute disease period. A combination of the following meteorological parameters: lowered mean temperature and low sunshine, high humidity and high wind speed all increase the risk of stroke during the winter period. High humidity combined with high precipitation, low wind speed and low sunshine in the autumn period are associated with the lowest stroke incidence risk. A possible relationship between phases of the moon and the incidence requires further investigation.

Emerging role of air pollution and meteorological parameters in COVID-19

Exposure to air pollutants has been associated with respiratory viral infections. Epidemiological studies have shown that air pollution exposure is related to increased cases of SARS-COV-2 infection and COVID-19-associated mortality. In addition, the changes of meteorological parameters have also been implicated in the occurrence and development of COVID-19. However, the molecular mechanisms by which pollutant exposure and changes of meteorological parameters affects COVID-19 remains unknown. This review summarizes the biology of COVID-19 and the route of viral transmission, and elaborates on the relationship between air pollution and climate indicators and COVID-19. Finally, we envisaged the potential roles of air pollution and meteorological parameters in COVID-19.

Impact of Lockdown Measures and Meteorological Parameters on the COVID-19 Incidence and Mortality Rate in Bangladesh

The coronavirus disease 2019 (COVID-19) pandemic has become a public health crisis and a global catastrophe for human societies. In the absence of a vaccine, non-pharmaceutical interventions have been implemented across the world to reduce COVID-19 transmission. Recently, several studies have articulated the influence of meteorological parameters on COVID-19 infections in several countries. The purpose of this study was to investigate the effect of lockdown measures and meteorological parameters on COVID-19 daily confirmed cases and deaths in Bangladesh. Different parameters, such as case fatality rate, recovery rate, number of polymerase chain reaction tests, and percentages of confirmed cases were calculated for data covering March to September 2020. The meteorological data include daily average temperature, humidity, and wind speed, and their effects on COVID-19 data were analyzed after 0, 3, 7, and 14 days. A linear regression analysis revealed that all the studied meteorological parameters were positively correlated with the daily new cases and deaths in Bangladesh, while the highest correlations were observed for the 14 days incubation period. These results provide useful implications for the healthcare authorities to contain the pandemic in Bangladesh and beyond.

Diversity, Concentration and Dynamics of Culturable Fungal Bioaerosols at Doha, Qatar

This research was conducted to investigate the dynamics of airborne fungi using viable culture collection and in respect to different abiotic variables, including seasonal and intra-diurnal variations. A gravimetric method was used to sample airborne fungal deposition on potato dextrose agar plates on alternate days, for a year between April 2015 to March 2016. From 176 settle plate exposures, a total of 1197 mould and 283 yeast colony-forming units (CFU), 21 genera and 62 species were retrieved. The highest fungal spore count was recorded in February 2016, whereas the lowest count occurred in August 2015. The main constituents of the fungal airspora were attributed to Cladosporium (60.2%), Aspergillus (10.4%), Fusarium (9.4%), Alternaria (8.5%), and Ganoderma spp. (2.3%). Temperature was negatively correlated with total colony count (r = −0.231, p ≤ 0.05) or species richness (r = −0.267, p ≤ 0.001), while wind speed was positively correlated with total colony count (r = 0.484, p ≤ 0.001) or species richness (r = 0.257, p ≤ −0.001). The highest dispersal of fungal spores was obtained at 18:00, whereas the lowest fungal spores release was recorded at 00:00 (midnight). There were no significant differences in species composition and richness of the airborne fungal population between two study sites, the Industrial area and Qatar University Campus. The count of Alternaria spp. and Fusarium spp. were significantly higher at the Industrial area site, which corresponds to a higher CO₂ level than the Qatar University site. This study lays the foundation for future work to assess the implications of such aeromycological data on public health.

[Analysis of Characteristics of Water-soluble Ions in PM2.5 in Chengdu Based on the MARGA]

