Maritime accidents in the estuary of the River Seine from 2009-2019.

BACKGROUND: In confined waters, ships run a high risk of groundings, contact, sinkings and near misses. In such waters the maritime traffic is dense, the waterway is narrow, the depth is limited, and tides and currents are constantly changing. MATERIALS AND METHODS: From 2009-2019, 75 accidents were investigated in the estuary of the Seine. Weather conditions and perceived fatigue were studied. From May to June 2020, 114 seafarers, 34 pilots and 80 captains, responded to a questionnaire focusing on the use of Pilot Portable Units (PPU) and Electronic Chart Display Information Systems (ECDIS). RESULTS: The 75 accidents corresponded to an average of 6.8 ± 3.2 accidents per year. Groundings were the most frequent accidents (35%, n = 26) followed by contact accidents with the quayside (25%, n = 19), between ships or tugs while manoeuvring (8%, n = 6) or while sailing (1%, n = 1). There was no loss of vessels nor fatalities of crew members. In poor weather conditions, there were 76% more accidents than in normal conditions (4.4 ± 2.5 accidents/10,000 movements versus 2.5 ± 1.9 accidents/10,000 movements, p < 0.03). Almost all the accidents (96%) were related to human errors of judgment (81%), or negligence (53%), or both (39). Perceived fatigue was probably in cause in 6 accidents. Only 3 accidents were related to mechanical causes. Through the questionnaires, 69% of the pilots complained of difficulties in mastering the devices and software. They felt distracted by alarms which affected their attention while navigating. They requested training on a simulator. Concerning ship captains, 83% felt comfortable with ECDIS devices yet only 20% were able to configure the ECDIS correctly. CONCLUSIONS: In the Seine estuary, 75 accidents occurred within the 11 year-study. Risk factors were poor weather conditions and human error. PPU and ECDIS were considered as useful tools in the prevention of accidents. However, pilots and captains requested more thorough training in their use.

Assessing the relationship between malaria incidence levels and meteorological factors using cluster-integrated regression.

This paper introduces a novel approach to modeling malaria incidence in Nigeria by integrating clustering strategies with regression modeling and leveraging meteorological data. By decomposing the datasets into multiple subsets using clustering techniques, we increase the number of explanatory variables and elucidate the role of weather in predicting different ranges of incidence data. Our clustering-integrated regression models, accompanied by optimal barriers, provide insights into the complex relationship between malaria incidence and well-established influencing weather factors such as rainfall and temperature.We explore two models. The first model incorporates lagged incidence and individual-specific effects. The second model focuses solely on weather components. Selection of a model depends on decision-makers priorities. The model one is recommended for higher predictive accuracy. Moreover, our findings reveal significant variability in malaria incidence, specific to certain geographic clusters and beyond what can be explained by observed weather variables alone.Notably, rainfall and temperature exhibit varying marginal effects across incidence clusters, indicating their differential impact on malaria transmission. High rainfall correlates with lower incidence, possibly due to its role in flushing mosquito breeding sites. On the other hand, temperature could not predict high-incidence cases, suggesting that other factors other than temperature contribute to high cases.Our study addresses the demand for comprehensive modeling of malaria incidence, particularly in regions like Nigeria where the disease remains prevalent. By integrating clustering techniques with regression analysis, we offer a nuanced understanding of how predetermined weather factors influence malaria transmission. This approach aids public health authorities in implementing targeted interventions. Our research underscores the importance of considering local contextual factors in malaria control efforts and highlights the potential of weather-based forecasting for proactive disease management.

Vulnerability assessment of aerosol and climate variability for rice and maize yield using EO datasets for sustainable agriculture over India.

