Rising frequency of ozone-favorable synoptic weather patterns contributes to 2015-2022 ozone increase in Guangzhou.

Objective weather classification methods have been extensively applied to identify dominant ozone-favorable synoptic weather patterns (SWPs), however, the consistency of different classification methods is rarely examined. In this study, we apply two widely-used objective methods, the self-organizing map (SOM) and K-means clustering analysis, to derive ozone-favorable SWPs at four Chinese megacities in 2015-2022. We find that the two algorithms are largely consistent in recognizing dominant ozone-favorable SWPs for four Chinese megacities. In the case of classifying six SWPs, the derived circulation fields are highly similar with a spatial correlation of 0.99 between the two methods, and the difference in the mean frequency of each SWP is less than 7%. The six dominant ozone-favorable SWPs in Guangzhou are all characterized by anomaly higher radiation and temperature, lower cloud cover, relative humidity, and wind speed, and stronger subsidence compared to climatology mean. We find that during 2015-2022, the occurrence of ozone-favorable SWPs days increases significantly at a rate of 3.2 day/year, faster than the increases in the ozone exceedance days (3.0 day/year). The interannual variability between the occurrence of ozone-favorable SWPs and ozone exceedance days are generally consistent with a temporal correlation coefficient of 0.6. In particular, the significant increase in ozone-favorable SWPs in 2022, especially the Subtropical High type which typically occurs in September, is consistent with a long-lasting ozone pollution episode in Guangzhou during September 2022. Our results thus reveal that enhanced frequency of ozone-favorable SWPs plays an important role in the observed 2015-2022 ozone increase in Guangzhou.

Influence of weather conditions and plant growth on populations of the solenopsis mealybug (Phenacoccus solenopsis Tinsley) infesting maize plants.

The cotton or solenopsis mealybug, Phenacoccus solenopsis (Tinsley, 1898) (Hemiptera: Pseudococcidae), infests various host plants in Egypt. A study was conducted to observe the incidence of mealybugs and the possible influences of meteorological variables and plant age on the insect population of maize (single-hybrid 168 yellow maize cultivar) plants in Esna district, Luxor governorate, Egypt, in two consecutive seasons (2021 and 2022). P. solenopsis infested maize plants from the 3rd week of June to harvest, and had three peaks of seasonal incidence/season namely; in the 1st week of June in the 3rd/4th week of July, and the 2nd week of August. Similarly, there were three peaks in the percent of infestations per season. In the first season, the average population density of P. solenopsis per sample was 174.04 ± 16.93 individuals, and in the second season, 156.72 ± 14.28 individuals. The most favorable climate for P. solenopsis population increase and infestation occurred in August in the first season and in September in the second season, while June was less suitable in both growing seasons (as estimated by weekly surveys). The combined effects of weather conditions and plant age are significantly related to the estimates of P. solenopsis populations, with an explained variance (E.V.) of 93.18 and 93.86%, respectively, in the two seasons. In addition, their influences explained differences in infestation percentages of 93.30 and 95.54%, respectively, in the two seasons. Maize plant age was the most effective factor in determining changes in P. solenopsis population densities in each season. The mean daily minimum temperature in the first season and mean daily dew point in the second season were the most important factors affecting the percent changes in infestation. However, in both seasons, the mean daily maximum temperature was the least effective variable in population and infestation variation. This study paves the way for monitoring and early detection of mealybugs in maize; as well as the optimal climatic conditions for its development.

Studying the factors that affect urban road noise-case study: El-Tahrir Street, Al-Dokki Cairo, Egypt.

Expanding road networks to accommodate various activities has significantly increased urban noise pollution, adversely affecting human health and quality of life. Numerous factors influence the noise level in urban areas, including road characteristics, traffic characteristics, surrounding buildings, and weather conditions. While previous studies have considered many of these factors individually, this study aims to integrate all relevant variables to comprehensively monitor and analyze their combined effects on noise levels. The objective is to determine the most influential factors that could be incorporated into effective noise reduction strategies. This research focuses on Cairo, one of the most densely populated cities in the world, where high noise levels are a persistent issue. A detailed case study of Tahrir Street in Dokki, Cairo, provides the basis for this investigation. One of the most crowded areas is El-Tahrir Street in Al-Dokki, which was selected as a case study in this research. This area experiences high traffic volume, with up to 1700 vehicles passing through within a 15-min interval. This significant traffic volume is the primary driver of the elevated noise levels in the area. Traffic and noise level monitoring was conducted using a field survey using the sound level meter. Consequently, a statistical analysis was conducted to investigate the correlation between different factors and the noise level and determine the most influential factors. The study revealed that traffic volume and congestion are the most significant factors influencing noise levels on Tahrir Street, exhibiting strong positive correlations (R = 0.38). Additionally, the study found an inverse relationship between vehicle speed and noise level due to high traffic volumes and identified that building characteristics and wind direction also play roles, albeit to a lesser extent.