Water-soluble ions in PM_2.5 were serially on-line monitored using the MARGA sampling and measurement system in Chengdu in 2019. Pollution characteristics of water-soluble ions were analyzed using the meteorological monitoring data. The results show that variations in the concentrations of eight water-soluble ions were consistent with the variations in PM_2.5 in Chengdu. The annual average mass concentration of the total water-soluble ions was (20.2±12.7) μg ·m^-3, accounting for 48.6% of the PM_2.5 mass, which indicates that water-soluble ions were the major components of PM_2.5. The mass concentrations of all the ions were in the order of NO₃^- > SO₄^2- > NH₄⁺ > Cl^- > Ca²⁺ > K⁺ > Mg²⁺ > Na⁺. The annual average mass concentration of secondary ions was (20.2±12.7) μg ·m^-3, accounting for 87.2% of total water-soluble ions. The concentrations of total water-soluble ions in different seasons were in the order of winter > spring ≈ autumn > summer. Monthly variations in total water-soluble ion concentrations followed a U-shaped curve; mass concentrations were the highest in January and December and lowest from June to August. Monthly variations in the concentrations of NO₃^-, SO₄^2-, NH₄⁺, Cl^-, Na⁺, and K⁺ were consistent with the total water-soluble ion concentrations, while the concentrations of Ca²⁺ and Mg²⁺ were the highest in June. Visibility declined with the increase in the concentration of water-soluble ions, especially secondary water-soluble ions regardless of the rainfall. Light rain (accumulated rainfall in 24 h <10 mm) had no scavenging effect on water-soluble ions, while moderate and heavy rainfall had a significant effect. There was a significant positive correlation between NO₃^-, SO₄^2-, and NH₄⁺ (all the correlation coefficients were over 0.7), indicating that the mechanisms of evolution of secondary water-soluble ions in the atmosphere are highly similar to each other. The annual mean values of SOR and NOR were 0.42 and 0.12, respectively, which were negatively correlated with temperature and O₃ and positively correlated with humidity, indicating that the main source of SO₄^2- was heterogeneous oxidation reactions in the liquid phase and the main source of NO₃^- was heterogeneous oxidation reactions at night. The annual mean values of CE/AE and NR were 1.2 and 1.1, respectively, indicating that most aerosols in the study area were relatively alkaline. The atmospheric environment of Chengdu is rich in ammonia; thus, (NH₄)₂ SO₄ and NH₄NO₃ were the main forms of secondary ions.

Monitoring of PM2.5 Concentrations by Learning from Multi-Weather Sensors

This paper aims to monitor the ambient level of particulate matter less than 2.5 μm (PM2.5) by learning from multi-weather sensors. Over the past decade, China has established a high-density network of automatic weather stations. In contrast, the number of PM monitors is much smaller than the number of weather stations. Since the haze process is closely related to the variation of meteorological parameters, it is possible and promising to calculate the concentration of PM2.5 by studying the data from weather sensors. Here, we use three machine learning methods, namely multivariate linear regression, multivariate nonlinear regression, and neural network, in order to monitor PM2.5 by exploring the data of multi-weather sensors. The results show that the multivariate linear regression method has the root mean square error (RMSE) of 24.6756 μg/m3 with a correlation coefficient of 0.6281, by referring to the ground truth of PM2.5 time series data; and the multivariate nonlinear regression method has the RMSE of 24.9191 μg/m3 with a correlation coefficient of 0.6184, while the neural network based method has the best performance, of which the RMSE of PM2.5 estimates is 15.6391 μg/m3 with the correlation coefficient of 0.8701.

Concentration Variability of Water-Soluble Ions during the Acceptable and Exceeded Pollution in an Industrial Region

This study investigates the chemical composition of water-soluble inorganic ions at eight localities situated in the Moravian-Silesian Region (the Czech Republic) at the border with Poland. Water-soluble inorganic ions were monitored in the winter period of 2018 (January, 11 days and February, 5 days). The set was divided into two periods: the acceptable period (the 24-h concentration of PM₁₀ < 50 µg/m³) and the period with exceeded pollution (PM₁₀ ˃ 50 µg/m³). Air quality in the Moravian-Silesian Region and Upper Silesia is among the most polluted in Europe, especially in the winter season when the concentration of PM₁₀ is repeatedly exceeded. The information on the occurrence and behaviour of water-soluble inorganic ions in the air during the smog episodes in Europe is insufficient. The concentrations of water-soluble ions (chlorides, sulphates, nitrates, ammonium ions, potassium) during the exceeded period are higher by two to three times compared with the acceptable period. The major anions for both acceptable period and exceeded pollution are nitrates. During the period of exceeded pollution, percentages of water-soluble ions in PM₁₀ decrease while percentages of carbonaceous matter and insoluble particles (fly ash) increase.

Spatiotemporal Variations of Particulate and Gaseous Pollutants and Their Relations to Meteorological Parameters: The Case of Xiangyang, China

High air pollution levels have become a nationwide problem in China, but limited attention has been paid to prefecture-level cities. Furthermore, different time resolutions between air pollutant level data and meteorological parameters used in many previous studies can lead to biased results. Supported by synchronous measurements of air pollutants and meteorological parameters, including PM_2.5, PM₁₀, total suspended particles (TSP), CO, NO₂, O₃, SO₂, temperature, relative humidity, wind speed and direction, at 16 urban sites in Xiangyang, China, from 1 March 2018 to 28 February 2019, this paper: (1) analyzes the overall air quality using an air quality index (AQI); (2) captures spatial dynamics of air pollutants with pollution point source data; (3) characterizes pollution variations at seasonal, day-of-week and diurnal timescales; (4) detects weekend effects and holiday (Chinese New Year and National Day holidays) effects from a statistical point of view; (5) establishes relationships between air pollutants and meteorological parameters. The principal results are as follows: (1) PM_2.5 and PM₁₀ act as primary pollutants all year round and O₃ loses its primary pollutant position after November; (2) automobile manufacture contributes to more particulate pollutants while chemical plants produce more gaseous pollutants. TSP concentration is related to on-going construction and road sprinkler operations help alleviate it; (3) an unclear weekend effect for all air pollutants is confirmed; (4) celebration activities for the Chinese New Year bring distinctly increased concentrations of SO₂ and thereby enhance secondary particulate pollutants; (5) relative humidity and wind speed, respectively, have strong negative correlations with coarse particles and fine particles. Temperature positively correlates with O₃.