Human-generated aerosol pollution gradually modifies the atmospheric chemical and physical attributes, resulting in significant changes in weather patterns and detrimental effects on agricultural yields. The current study assesses the loss in agricultural productivity due to weather and anthropogenic aerosol variations for rice and maize crops through the analysis of time series data of India spanning from 1998 to 2019. The average values of meteorological variables like maximum temperature (TMAX), minimum temperature (TMIN), rainfall, and relative humidity, as well as aerosol optical depth (AOD), have also shown an increasing tendency, while the average values of soil moisture and fraction of absorbed photosynthetically active radiation (FAPAR) have followed a decreasing trend over that period. This study's primary finding is that unusual variations in weather variables like maximum and minimum temperature, rainfall, relative humidity, soil moisture, and FAPAR resulted in a reduction in rice and maize yield of approximately (2.55%, 2.92%, 2.778%, 4.84%, 2.90%, and 2.82%) and (5.12%, 6.57%, 6.93%, 6.54%, 4.97%, and 5.84%), respectively. However, the increase in aerosol pollution is also responsible for the reduction of rice and maize yield by 7.9% and 8.8%, respectively. In summary, the study presents definitive proof of the detrimental effect of weather, FAPAR, and AOD variability on the yield of rice and maize in India during the study period. Meanwhile, a time series analysis of rice and maize yields revealed an increasing trend, with rates of 0.888 million tons/year and 0.561 million tons/year, respectively, due to the adoption of increasingly advanced agricultural techniques, the best fertilizer and irrigation, climate-resilient varieties, and other factors. Looking ahead, the ongoing challenge is to devise effective long-term strategies to combat air pollution caused by aerosols and to address its adverse effects on agricultural production and food security.

Does behavior mediate the effect of weather on SARS-CoV-2 transmission? evidence from cell-phone data.

There is growing evidence that weather alters SARS-CoV-2 transmission, but it remains unclear what drives the phenomenon. One prevailing hypothesis is that people spend more time indoors in cooler weather, leading to increased spread of SARS-CoV-2 related to time spent in confined spaces and close contact with others. However, the evidence in support of that hypothesis is limited and, at times, conflicting. We use a mediation framework, and combine daily weather, COVID-19 hospital surveillance, cellphone-based mobility data and building footprints to estimate the relationship between daily indoor and outdoor weather conditions, mobility, and COVID-19 hospitalizations. We quantify the direct health impacts of weather on COVID-19 hospitalizations and the indirect effects of weather via time spent indoors away-from-home on COVID-19 hospitalizations within five Colorado counties between March 4th 2020 and January 31st 2021. We also evaluated the evidence for seasonal effect modification by comparing the results of all-season (using season as a covariate) to season-stratified models. Four weather conditions were associated with both time spent indoors away-from-home and 12-day lagged COVID-19 hospital admissions in one or more season: high minimum temperature (all-season), low maximum temperature (spring), low minimum absolute humidity (winter), and high solar radiation (all-season & winter). In our mediation analyses, we found evidence that changes in 12-day lagged hospital admissions were primarily via the direct effects of weather conditions, rather than via indirect effects by which weather changes time spent indoors away-from-home. Our findings do not support the hypothesis that weather impacted SARS-CoV-2 transmission via changes in mobility patterns during the first year of the pandemic. Rather, weather appears to have impacted SARS-CoV-2 transmission primarily via mechanisms other than human movement. We recommend further analysis of this phenomenon to determine whether these findings generalize to current SARS-CoV-2 transmission dynamics, as well as other seasonal respiratory pathogens.

Digital replica to unveil the impact of growing conditions on orange postharvest quality.