Spatiotemporal patterns and association with climate for malaria elimination in Lao PDR: a hierarchical modelling analysis with two-step Bayesian model selection.

BACKGROUND: The government of Lao PDR has increased efforts to control malaria transmission in order to reach its national elimination goal by 2030. Weather can influence malaria transmission dynamics and should be considered when assessing the impact of elimination interventions but this relationship has not been well characterized in Lao PDR. This study examined the space-time association between climate variables and Plasmodium falciparum and Plasmodium vivax malaria incidence from 2010 to 2022. METHODS: Spatiotemporal Bayesian modelling was used to investigate the monthly relationship, and model selection criteria were used to evaluate the performance of the models and weather variable specifications. As the malaria control and elimination situation was spatially and temporally dynamic during the study period, the association was examined annually at the provincial level. RESULTS: Malaria incidence decreased from 2010 to 2022 and was concentrated in the southern regions for both P. falciparum and P. vivax. Rainfall and maximum humidity were identified as most strongly associated with malaria during the study period. Rainfall was associated with P. falciparum incidence in the north and central regions during 2010-2011, and with P. vivax incidence in the north and central regions during 2012-2015. Maximum humidity was persistently associated with P. falciparum and P. vivax incidence in the south. CONCLUSIONS: Malaria remains prevalent in Lao PDR, particularly in the south, and the relationship with weather varies between regions but was strongest for rainfall and maximum humidity for both species. During peak periods with suitable weather conditions, vector control activities and raising public health awareness on the proper usage of intervention measures, such as indoor residual spraying and personal protection, should be prioritized.

Associations between meteorological factors and COVID-19: a global scoping review.

Background: Many respiratory viruses and their associated diseases are sensitive to meteorological factors. For SARS-CoV-2 and COVID-19, evidence on this sensitivity is inconsistent. Understanding the influence of meteorological factors on SARS-CoV-2 transmission and COVID-19 epidemiology can help to improve pandemic preparedness. Objectives: This review aimed to examine the recent evidence about the relation between meteorological factors and SARS-CoV-2/COVID-19. Methods: We conducted a global scoping review of peer-reviewed studies published from January 2020 up to January 2023 about the associations between temperature, solar radiation, precipitation, humidity, wind speed, and atmospheric pressure and SARS-CoV-2/COVID-19. Results: From 9,156 initial records, we included 474 relevant studies. Experimental studies on SARS-CoV-2 provided consistent evidence that higher temperatures and solar radiation negatively affect virus viability. Studies on COVID-19 (epidemiology) were mostly observational and provided less consistent evidence. Several studies considered interactions between meteorological factors or other variables such as demographics or air pollution. None of the publications included all determinants holistically. Discussion: The association between short-term meteorological factors and SARS-CoV-2/COVID-19 dynamics is complex. Interactions between environmental and social components need further consideration. A more integrated research approach can provide valuable insights to predict the dynamics of respiratory viruses with pandemic potential.

Data analysis evidence beyond correlation of a possible causal impact of weather on the COVID-19 spread, mediated by human mobility.

Previous correlative and modeling approaches indicate influences of environmental factors on COVID-19 spread through atmospheric conditions' impact on virus survival and transmission or host susceptibility. However, causal connections from environmental factors to the pandemic, mediated by human mobility, received less attention. We use the technique of Convergent Cross Mapping to identify the causal connections, beyond correlation at the country level, between pairs of variables associated with weather conditions, human mobility, and the number of COVID-19 cases for 32 European states. Here, we present data-based evidence that the relatively reduced number of cases registered in Northern Europe is related to the causal impact of precipitation on people's decision to spend more time at home and that the relatively large number of cases observed in Southern Europe is linked to people's choice to spend time outdoors during warm days. We also emphasize the channels of the significant impact of the pandemic on human mobility. The weather-human mobility connections inferred here are relevant not only for COVID-19 spread but also for any other virus transmitted through human interactions. These results may help authorities and public health experts contain possible future waves of the COVID-19 pandemic or limit the threats of similar human-to-human transmitted viruses.