[Distribution of Total Microbes in Atmospheric Bioaerosols in the Coastal Region of Qingdao]

In order to determine the concentrations and size distributions of total airborne microbes in atmospheric bioaerosols, bioaerosol samples were collected in the coastal region of Qingdao, China, from Sep. 2016 to Jul. 2017 using size-fractionated bioaerosol samplers. The concentrations of total airborne microbes were then measured using an epifluorescence microscope after staining with DAPI (4', 6-diamidino-2-phenylindole). The results showed that the concentrations of total airborne microbes were in the range of 1.86×10⁵-2.54×10⁶ cells·m^-3 in Qingdao during the sampling period, with an average of (6.84±4.83)×10⁵ cells·m^-3. The concentrations of total airborne microbes showed seasonal variation, with the highest values occurring in spring and winter, and the lowest values in summer and fall. Statistical analysis showed that the concentrations of total airborne microbes in autumn had a significant seasonal difference with spring and summer (P<0.05). The monthly averaged concentrations of total airborne microbes ranged from 2.65×10⁵ cells·m^-3 to 1.12×10⁶ cells·m^-3, with the highest values in Feb. 2017 and the lowest in Sep. 2016. Total microbe concentrations also varied greatly over a diurnal timescale in fall and winter during the period of 2015 to 2017, but this variation was not statistically significant (P>0.05). Overall, the size distribution of total airborne microbes was skewed, with the highest ratio occurring at >7.0 μm with a ratio of 20.5%-27.3%. The monthly averaged size distribution of total airborne microbes varied between months, showing two main distribution types, specifically bimodal and skewed patterns, respectively. Spearman's rank correlation analysis showed that the AQI and the concentrations of CO, PM_2.5, and PM₁₀ had a significant positive correlation with the concentrations of total airborne microbes (P<0.05), while air temperature, wind speed and direction, NO₂, SO₂, and O₃ were not significantly correlated with the airborne microbe concentration during the sampling period (P>0.05). Multiple linear regression showed that 20.6% of the variation in the total microbe concentrations in bioaerosols correlated with relative humidity and PM_2.5.

Methane emissions from landfill: influence of vegetation and weather conditions

Vegetation plays an important role in CH₄ transport and oxidation in landfill cover soil. This study investigated CH₄ emission fluxes in two landfills with different surface coverage conditions and it found that the CH₄ emission fluxes presented spatial and temporal disparities. A significant discrepancy in CH₄ emission flux between day and night in areas covered with Kochia sieversiana indicated that enhanced diffusion induced by rising temperature was the main mechanism for CH₄ transport during daytime. A significant increase of CH₄ emission flux after the K. sieversiana and Suaeda glauca plants were cut indicated that these plants provide greater contributions to CH₄ oxidation than to CH₄ transport. Diel CH₄ emission flux was found closely correlated with the climatic conditions. Diffusion was determined as the main mechanism for CH₄ transport at daytime in bare area, mediated by solar radiation and air temperature. Diffusion and plant-mediated transport by convection was established as the main transport mechanism in areas covered with K. sieversiana. Our results further the understanding of both the CH₄ emission mechanism and the impact of vegetation on CH₄ oxidation, transport, and emission, which will benefit the development of a reliable model for landfill CH₄ emissions.

Agglomerative Clustering of Enteric Infections and Weather Parameters to Identify Seasonal Outbreaks in Cold Climates

The utility of agglomerative clustering methods for understanding dynamic systems that do not have a well-defined periodic structure has not yet been explored. We propose using this approach to examine the association between disease and weather parameters, to compliment the traditional harmonic regression models, and to determine specific meteorological conditions favoring high disease incidence. We utilized daily records on reported salmonellosis and non-specific enteritis, and four meteorological parameters (ambient temperature, dew point, humidity, and barometric pressure) in Barnaul, Russia in 2004–2011, maintained by the CliWaDIn database. The data structure was examined using the t-distributed stochastic neighbor embedding (t-SNE) method. The optimal number of clusters was selected based on Ward distance using the silhouette metric. The selected clusters were assessed with respect to their density and homogeneity. We detected that a well-defined cluster with high counts of salmonellosis occurred during warm summer days and unseasonably warm days in spring. We also detected a cluster with high counts of non-specific enteritis that occurred during unusually “very warm” winter days. The main advantage offered by the proposed technique is its ability to create a composite of meteorological conditions—a rule of thumb—to detect days favoring infectious outbreaks for a given location. These findings have major implications for understanding potential health impacts of climate change.

Influence of Climate on the Incidence of RCVS - A Retrospective Study From Taiwan

BACKGROUND

Cold weather is reportedly a precipitator of reversible cerebral vasoconstriction syndrome (RCVS) in a few cases. We systematically investigated whether meteorological factors correlate with the occurrence of RCVS.