The postharvest end-quality of citrus is significantly impacted by pre-harvest factors such as weather, which varies among growing regions. Despite the importance of these factors, the influence of regional weather variations, such as variations in temperature, humidity, wind, vapor pressure deficit (VPD), and solar radiation on postharvest citrus quality, is largely unknown. This study aims to quantify this impact through a physics-driven digital replica of the entire value chain of Valencia oranges, from orchards in South Africa to retail in Europe. Predicted fruit properties data at harvest and hygrothermal sensor data from orchard to retail for different production regions are coupled to a physics-based fruit model to simulate key postharvest fruit quality metrics. These metrics include mass loss, chilling injury, fruit quality index (FQI), remaining shelf life (RSL), total soluble solids (TSS), and titratable acidity (TA). Our digital fruit model reveals that regional weather variability significantly affects fruit quality evolution when comparing data from Nelspruit, Letsitele, and Sunday's River Valley (SRV). The impact of weather variations is most pronounced in the temperate oceanic climate of SRV compared to the hotter climates of Letsitele and Nelspruit. Our findings indicate that differences in weather conditions between these growing regions impact postharvest mass loss, FQI, RSL, TSS, and TA of Valencia oranges at retail. The impact is up to 10% variation in mass loss and RSL, 4% in TSS, and 1% in TA among oranges grown in different regions. We show that temperature and humidity variations in the postharvest local transport of oranges between different regions largely increase mass loss by up to twofold, FQI by up to ~ 12%, and RSL by up to ~ 15% at retail. Our research also shows that weather temperature is the most important metric during fruit growth affecting various aspects of postharvest orange quality. This study offers valuable insights into the impact of regional weather variations on the quality of oranges available to consumers. These findings could help the citrus industry enhance growing practices, postharvest logistics, retail marketing, and cold chain strategies, thereby improving product quality and consumer satisfaction.

Investigating the relationship between extreme weather and cryptosporidiosis and giardiasis in Colorado: A multi-decade study using distributed-lag nonlinear models.

Environmentally-mediated protozoan diseases like cryptosporidiosis and giardiasis are likely to be highly impacted by extreme weather, as climate-related conditions like temperature and precipitation have been linked to their survival, distribution, and overall transmission success. Our aim was to investigate the relationship between extreme temperature and precipitation and cryptosporidiosis and giardiasis infection using monthly weather data and case reports from Colorado counties over a twenty-one year period. Data on reportable diseases and weather among Colorado counties were collected using the Colorado Electronic Disease Reporting System (CEDRS) and the Daily Surface Weather and Climatological Summaries (Daymet) Version 3 dataset, respectively. We used a conditional Poisson distributed-lag nonlinear modeling approach to estimate the lagged association (between 0 and 12-months) between relative temperature and precipitation extremes and the risk of cryptosporidiosis and giardiasis infection in Colorado counties between 1997 and 2017, relative to the risk found at average values of temperature and precipitation for a given county and month. We found distinctly different patterns in the associations between temperature extremes and cryptosporidiosis, versus temperature extremes and giardiasis. When maximum or minimum temperatures were high (90th percentile) or very high (95th percentile), we found a significant increase in cryptosporidiosis risk, but a significant decrease in giardiasis risk, relative to risk at the county and calendar-month mean. Conversely, we found very similar relationships between precipitation extremes and both cryptosporidiosis and giardiasis, which highlighted the prominent role of long-term (>8 months) lags. Our study presents novel insights on the influence that extreme temperature and precipitation can have on parasitic disease transmission in real-world settings. Additionally, we present preliminary evidence that the standard lag periods that are typically used in epidemiological studies to assess the impacts of extreme weather on cryptosporidiosis and giardiasis may not be capturing the entire relevant period.

Weather Variability and COPD: A Risk Estimation Identified a Vulnerable Sub-population in Hungary.

BACKGROUND/AIM: Chronic obstructive pulmonary disease (COPD) is a major public health concern, affecting over 200 million people worldwide in 2019. The prevalence of COPD has risen by 40% from 1990 to 2010 and continued to increase by 13% from 2010 to 2019, causing over 3 million deaths globally in 2019, ranking it as the third leading cause of death. This study explored how daily weather changes relate to the number of COPD-related emergency department (ED) visits. MATERIALS AND METHODS: We collected data on daily COPD-related ED visits in 2017 in Pécs along with corresponding meteorological data to analyze this connection. RESULTS: High diurnal temperature range (DTR) and day-to-day variability in dew point were linked to a 4.5% increased risk of more COPD-related ED visits. Notably, DTR had a stronger impact on males, contributing to a 6.3% increase, while dew point variability significantly affected males with an odds ratio (OR) of 1.083. (OR=1.083). Stratifying by age revealed heightened risks for those aged 30-39 (43.5% increase) and 50-59 (7.6% increase). Females aged 30-39 and 50-59 faced elevated risks of 42.7% and 9.1%, respectively, whereas males aged 60-69 showed a 9.8% increase. CONCLUSION: Our findings highlight the influence of weather variations on COPD-related ED visits, with nuanced effects based on age and sex.