Short- and long-term weather prediction based on a hybrid of CEEMDAN, LMD, and ANN.

Agriculture is one of the major economic sectors in Africa, and it predominantly depends on the climate. However, extreme climate changes do have a negative impact on agricultural production. The damage resulting from extreme climate change can be mitigated if farmers have access to accurate weather forecasts, which can enable them to make the necessary adjustments to their farming practices. To improve weather prediction amidst extreme climate change, we propose a novel prediction model based on a hybrid of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), local mean decomposition (LMD), and artificial neural networks (NN). A detailed comparison of the performance metrics for the short- and long-term prediction results with other prediction models reveals that the three-phase hybrid CEEMDAN-LMD-NN model is optimal in terms of the evaluation metrics used. The study's findings demonstrate the efficiency of the three-phase hybrid CEEMDAN-LMD-NN prediction model in decision-system design, particularly for large-scale commercial farmers, small-holder farmers, and the agricultural index insurance industry that require reliable forecasts generated at multi-step horizons.

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.

Forecasters, emergency managers, and residents: Building better risk communication.

The US National Weather Service (NWS) and emergency managers (EMs) around the country are tasked with communicating severe weather information to the public. Frequent interaction between professionals and residents is essential to building effective partnerships. This paper investigates these interactions and also explores the perspectives of NWS forecasters, EMs, and rural residents related to the efficacy of warning communication, message understanding, preferred platforms, and engagement in protective actions. Data for this study were collected through three original survey instruments that were directed to NWS forecasters and EMs across the country, and residents in four rural communities. Findings reveal that residents generally understand warning messages and generally feel tornado risk communication is effective in their communities. However, residents do not appear to have a plan of action formulated prior to a warning and are, therefore, making, rather than implementing, a plan when warning is issued. This study gives rural residents a voice in the warning communication process and a chance for forecasters and EMs to gain valuable information as they better plan to serve these communities.

Validation of Citizen Science Meteorological Data: Can They Be Considered a Valid Help in Weather Understanding and Community Engagement?

Citizen science has emerged as a potent approach for environmental monitoring, leveraging the collective efforts of volunteers to gather data at unprecedented scales. Within the framework of the I-CHANGE project, MeteoTracker, a citizen science initiative, was employed to collect meteorological measurements. Through MeteoTracker, volunteers contributed to a comprehensive dataset, enabling insights into local weather patterns and trends. This paper presents the analysis and the results of the validation of such observations against the official Italian civil protection in situ weather network, demonstrating the effectiveness of citizen science in generating valuable environmental data. The work discusses the methodology employed, including data collection and statistical analysis techniques, i.e., time-series analysis, spatial and temporal interpolation, and correlation analysis. The overall analysis highlights the high quality and reliability of citizen-generated data as well as the strengths of the MeteoTracker platform. Furthermore, our findings underscore the potential of citizen science to augment traditional monitoring efforts, inform decision-making processes in environmental research and management, and improve the social awareness about environmental and climate issues.

Weather-related changes in the dehydration of respiratory droplets on surfaces bolster bacterial endurance.

HYPOTHESIS: The study shows for the first time a fivefold difference in the survivability of the bacterium Pseudomonas Aeruginosa (PA) in a realistic respiratory fluid droplet on fomites undergoing drying at different environmental conditions. For instance, in 2023, the annual average outdoor relative humidity (RH) and temperature in London (UK) is 71 % and 11 °C, whereas in New Delhi (India), it is 45 % and 26 °C, showing that disease spread from fomites could have a demographic dependence. Respiratory fluid droplet ejections containing pathogens on inanimate surfaces are crucial in disease spread, especially in nosocomial settings. However, the interplay between evaporation dynamics, internal fluid flow and precipitation and their collective influence on the distribution and survivability of pathogens at different environmental conditions are less known. EXPERIMENTS: Shadowgraphy imaging is employed to study evaporation, and optical microscopy imaging is used for precipitation dynamics. Micro-particle image velocimetry (MicroPIV) measurements reveal the internal flow dynamics. Confocal imaging of fluorescently labelled PA elucidates the bacterial distribution within the deposits. FINDINGS: The study finds that the evaporation rate is drastically impeded during drying at elevated solutal concentrations, particularly at high RH and low temperature conditions. MicroPIV shows reduced internal flow under high RH and low temperature (low evaporation rate) conditions. Evaporation rate influences crystal growth, with delayed efflorescence and extending crystallization times. PA forms denser peripheral arrangements under high evaporation rates and shows a fivefold increase in survivability under low evaporation rates. These findings highlight the critical impact of environmental conditions on pathogen persistence and disease spread from inanimate surfaces.