METHODS

We conducted a retrospective analysis of a cohort of patients diagnosed with RCVS or probable RCVS, based on International Classification of Headache Disorders, third edition (ICHD-3) criteria, in a hospital-based headache center from March 2005 to February 2014. Monthly averages of local weather data measured in Taipei were obtained from the Central Weather Bureau in Taiwan. Primary weather variables were compared with the number of monthly new-onset cases of RCVS.

RESULTS

We recruited 226 patients with established RCVS and 72 patients with probable RCVS during a 108-month study period. Incidence of RCVS was higher in winter than summer months (3.3 persons/month [SD: 2.0] vs 2.1 persons/month [SD: 1.5], P = .013). The monthly incidence of RCVS correlated negatively with mean daily temperature (r = -0.231, P = .016) and average precipitation (r = -0.269, P = .005), but positively with barometric pressure (r = 0.274, P = .004). These 3 correlated meteorological factors together explained about 10% of the variance in RCVS monthly incidence (R²  = 0.095, P = .015).

CONCLUSION

RCVS was found to be more common in winter months and to be associated with weather variables in Taiwan. Further studies are needed to explore the underlying mechanisms of these associations.

Influence of meteorological parameters in the seasonality of influenza viruses circulating in Northern Cameroon

BACKGROUND

Several studies have demonstrated the role of meteorological parameters in the seasonality of influenza viruses in tropical and subtropical regions, most importantly temperature, humidity, and rainfall.

OBJECTIVES

This study aimed to describe the influence of meteorological parameters in the seasonality of influenza viruses in Northern Cameroon, a region characterized by high temperatures.

METHODS

This was a retrospective study performed in Garoua Cameroon from January 2014 to December 2016. Monthly proportions of confirmed influenza cases from six sentinel sites were considered as dependent variables, whereas monthly values of mean temperature, average relative humidity, and accumulated rainfall were considered as independent variables. A vector error correction model was used to determine the relationship between influenza activity and the meteorological variables.

RESULTS AND CONCLUSION

Analysis showed that there was a statistically significant association between overall influenza activity and influenza A activity with respect to average relative humidity. A unit increase in humidity within a given month leads to more than 85% rise in the overall influenza and influenza A activity 2 months later. Meanwhile, none of the three meteorological variables could explain influenza B activity. This observation is essential in filling the gap of knowledge and could help in the prevention and control strategies to strengthen influenza surveillance program in Cameroon.

Relationships between Meteorological Parameters and Particulate Matter in Mae Hong Son Province, Thailand

Meteorological parameters play an important role in determining the prevalence of ambient particulate matter (PM) in the upper north of Thailand. Mae Hong Son is a province located in this region and which borders Myanmar. This study aimed to determine the relationships between meteorological parameters and ambient concentrations of particulate matter less than 10 µm in diameter (PM₁₀) in Mae Hong Son. Parameters were measured at an air quality monitoring station, and consisted of PM₁₀, carbon monoxide (CO), ozone (O₃), and meteorological factors, including temperature, rainfall, pressure, wind speed, wind direction, and relative humidity (RH). Nine years (2009⁻2017) of pollution and climate data obtained from the Thai Pollution Control Department (PCD) were used for analysis. The results of this study indicate that PM₁₀ is influenced by meteorological parameters; high concentration occurred during the dry season and northeastern monsoon seasons. Maximum concentrations were always observed in March. The PM₁₀ concentrations were significantly related to CO and O₃ concentrations and to RH, giving correlation coefficients of 0.73, 0.39, and -0.37, respectively (p-value < 0.001). Additionally, the hourly PM₁₀ concentration fluctuated within each day. In general, it was found that the reporting of daily concentrations might be best suited to public announcements and presentations. Hourly concentrations are recommended for public declarations that might be useful for warning citizens and organizations about air pollution. Our findings could be used to improve the understanding of PM₁₀ concentration patterns in Mae Hong Son and provide information to better air pollution measures and establish a warning system for the province.

Understanding Weather and Hospital Admissions Patterns to Inform Climate Change Adaptation Strategies in the Healthcare Sector in Uganda

Background: Season and weather are associated with many health outcomes, which can influence hospital admission rates. We examined associations between hospital admissions (all diagnoses) and local meteorological parameters in Southwestern Uganda, with the aim of supporting hospital planning and preparedness in the context of climate change. Methods: Hospital admissions data and meteorological data were collected from Bwindi Community Hospital and a satellite database of weather conditions, respectively (2011 to 2014). Descriptive statistics were used to describe admission patterns. A mixed-effects Poisson regression model was fitted to investigate associations between hospital admissions and season, precipitation, and temperature. Results: Admission counts were highest for acute respiratory infections, malaria, and acute gastrointestinal illness, which are climate-sensitive diseases. Hospital admissions were 1.16 (95% CI: 1.04, 1.31; p = 0.008) times higher during extreme high temperatures (i.e., >95th percentile) on the day of admission. Hospital admissions association with season depended on year; admissions were higher in the dry season than the rainy season every year, except for 2014. Discussion: Effective adaptation strategy characteristics include being low-cost and quick and practical to implement at local scales. Herein, we illustrate how analyzing hospital data alongside meteorological parameters may inform climate-health planning in low-resource contexts.