Enhancement of ANN-based wind power forecasting by modification of surface roughness parameterization over complex terrain.

Wind energy plays an important role in the sustainable energy transition towards a low-carbon society. Proper assessment of wind energy resources and accurate wind energy prediction are essential prerequisites for balancing electricity supply and demand. However, these remain challenging, especially for onshore wind farms over complex terrains, owing to the interplay between surface heterogeneities and intermittent turbulent flows in the planetary boundary layer. This study aimed to improve wind characteristic assessment and medium-term wind power forecasts over complex hilly terrain using a numerical weather prediction (NWP) model. The NWP model reproduced the wind speed distribution, duration, and spatio-temporal variabilities of the observed hub-height wind speed at 24 wind turbines in onshore wind farms when incorporating more realistic surface roughness effects, such as the subgrid-scale topography, roughness sublayer, and canopy height. This study also emphasizes the good features for machine learning that represent heterogeneities in the surface roughness elements in the atmospheric model. We showed that medium-term forecasting using the NWP model output and a simple artificial neural network (ANN) improved day-ahead wind power forecasts by 14% in terms of annual normalized mean absolute error. Our results suggest that better parameterizations of surface friction in atmospheric models are important for wind power forecasting and resource assessment using NWP models, especially when combined with machine learning techniques, and shed light on onshore wind power forecasting and wind energy assessment in mountainous regions.

Quantitative fecal source characterization of urban municipal storm sewer system outfall 'wet' and 'dry' weather discharges.

Urban areas are built environments containing substantial amounts of impervious surfaces (e.g., streets, sidewalks, roof tops). These areas often include elaborately engineered drainage networks designed to collect, transport, and discharge untreated stormwater into local surface waters. When left uncontrolled, these discharges may contain unsafe levels of fecal waste from sources such as sanitary sewage and wildlife even under dry weather conditions. This study evaluates paired measurements of host-associated genetic markers (log10 copies per reaction) indicative of human (HF183/BacR287 and HumM2), ruminant (Rum2Bac), canine (DG3), and avian (GFD) fecal sources, 12-hour cumulative precipitation (mm), four catchment land use metrics determined by global information system (GIS) mapping, and Escherichia coli (MPN/100 ml) from seven municipal separate storm sewer system outfall locations situated at the southern portion of the Anacostia River Watershed (District of Columbia, U.S.A.). A total of 231 discharge samples were collected twice per month (n = 24 sampling days) and after rain events (n = 9) over a 13-month period. Approximately 50 % of samples (n = 116) were impaired, exceeding the local E. coli single sample maximum of 2.613 log10 MPN/100 ml. Genetic quality controls indicated the absence of amplification inhibition in 97.8 % of samples, however 14.7 % (n = 34) samples showed bias in DNA recovery. Of eligible samples, quantifiable levels were observed for avian (84.1 %), human (57.4 % for HF183/BacR287 and 40 % for HumM2), canine (46.7 %), and ruminant (15.9 %) host-associated genetic markers. Potential links between paired measurements are explored with a recently developed Bayesian qPCR censored data analysis approach. Findings indicate that human, pet, and urban wildlife all contribute to storm outfall discharge water quality in the District of Columbia, but pollutant source contributions vary based on 'wet' and 'dry' conditions and catchment land use, demonstrating that genetic-based fecal source identification methods combined with GIS land use mapping can complement routine E. coli monitoring to improve stormwater management in urban areas.

A modest increase in fire weather overcomes resistance to fire spread in recently burned boreal forests.