Exploratory analysis of injury severity under different levels of driving automation (SAE Levels 2 and 4) using multi-source data.

Vehicles equipped with automated driving capabilities have shown potential to improve safety and operations. Advanced driver assistance systems (ADAS) and automated driving systems (ADS) have been widely developed to support vehicular automation. Although the studies on the injury severity outcomes that involve automated vehicles are ongoing, there is limited research investigating the difference between injury severity outcomes for the ADAS and ADS equipped vehicles. To ensure a comprehensive analysis, a multi-source dataset that includes 1,001 ADAS crashes (SAE Level 2 vehicles) and 548 ADS crashes (SAE Level 4 vehicles) is used. Two random parameters multinomial logit models with heterogeneity in the means of random parameters are considered to gain a better understanding of the variables impacting the crash injury severity outcomes for the ADAS (SAE Level 2) and ADS (SAE Level 4) vehicles. It was found that while 67 percent of crashes involving the ADAS equipped vehicles in the dataset took place on a highway, 94 percent of crashes involving ADS took place in more urban settings. The model estimation results also reveal that the weather indicator, driver type indicator, differences in the system sophistication that are captured by both manufacture year and high/low mileage as well as rear and front contact indicators all play a role in the crash injury severity outcomes. The results offer an exploratory assessment of safety performance of the ADAS and ADS equipped vehicles using the real-world data and can be used by the manufacturers and other stakeholders to dictate the direction of their deployment and usage.

Climate change effects and their implications for the financial markets: Evidence from the United Kingdom.

This study aims to examine how the climate affects the behaviour of the stock market. To achieve this, we have drawn on daily data from Jan 2005 to Jan 31, 2023 and several environmental factors (e.g., temperature, humidity, cloud cover and visibility) to account for extreme weather conditions using the 21-day moving average and its standard deviation. The empirical analysis has revealed three key findings regarding the impact of weather on the stock market's behaviour. First, various forms of extreme weather conditions consistently lead to influence stock behaviour. Second, results provide valuable insights into market behaviour and help investors to make more informed investment decisions. Third, the weather conditions have new information about the climate risk and investors should react to it swiftly in light of our findings. The saliency theory can help reconcile the theoretical conflicts between the real options and risk-shifting theories when it comes to investing in uncertain and extreme climate conditions.

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.

Feedback timing-modulated weather prediction reveals relative deficits in both procedural and declarative learning in adults with dyslexia.

A topic of recent debate is the hypothesis that deficits associated with developmental disorders of language, such as reading disability, can be explained by a selective weakness in procedural memory. Adults with (n = 29; RD) and without (n = 29; TD) reading disability completed a weather prediction task under immediate and delayed feedback conditions, that rely on the striatal (procedural) and hippocampal (declarative) circuits, respectively. We examined trial-by-trial accuracy by feedback condition (immediate vs. delayed) and group (RD vs. TD). In the immediate feedback condition, we found the TD group to have a higher learning rate than the RD group. In the delayed feedback condition, we found the TD group reach a high level of accuracy early, and outperform the RD group for the duration of the task. The TD group also made gains in reaction time under both conditions, while the RD group slowed in their responses. Taken together, it appears that while procedural memory is indeed impaired in adults with reading disability, to a lesser extent, declarative memory is also affected. This lends partial support to the PDH, and more broadly to the position that reading disability is associated with general (non-linguistic) difficulties in learning.

Parade safety and planning: A heat balance case study of marching band artists.