The Association Between Heat Waves and Other Meteorological Parameters and Snakebites: Israel National Study

BACKGROUND

Published annual estimates report a global burden of 2.5 million snakebite cases and >100,000 deaths. In Israel, envenomations are the third most frequent cause of poisonings that are of moderate to major clinical severity. Most studies focus on the clinical descriptions of snakebites in tropical climates, and we sought to investigate the association between snakebite frequency and meteorological parameters.

OBJECTIVE

We sought to investigate the seasonality of snakebites and evaluate the association between increasingly common heat waves and other meteorological parameters and snakebite frequency in a semiarid nontropical climate.

METHODS

We obtained data for all medical evacuations (2008-2015) because of snakebites in Israel. Climate data included daily 24-hour average temperature (°C) and relative humidity (%). We used a time-stratified case crossover method, in which a conditional logistic regression was applied to estimate the association, and we also stratified our analysis by season and by region.

RESULTS

We identified 1234 snakebite cases over 8 years, of which most (74.2%) occurred in hot seasons and between 6 pm and 9 pm. The risk of snakebite was positively associated with temperature >23°C (odds ratio [OR] 1.24, 95% confidence interval [CI] 1.01-1.53) and inversely with humidity >40% (OR 0.74, 95% CI 0.57-0.97). We also found an association with heat waves both in cold (OR 1.62, 95% CI 1.01-2.60) and hot seasons (OR 1.50, 95% CI 1.18-1.92).

CONCLUSIONS

In a semiarid nontropical climate, we observed an association between an increase in the number of snakebite cases and higher temperatures and lower humidity. Moreover, heat waves increased the frequency of snakebites in both cold and hot seasons.

Climatic Factors and Influenza Transmission, Spain, 2010-2015

The spatio-temporal distribution of influenza is linked to variations in meteorological factors, like temperature, absolute humidity, or the amount of rainfall. The aim of this study was to analyse the association between influenza activity, and meteorological variables in Spain, across five influenza seasons: 2010–2011 through to 2014–2015 using generalized linear negative binomial mixed models that we calculated the weekly influenza proxies, defined as the weekly influenza-like illness rates, multiplied by the weekly proportion of respiratory specimens that tested positive for influenza. The results showed an association between influenza transmission and dew point and cumulative precipitation. In increase in the dew point temperature of 5 degrees produces a 7% decrease in the Weekly Influenza Proxy (RR 0.928, IC: 0.891–0.966), and while an increase of 10 mm in weekly rainfall equates to a 17% increase in the Weekly Influenza Proxy (RR 1.172, IC: 1.097–1.251). Influenza transmission in Spain is influenced by variations in meteorological variables as temperature, absolute humidity, or the amount of rainfall.

The influence of seasonality and weather changes on premature birth incidence

Background

Although the effects of meteorological factors on the general population health are widely documented, little is known about their influence on human pregnancy and birth. The present study aims at analyzing the influence of the atmospheric conditions on premature births.

Method

One hundred and eight nine cases of premature births were included in the study, with a gestational age between 24 to 37 weeks of amenorrhea. Cases with antepartum fetal death and those with uncertain gestational age have been excluded. Daily weather data were obtained using http://www.wunderground.com site.

A Pearson’s product-moment correlation was run to assess the relationship between weekly preterm birth incidence and the total number of premature births and the mean maximum and minimum temperature (Tmax, Tmin), maximum and minimum average humidity (Umax, Umin), maximum and minimum atmospheric pressure mean (P max, P min), average wind speed and average quantity precipitations, calculated for one calendar week.

Results

Approximately 7.7% of all births during the study period occurred before 37 weeks of gestation, the main reason for hospitalization being premature rupture of membranes (45%). The analysis revealed a moderate positive correlation between weekly preterm birth incidence and the average temperature (r=0.306, n=52, p=0.027) and a moderate positive correlation between weekly preterm birth incidence and temperature variation (r=0.307, n=52, p=0.007). Our study found no significant statistic correlation between the humidity variation, pressure variation, and wind speed.

Conclusions

The incidence of premature births can be influenced by variations of specific weather factors, especially during the weeks characterized by large fluctuations in temperature. The results obtained might inspire the construction of multicenter studies to investigate more thoroughly the adverse effects of some meteorological factors that can influence the outcomes of human pregnancy.