Recently burned boreal forests have lower aboveground fuel loads, generating a negative feedback to subsequent wildfires. Despite this feedback, short-interval reburns (≤20 years between fires) are possible under extreme weather conditions. Reburns have consequences for ecosystem recovery, leading to enduring vegetation change. In this study, we characterize the strength of the fire-fuel feedback in recently burned Canadian boreal forests and the weather conditions that overwhelm resistance to fire spread in recently burned areas. We used a dataset of daily fire spread for thousands of large boreal fires, interpolated from remotely sensed thermal anomalies to which we associated local weather from ERA5-Land for each day of a fire's duration. We classified days with >3 ha of fire growth as spread days and defined burned pixels overlapping a fire perimeter ≤20 years old as short-interval reburns. Results of a logistic regression showed that the odds of fire spread in recently burned areas were ~50% lower than in long-interval fires; however, all Canadian boreal ecozones experienced short-interval reburning (1981-2021), with over 100,000 ha reburning annually. As fire weather conditions intensify, the resistance to fire spread declines, allowing fire to spread in recently burned areas. The weather associated with short-interval fire spread days was more extreme than the conditions during long-interval spread, but overall differences were modest (e.g. relative humidity 2.6% lower). The frequency of fire weather conducive to short-interval fire spread has significantly increased in the western boreal forest due to climate warming and drying (1981-2021). Our results suggest an ongoing degradation of fire-fuel feedbacks, which is likely to continue with climatic warming and drying.

The Joint Effects of Thunderstorms and Power Outages on Respiratory-Related Emergency Visits and Modifying and Mediating Factors of This Relationship.

BACKGROUND: While limited studies have evaluated the health impacts of thunderstorms and power outages (POs) separately, few have assessed their joint effects. We aimed to investigate the individual and joint effects of both thunderstorms and POs on respiratory diseases, to identify disparities by demographics, and to examine the modifications and mediations by meteorological factors and air pollution. METHODS: Distributed lag nonlinear models were used to examine exposures during three periods (i.e., days with both thunderstorms and POs, thunderstorms only, and POs only) in relation to emergency department visits for respiratory diseases (2005-2018) compared to controls (no thunderstorm/no PO) in New York State (NYS) while controlling for confounders. Interactions between thunderstorms and weather factors or air pollutants on health were assessed. The disparities by demographics and seasons and the mediative effects by particulate matter with aerodynamic diameter ≤2.5µm (PM2.5) and relative humidity (RH) were also evaluated. RESULTS: Thunderstorms and POs were independently associated with total and six subtypes of respiratory diseases in NYS [highest risk ratio (RR) = 1.12; 95% confidence interval (CI): 1.08, 1.17], but the impact was stronger when they co-occurred (highest RR = 1.44; 95% CI: 1.22, 1.70), especially during grass weed, ragweed, and tree pollen seasons. The stronger thunderstorm/PO joint effects were observed on chronic obstructive pulmonary diseases, bronchitis, and asthma (lasted 0-10 d) and were higher among residents who lived in rural areas, were uninsured, were of Hispanic ethnicity, were 6-17 or over 65 years old, and during spring and summer. The number of comorbidities was significantly higher by 0.299-0.782/case. Extreme cold/heat, high RH, PM2.5, and ozone concentrations significantly modified the thunderstorm-health effect on both multiplicative and additive scales. Over 35% of the thunderstorm effects were mediated by PM2.5 and RH. CONCLUSION: Thunderstorms accompanied by POs showed the strongest respiratory effects. There were large disparities in thunderstorm-health associations by demographics. Meteorological factors and air pollution levels modified and mediated the thunderstorm-health effects. https://doi.org/10.1289/EHP13237.

Zoonotic spillover and extreme weather events drive the global outbreaks of airborne viral emerging infectious diseases.