Marching band (MB) artists are subject to exertional heat illnesses (EHIs) similar to other active groups like laborers and athletes. Yet, they are an understudied population with no evidence-based heat safety guidelines. Presented here is a case study of the 233rd annual Bristol, RI Independence Day Parade in 2018 that resulted in over 50 EHIs, including 25 from the Saint Anthony Village marching band (MB) from suburban Minneapolis, MN. This research aims to identify the contributing factors that led to the large number of EHIs, as well as guide ensuring the safety of MB artists in future events. A human heat balance model in conjunction with local weather data was used to simulate heat stress on MB artists. Three modeling scenarios were used to isolate the roles of clothing (band uniform vs. t-shirt and shorts), weather (July 4, 2018 vs. 30-year climatology), and metabolic rate (slow, moderate, and brisk marching pacing) on heat stress. The results identify several key factors that increased heat stress. The meteorological conditions were unusually hot, humid, and sunny for Bristol, resulting in reduced cooling from evaporation and convection, and increased radiant heating. Behavioral factors also affect heat stress. The full marching band uniforms reduced evaporative cooling by 50% and the activity levels of marching 4 km over several hours without breaks resulted in conditions that were uncompensable. Finally, it is speculated that a lack of acclimatization for participants from cooler regions may have exacerbated heat-related impacts. These findings highlight several recommendations for MB directors and race organizers, including the use of summer uniforms for anticipated hot conditions, and advance parade planning that includes providing shade/hydration before and after the parade for participants, considering cooler routes that reduce radiant heating and preparing for anticipated heat-related health impacts appropriate for anticipated hot conditions.

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.

Meteorological gaps in audits of pedestrian environments: a scoping review.

BACKGROUND: Weather and season are determinants of physical activity. Therefore, it is important to ensure built environments are designed to mitigate negative impacts of weather and season on pedestrians to prevent these losses. This scoping review aims to identify built environment audits of pedestrian environments developed for use during a specific weather condition or season. Secondly, this review aims to investigate gaps in the inclusion of relevant weather mitigating built environment features in pedestrian environment audit tools. METHODS: Following a standard protocol, a systematic search was executed in CINAHL, Medline and Web of Science to identify built environment audit tools of pedestrian spaces. These databases were chosen since they are well-known to comprehensively cover health as well as multi-disciplinary research publications relevant to health. Studies were screened, and data were extracted from selected documents by two independent reviewers (e.g., psychometric properties and audit items included). Audit items were screened for the inclusion of weather mitigating built environment features, and the tool's capacity to measure temperature, precipitation, seasonal and sustainability impacts on pedestrians was calculated. RESULTS: The search returned 2823 documents. After screening and full text review, 27 articles were included. No tool was found that was developed specifically for use during a specific weather condition or season. Additionally, gaps in the inclusion of weather mitigating items were found for all review dimensions (thermal comfort, precipitation, seasonal, and sustainability items). Poorly covered items were: (1) thermal comfort related (arctic entry presence, materials, textures, and colours of buildings, roads, sidewalk and furniture, and green design features); (2) precipitation related (drain presence, ditch presence, hazards, and snow removal features); (3) seasonal features (amenities, pedestrian scale lighting, and winter destinations and aesthetics); and (4) sustainability features (electric vehicle charging stations, renewable energy, car share, and bike share facilities). CONCLUSIONS: Current built environment audit tools do not adequately include weather / season mitigating items. This is a limitation as it is important to investigate if the inclusion of these items in pedestrian spaces can promote physical activity during adverse weather conditions. Because climate change is causing increased extreme weather events, a need exists for the development of a new built environment audit tool that includes relevant weather mitigating features.

Impact of extreme weather events on the occurrence of infectious diseases in Belgium from 2011 to 2021.

The role of meteorological factors, such as rainfall or temperature, as key players in the transmission and survival of infectious agents is poorly understood. The aim of this study was to compare meteorological surveillance data with epidemiological surveillance data in Belgium and to investigate the association between intense weather events and the occurrence of infectious diseases. Meteorological data were aggregated per Belgian province to obtain weekly average temperatures and rainfall per province and categorized according to the distribution of the variables. Epidemiological data included weekly cases of reported pathogens responsible for gastroenteritis, respiratory, vector-borne and invasive infections normalized per 100 000 population. The association between extreme weather events and infectious events was determined by comparing the mean weekly incidence of the considered infectious diseases after each weather event that occurred after a given number of weeks. Very low temperatures were associated with higher incidences of influenza and parainfluenza viruses, Mycoplasma pneumoniae, rotavirus and invasive Streptococcus pneumoniae and Streptococcus pyogenes infections, whereas very high temperatures were associated with higher incidences of Escherichia coli, Salmonella spp., Shigella spp., parasitic gastroenteritis and Borrelia burgdorferi infections. Very heavy rainfall was associated with a higher incidence of respiratory syncytial virus, whereas very low rainfall was associated with a lower incidence of adenovirus gastroenteritis. This work highlights not only the relationship between temperature or rainfall and infectious diseases but also the most extreme weather events that have an individual influence on their incidence. These findings could be used to develop adaptation and mitigation strategies.