[Parameters modification and evaluation of two evapotranspiration models based on Penman-Monteith model for summer maize]

The dynamic variations of evapotranspiration (ET) and weather data during summer maize growing season in 2013-2015 were monitored with eddy covariance system, and the applicability of two operational models (FAO-PM model and KP-PM model) based on the Penman-Monteith model were analyzed. Firstly, the key parameters in the two models were calibrated with the measured data in 2013 and 2014; secondly, the daily ET in 2015 calculated by the FAO-PM model and KP-PM model was compared to the observed ET, respectively. Finally, the coefficients in the KP-PM model were further revised with the coefficients calculated according to the different growth stages, and the performance of the revised KP-PM model was also evaluated. These statistical parameters indicated that the calculated daily ET for 2015 by the FAO-PM model was closer to the observed ET than that by the KP-PM model. The daily ET calculated from the revised KP-PM model for daily ET was more accurate than that from the FAO-PM model. It was also found that the key parameters in the two models were correlated with weather conditions, so the calibration was necessary before using the models to predict the ET. The above results could provide some guidelines on predicting ET with the two models.

Seasonal Dynamics of Metabolites in Needles of Taxus wallichiana var. mairei

Seasonal variations of the phytochemicals contents in needles of T. wallichiana var. mairei due to the effects of growth meteorological parameters were investigated in this study. The needles of T. wallichiana var. mairei were collected from different months and the contents of taxoids (paclitaxel, 10-deacetylbaccatin III (10-DAB), baccatin III, cephalomannine, 10-deacetyltaxol (10-DAT)), flavones (ginkgetin, amentoflavone, quercetin) and polysaccharides were quantified by ultra performance liquid chromatography (UPLC) and the resonance light scattering (RIL) method. The content of taxoids gave the highest level of 1.77 ± 0.38 mg·g-1 in January, and the lowest value of 0.61 ± 0.08 mg·g-1 in September. Unlike taxoids, the content of flavonoids was the highest in August. The content of polysaccharides reached peak value of 28.52 ± 0.57 mg·g-1 in September, which was two times higher than the lowest content of 9.39 ± 0.17 mg·g-1 in January. The contents of paclitaxel, 10-DAB, 10-DAT and polysaccharides significantly depended on meteorological parameters. The mean of minimum temperature (R = -0.61) and length of daylight (R = -0.60) were significantly correlated to 10-DAB content, while 10-DAT level showed significant correlation with length of daylight (R = -0.70) and relative humidity (R = 0.70). In addition, temperature had significantly negative effect on the content of paclitaxel and a significantly positive effect on that of polysaccharides. This study enriched the knowledge on the accumulation pattern of metabolites and could help us to determine the collecting time of T. wallichiana var. mairei for medicinal use.

Characteristics of Fine Particles in an Urban Atmosphere-Relationships with Meteorological Parameters and Trace Gases

Atmospheric fine particles (diameter < 1 μm) attract a growing global health concern and have increased in urban areas that have a strong link to nucleation, traffic emissions, and industrial emissions. To reveal the characteristics of fine particles in an industrial city of a developing country, two-year measurements of particle number size distribution (15.1 nm–661 nm), meteorological parameters, and trace gases were made in the city of Wuhan located in central China from June 2012 to May 2014. The annual average particle number concentrations in the nucleation mode (15.1 nm–30 nm), Aitken mode (30 nm–100 nm), and accumulation mode (100 nm–661 nm) reached 4923 cm⁻³, 12193 cm⁻³ and 4801 cm⁻³, respectively. Based on Pearson coefficients between particle number concentrations and meteorological parameters, precipitation and temperature both had significantly negative relationships with particle number concentrations, whereas atmospheric pressure was positively correlated with the particle number concentrations. The diurnal variation of number concentration in nucleation mode particles correlated closely with photochemical processes in all four seasons. At the same time, distinct growth of particles from nucleation mode to Aitken mode was only found in spring, summer, and autumn. The two peaks of Aitken mode and accumulation mode particles in morning and evening corresponded obviously to traffic exhaust emissions peaks. A phenomenon of “repeated, short-lived” nucleation events have been created to explain the durability of high particle concentrations, which was instigated by exogenous pollutants, during winter in a case analysis of Wuhan. Measurements of hourly trace gases and segmental meteorological factors were applied as proxies for complex chemical reactions and dense industrial activities. The results of this study offer reasonable estimations of particle impacts and provide references for emissions control strategies in industrial cities of developing countries.

Relative influence of meteorological conditions and aerosols on the lifetime of mesoscale convective systems

Using collocated measurements from geostationary and polar-orbital satellites over tropical continents, we provide a large-scale statistical assessment of the relative influence of aerosols and meteorological conditions on the lifetime of mesoscale convective systems (MCSs). Our results show that MCSs' lifetime increases by 3-24 h when vertical wind shear (VWS) and convective available potential energy (CAPE) are moderate to high and ambient aerosol optical depth (AOD) increases by 1 SD (1σ). However, this influence is not as strong as that of CAPE, relative humidity, and VWS, which increase MCSs' lifetime by 3-30 h, 3-27 h, and 3-30 h per 1σ of these variables and explain up to 36%, 45%, and 34%, respectively, of the variance of the MCSs' lifetime. AOD explains up to 24% of the total variance of MCSs' lifetime during the decay phase. This result is physically consistent with that of the variation of the MCSs' ice water content (IWC) with aerosols, which accounts for 35% and 27% of the total variance of the IWC in convective cores and anvil, respectively, during the decay phase. The effect of aerosols on MCSs' lifetime varies between different continents. AOD appears to explain up to 20-22% of the total variance of MCSs' lifetime over equatorial South America compared with 8% over equatorial Africa. Aerosols over the Indian Ocean can explain 20% of total variance of MCSs' lifetime over South Asia because such MCSs form and develop over the ocean. These regional differences of aerosol impacts may be linked to different meteorological conditions.