Outbreaks of airborne viral emerging infectious diseases (EIDs) cause an increasing burden on global public health, particularly with a backdrop of intensified climate change. However, infection sources and drivers for outbreaks of airborne viral EIDs remain unknown. Here, we aim to explore the driving mechanisms of outbreaks based on the one health perspective. Outbreak information for 20 types of airborne viral EIDs was collected from the Global Infectious Disease and Epidemiology Network database and a systematic literature review. Four statistically significant and high-risk spatiotemporal clusters for airborne viral EID outbreaks were identified globally using multivariate scan statistic tests. There were 112 outbreaks with clear infection sources, and zoonotic spillover was the most common source (95.54%, 107/112). Since 1970, the majority of outbreaks occurred in healthcare facilities (24.82%), followed by schools (17.93%) and animal-related settings (15.93%). Significant associations were detected between the number of earthquakes, storms, duration of floods, and airborne viral EIDs' outbreaks using a case-crossover study design and multivariable conditional logistic regression. These findings implied that zoonotic spillover and extreme weather events are driving global outbreaks of airborne viral EIDs, and targeted prevention and control measures should be made to reduce the airborne viral EIDs burden.

Estimation of rice yield using multivariate analysis techniques based on meteorological parameters.

This study aims to develop predictive models for rice yield by applying multivariate techniques. It utilizes stepwise multiple regression, discriminant function analysis and logistic regression techniques to forecast crop yield in specific districts of Haryana. The time series data on rice crop have been divided into two and three classes based on crop yield. The yearly time series data of rice yield from 1980-81 to 2020-21 have been taken from various issues of Statistical Abstracts of Haryana. The study also utilized fortnightly meteorological data sourced from the Agrometeorology Department of CCS HAU, India. For comparing various predictive models' performance, evaluation of measures like Root Mean Square Error, Predicted Error Sum of Squares, Mean Absolute Deviation and Mean Absolute Percentage Error have been used. Results of the study indicated that discriminant function analysis emerged as the most effective to predict the rice yield accurately as compared to logistic regression. Importantly, the research highlighted that the optimum time for forecasting the rice yield is 1 month prior to the crops harvesting, offering valuable insight for agricultural planning and decision-making. This approach demonstrates the fusion of weather data and advanced statistical techniques, showcasing the potential for more precise and informed agricultural practices.

[Dependence of medical rehabilitation effectiveness of patients with somatic diseases and meteopathology on features of weather-forming factors of the Moscow region]. / Zavisimost' effektivnosti meditsinskoi reabilitatsii patsientov s somaticheskimi zabolevaniyami i meteopatologiei ot osobennostei pogodoobrazuyushchikh faktorov Moskovskogo regiona.

Climate change and formation of regional weather features increase both direct (immediate effects of extremal temperature and other weather and climatic anomalies) and indirect (environmental deterioration, etc.) consequences of global climate change. The development of investigations of medical weather assessment, medical and meteorological forecasting system, the use of treatment and preventive measures give the opportunity to prepare for weather biotropic conditions and reduce weather-conditioned exacerbations. OBJECTIVE: To assess the clinical effectiveness of non-drug comprehensive methods, including balneotherapy and physiotherapy, in patients with somatic diseases, complicated by increased meteosensitivity, depending on the features of weather conditions in the Moscow region. MATERIAL AND METHODS: The study included 120 patients diagnosed with «osteoarthrosis/osteoarthritis¼ with predominant hip and knee joint damage. The majority of patients had comorbidities, namely hypertensive disease (67.2%), ischemic heart disease (32.8%), chronic obstructive pulmonary disease and bronchial asthma (10.8%), grade II-III obesity (10%) and compensated diabetes mellitus (9.2%). The severity and main symptoms of the meteopathic reactions' manifestation were assessed by questionnaires consisting of three blocks, HAM, SF-36 tests and psychological stress scale. Medical and meteorological assessment of weather conditions in Moscow included analysis of the main weather-forming factors for 4 main synoptic observations in 10-minute mode for current and predictive 2 days, as well as daily characteristics of solar activity. Treatment methods included alternating magnetic field (AMF) procedures, general sodium chloride baths, massage and rehabilitation exercises (RE) (1st group); AMF, «dry¼ carbon dioxide baths, applications with brine on the affected joints and RE (2nd group); AMF, «dry¼ radon baths, applications with brine and RE (3rd group). RESULTS: The conducted studies have revealed the trigger role of most biotropic combinations of weather-forming factors that provoke exacerbation in patients with joint diseases. Sodium chloride, «dry¼ carbon dioxide and radon baths combined with AMF, applications with brine on the affected joints and RE are pathogenetically justified and contribute to increase of adaptive potential, functional reserves of the body and provide significantly high (p<0.05) meteocorrective action. CONCLUSION: The obtained results can be used for rehabilitation of patients with joint diseases complicated by increased meteosensitivity.

Exploring the influence of weather variability and climate change on health outcomes in people living with dementia: A scoping review protocol.

Environmental factors resulting from climate change and air pollution are risk factors for many chronic conditions including dementia. Although research has shown the impacts of air pollution in terms of cognitive status, less is known about the association between climate change and specific health-related outcomes of older people living with dementia. In response, we outline a scoping review protocol to systematically review the published literature regarding the evidence of climate change, including temperature and weather variability, on health-related quality of life, morbidity, mobility, falls, the utilization of health resources, and mortality among older adults living with dementia. This scoping review will be guided by the framework proposed by Arksey and O'Malley. Electronic search (Medline, Embase, PsycINFO, CINAHL, Scopus, Web of Science) using relevant subject headings and synonyms for two concepts (older people with dementia, weather/ climate change). No publication date or other restrictions will be applied to the search strategy. No language restriction will be applied in order to understand the impact of non-English studies in the literature. Eligible studies must include older adults (65+years) with dementia living in the community and investigate the impacts of climate change and/or weather on their health-related quality of life, morbidity, mobility, falls, use of health resources and mortality. Two independent reviewers will screen abstracts and select those for a full-text review, perform these reviews, select articles for retention, and extract data from them in a standardized manner. This data will then be synthesized and interpreted. OSF registration: DOI: 10.17605/OSF.IO/YRFM8.

Linking weather and health outcomes: Examining the potential influences of weather factors and particulate matter pollution on adverse pregnancy outcomes in the Kavre district, Nepal.

INTRODUCTION: Adverse pregnancy outcomes (APOs) include stillbirth, preterm birth, and low birthweight (LBW). Studies exploring the impact of weather factors and air pollution on APOs are scarce in Nepal. We examined the impacts of prenatal exposure to temperature, precipitation, and air pollution (PM2.5) on APOs among women living in Kavre, Nepal. METHODS: We conducted a hospital and rural health centers-based historical cohort study that included health facility birth records (n = 1716) from the Nepali fiscal year 2017/18 through 2019/20. We linked health records to temperature, precipitation, and PM2.5 data for Kavre for the six months preceding each birth. A random intercept model was used to analyze birthweight, while a composite APO variable, was analyzed using multivariable logistic regression in relation to environmental exposures. RESULTS: The proportion of LBW (<2500 gm), preterm birth (babies born alive before 37 weeks of gestation), and stillbirth was 13%, 4.3%, and 1.5%, respectively, in this study. Overall, around 16% of the study participants had one or more APOs. Total precipitation (ß: 0.17, 95% CI 0.01 to 0.33, p = 0.03) had a positive effect on birthweight in the wetter season. Negative effects for mean maximum (ß: 33.37, 95% CI -56.68 to -10.06, p = 0.005), mean (ß: 32.35, 95% CI -54.44 to -10.27, p = 0.004), and mean minimum temperature (ß: 29.28, 95% CI -49.58 to -8.98, p = 0.005) on birthweight was also observed in the wetter season. CONCLUSION: A positive effect of temperature (mean maximum, mean, and mean minimum) and total precipitation on birthweight was found in the wetter season. This study emphasizes the need for future research using larger cohorts to elucidate these complex relationships in Nepal.

Influence of meteorological variables and air pollutants on fog/smog formation in seven major cities of Indo-Gangetic Plain.