The association between the incidence of mumps and meteorological parameters in Taiwan

Mumps is caused by a paramyxovirus. It is an acute, but mild infectious disease. However, approximately 10% of patients with mumps can develop severe meningoencephalitis, disability, and death. Seasonal patterns in mumps vary across countries, but the reasons for this phenomenon remain unclear. The aim of this study was to assess the role of meteorological factors on mumps infection. We investigated the relationships between weather variability and the incidence of mumps in Taiwan using a Poisson regression analysis and case-crossover methodology. Between 2006 and 2011, 6,612 cases of mumps were reported to the Centers for Disease Control, Taiwan (Taiwan CDC). The incidence of mumps showed a significant seasonality in summertime (for oscillation, P < 0.001). The number of mumps started to increase at temperatures of 20°C (r(2) = 0.73, P < 0.001), and the case count of mumps began to decline when the temperatures were higher than approximately 25°C (r(2) = 0.24, p = 0.04), producing an inverted V-shaped relationship. Similarly, the number of mumps began to increase at a vapor pressure of 5-9 hPa (r(2) = 0.87, P < 0.005) and decreased at a vapor pressure higher than 25-29 hPa (r(2) = 0.21, p = 0.05). The number of mumps cases was positively associated with temperature and vapor pressure in the preceding period of the infection. In conclusion, this study showed that the occurrence of mumps is significantly associated with increasing temperature and vapor pressure in Taiwan. Therefore, these factors could be regarded as warning signals indicating the need to implement preventive measures.

Air pollution by allergenic spores of the genus Alternaria in the air of central and eastern Europe

Spores of the genus Alternaria belong to one of the most prevailing constituents of the air in all regions of the world. They form infectious inoculum of numerous plant species as well as severe inhaled allergies. The aim of this study was to compare the biological pollution with Alternaria spores of the air of 12 cities located in central and eastern Europe. The experiment was done in 2010 and it covered the territory of Latvia (LV), Lithuania (LT), Poland (PL) and Ukraine (UA). The spores were counted using an identical method and standard equipment (7-day Lanzoni volumetric sampler) followed by extensive statistical calculations. The timing of the day of maximum concentration changed mainly along the N-S direction and had a positive correlation with latitude. The most important factor determining the increase in Alternaria spore concentration was the temperature, whereas other weather parameters were not related or of low significance. Regardless of geographical location, the first phase of the season (0-0.9 % of Alternaria spores in the air) was the longest (up to 60 days) and the last (97.5 to 99 %) was the shortest (22 days or less). The means of daily concentrations of Alternaria spores ranged from 11 spores m(-3) in Klaipeda (LT, Baltic Sea coast) to 187 in Poznan (west PL, agricultural plain). The threshold value of 80 spores m(-3) that triggers the first allergy symptoms was exceeded in 8 to 86 days (Vinnitsa, UA, temperate continental, forest-steppes region). There were considerable differences between the highest number of spores per cubic metre of air, varying from 139 in the north (Klaipeda, LT) to 2,295 in central west (Poznan, PL). The biological pollution by Alternaria spores in several places of central and eastern Europe was high; the number of days exceeding the threshold value of 300 spores m(-3) connected with serious health problems of atopic people ranged from 0 to 1 on the north (LV, LT) to 29 in central west (Poznan, PL).

Prediction of the birch pollen season characteristics in Cracow, Poland using an 18-year data series

The aim of the study was to construct the model forecasting the birch pollen season characteristics in Cracow on the basis of an 18-year data series. The study was performed using the volumetric method (Lanzoni/Burkard trap). The 98/95 % method was used to calculate the pollen season. The Spearman's correlation test was applied to find the relationship between the meteorological parameters and pollen season characteristics. To construct the predictive model, the backward stepwise multiple regression analysis was used including the multi-collinearity of variables. The predictive models best fitted the pollen season start and end, especially models containing two independent variables. The peak concentration value was predicted with the higher prediction error. Also the accuracy of the models predicting the pollen season characteristics in 2009 was higher in comparison with 2010. Both, the multi-variable model and one-variable model for the beginning of the pollen season included air temperature during the last 10 days of February, while the multi-variable model also included humidity at the beginning of April. The models forecasting the end of the pollen season were based on temperature in March-April, while the peak day was predicted using the temperature during the last 10 days of March.