The Indo-Gangetic Plains (IGP) of the Indian subcontinent during winters experience widespread fog episodes. The low visibility is not only attributed to meteorological conditions but also to the increased pollution levels in the region. The study was carried out for Tier 1 and Tier II cities of the IGP of India, including Kolkata, Amritsar, Patiala, Hisar, Delhi, Patna, and Lucknow. This work analyzes data from 1990 to 2023 (33 years) employing the Mann-Kendall-Theil-Sen slope to determine the trends in fog occurrences and the relation between fog and meteorological parameters using multiple linear regressions. Furthermore, identifying the most relevant fog (visibility)-impacting factors from a set of both meteorological factors and air pollutants using step-wise regression. All cities indicated trend in the number of foggy days except for Kolkata. The multiple regression analysis reveals relatively low associations between fog occurrences and meteorological factors (30 to 59%), although the association was stronger when air pollution levels were considered (60 to 91%). Relative humidity, PM2.5, and PM10 have the most influence on fog formation. The study provides comprehensive insights into fog trends by incorporating meteorological data and air pollution analysis. The findings highlight the significance of acknowledging meteorological and pollution factors to understand and mitigate the impacts of reduced visibility. Hence, this information can guide policymakers, urban planners, and environmental management agencies in developing effective strategies to manage fog-related risks and improve air quality.

Influence of sticky trap color, host plant species, and weather factors on the population dynamics of thrips species in Southern Ghana.

It is essential to correctly identify and keep track of the abundance of thrips species on infested host crops to understand their population dynamics and implement control measures promptly. The current study was conducted to evaluate the performance of sticky traps in monitoring thrips species in exporters' eggplant and chili farms and to assess the impact of weather factors on thrips population dynamics. Thrips species were monitored using blue, yellow, and white sticky traps on chili and eggplant farms in Tuba, respectively, in 2020 and 2021. Each field was divided into 8 blocks, and in each replicate, all colors representing 3 treatments were randomly tied to stakes at the center of the respective crop. Data loggers were installed to record hourly weather variables. Three thrips species [Thrips parvispinus Karny (Thysanoptera: Thripidae), Franklinella schultzei Trybom (Thysanoptera: Thripidae), and Thrips tabaci Lindeman (Thysanoptera: Thripidae)] were identified from both farms and the different species showed varied attractiveness to trap color for both seasons, with white proving more attractive to T. parvispinus. The population dynamics of the species varied significantly with the season and weather but not with the crop. Optimum temperatures (28-31 °C) and relative humidity (60%-78%) showed a positive linear relationship between the trapped insects with temperature and RH, while extreme temperatures (35 °C) negatively affected their abundance. All sticky trap colors attracted several nontarget organisms; however, yellow colors had higher populations, including the predator, Orius insidiosus. White sticky traps are recommended for inclusion in the country-wide monitoring for thrips, especially T. parvispinus.

Association between meteorological variations and the superspreading potential of SARS-CoV-2 infections.

BACKGROUND: While many investigations examined the association between environmental covariates and COVID-19 incidence, none have examined their relationship with superspreading, a characteristic describing very few individuals disproportionally infecting a large number of people. METHODS: Contact tracing data of all the laboratory-confirmed COVID-19 cases in Hong Kong from February 16, 2020 to April 30, 2021 were used to form the infection clusters for estimating the time-varying dispersion parameter (kt), a measure of superspreading potential. Generalized additive models with identity link function were used to examine the association between negative-log kt (larger means higher superspreading potential) and the environmental covariates, adjusted with mobility metrics that account for the effect of social distancing measures. RESULTS: A total of 6,645 clusters covering 11,717 cases were reported over the study period. After centering at the median temperature, a lower ambient temperature at 10th percentile (18.2 °C) was significantly associated with a lower estimate of negative-log kt (adjusted expected change: -0.239 [95 % CI: -0.431 to -0.048]). While a U-shaped relationship between relative humidity and negative-log kt was observed, an inverted U-shaped relationship with actual vapour pressure was found. A higher total rainfall was significantly associated with lower estimates of negative-log kt. CONCLUSIONS: This study demonstrated a link between meteorological factors and the superspreading potential of COVID-19. We speculated that cold weather and rainy days reduced the social activities of individuals minimizing the interaction with others and the risk of spreading the diseases in high-risk facilities or large clusters, while the extremities of relative humidity may favor the stability and survival of the SARS-CoV-2 virus.