The effect of meteorological factors on airborne Betula pollen concentrations in Lublin (Poland)

The present study investigated the pattern of the birch atmospheric pollen seasons in Lublin in the period 2001-2010. Pollen monitoring was conducted using a Lanzoni VPPS 2000 sampler. The atmospheric pollen seasons were determined with the 98% method. Regression analysis was used to determine correlations between meteorological conditions and the pattern of the birch pollen season. On average, the birch pollen season started on 12 April, ended on 13 May, and lasted 32 days. The peak value and the Seasonal Pollen Index showed the greatest variation in particular years. All the seasons were right-skewed. During the study years, a trend was found towards earlier occurrence of the seasonal peak. Regression equations were developed for the following parameters of the atmospheric pollen season: start, duration, peak value and average pollen concentration during the season. The obtained model fit was at a level of 64-81%. Statistical analysis shows that minimum temperature of February and March and total rainfall in June in the year preceding pollen release have the greatest effect on the birch atmospheric pollen season in Lublin. Low temperatures in February promote the occurrence of high pollen concentrations.

Occurrence of Didymella ascospores in western and southern Poland in 2004-2006

The concentration of airborne Didymella spores has been investigated at two monitoring sites situated along the west-south transect in Poland (Szczecin, Kraków), i.e. from a height of 100 to 219 m, respectively, above sea level. The aerobiological monitoring of fungal spores was performed by means of two Lanzoni volumetric spore traps. The high Didymella spore numbers were observed at both cities in June, July and August. Statistically significant correlations have been found mainly between the Didymella spore concentrations in the air and the minimum air temperature and relative air humidity. The spore count of Didymella is determined by the diversity of local flora and weather conditions, especially by the relative air humidity. The identification of factors that influence and shape spore concentrations may significantly improve the current methods of allergy prevention.

Possible effects of changes in the meteorological state over semi-arid areas on the general well-being of weather-sensitive patients

BACKGROUND

The influence of the changes in atmospheric states, typical for areas close to big deserts, on general well-being of hypertensive persons was analyzed.

METHODS

Under test was the group of 20 hypertensive weather-sensitive patients; their blood pressure, pulse rate and appearance of 4 symptoms of discomfort sensations: arthritic pain, unjustified anxiety, severe headache and inexplicable tiredness- were registered. Symptoms are classified in ICD-9 code (780-790) and scored on a 4-point scale. Results were defined as positive (no departure from the range of normal values) or problematic; the daily number of the latter results was collected under the name "pathological reactions" NPR if at least two of these 7 checked symptoms (of one patient) were outside the normal range. Comparison of the current weather conditions with their means, questioning of patients and repeated examinations are used to gain information. The data was analyzed employing the SAS statistical software. Pearson and Spearman correlations were used, applied on the best and worst days, when a minimum and a maximum of pathological changes NPR in the patients' well-being were observed. The statistical significance was p < 0.05 in all cases.

RESULTS

~1500 medical observations and verbal statements were registered in the Primary Care Clinic (Be'er-Sheva, Israel) during 2001-2002. No meaning correlation was found between NPR and absolute values of temperature, humidity and atmospheric pressure. Variations in wind speed WS and direction were expressed in blood pressure changes and in exacerbation of discomfort of various degrees. Unfavorable conditions correspond to days with dominant desert air streams and to high WS, when NPR reaches 85.7%; during the days with prevalent sea breeze NPR was ≤22.9%. The role of wind direction in NPR occurrence is prevalent when WS > 4 m·s-1. The Spearman test gives higher correlation than Pearson test (ρ ~ 0.14, p < 0.03 against ρ ~ 0.1, p < 0.04).

CONCLUSIONS

NPR is more affected by the air streams than by absolute values of meteorological parameters. The method of this study might give to family doctors some additional tools to predict deterioration in general feelings of chronic patients and could be related to other health problems influenced by the meteorological environment.

Airborne Aspergillus and Penicillium in the atmosphere of Szczecin, (Poland) (2004-2009)

The investigation into airborne fungal spore concentrations was conducted in Szczecin (Poland) between 2004 and 2009. The objective of the studies was to determine a seasonal variation in concentrations of amerospores on the basis of meteorological parameters. The presence of spores in Szczecin was recorded using a volumetric method. Fungal spores were present in the air in high numbers in late summer and early autumn. The highest concentrations were noted in September, October and November. The peak period was recorded in August, September, October and November. The highest annual number of spores occurred in 2005 and 2007 and the lowest in 2006. High values of daily concentration of amerospores occurred during the afternoon and late at night. In 2005 and 2007 the late-night maximum was overdue about 1 or 2 h. For daily values of dew point temperature and relative humidity, the coefficients were positive, significant for p = 0.001 and ranged from 0.342 to 0.258. The average wind speed was positively correlated for p = 0.01 and the coefficient was 0.291. The similar relations were noted for hourly values of spore concentrations for p = 0.05, p = 0.01 and p = 0.001. For these spore types, the dew point temperature and relative humidity appeared to be the most influential factor.