International Journal of Biometeorology最新文献

The growing season is the period when weather conditions (e.g., precipitation, temperature, wind, etc.) in a given area support plant growth and development. This study examines how climate change has influenced the growing season duration in Poland over the past nearly 80 years, based solely on phenological observations. The research was conducted for two 15-year periods: the pre-warming period (1946–1960), before significant global climate warming became evident, and the warming period (2007–2021), characterized by the additional influence of the “greenhouse component” on climate trends. The former dataset was sourced from the Yearbooks of Phenological Observations, which had not been previously available to a wider audience and were digitized by the authors for this study. The latter dataset was obtained from the database of the Institute of Meteorology and Water Management–National Research Institute. The results indicate an increasingly earlier onset of the vegetation period and a slight delay in its end over time, leading to an extended growing season. Today, as a result of climate change, its duration has increased to over 240 days in the west, while in the central lowlands and the Lublin Upland, it has reached 220–230 days. However, it remains almost unchanged along the coast and in northeastern Poland. Due to its location in a transitional temperate climate zone, Poland experiences high weather variability, which is also reflected in fluctuations in the start dates and duration of growing seasons.

Amid the effects of climate change and rising urbanization, the interaction between urban heat islands (UHIs) and background climate factors has become critical to study. This study investigates the spatiotemporal variation of atmospheric urban heat island intensity (AUHII) in Guangdong Province, China, and evaluates the long-term influence of key climate variables: precipitation, relative humidity, and wind speed on AUHI. An integrated modeling approach was used, combining econometric techniques (Fully Modified Ordinary Least Squares and Dynamic Ordinary Least Squares) with machine learning and deep learning methods. The Random Forest (RF) model served as an initial benchmark, followed by a Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) framework to improve predictive accuracy. Results showed significant spatial and seasonal variations, with AUHII ranging from − 2.6 to 2.3 °C for daytime, nighttime, and mean values. Seasonal extremes were observed in winter (-4.1 to 3.9 °C) and summer (-1.8 to 1.4 °C), with nighttime and winter exhibiting the strongest AUHI effects, particularly in western and southern cities. Relative humidity was the most influential factor, followed by precipitation. While the RF model identified key predictors, the CNN-LSTM model demonstrated stronger generalization, achieving testing R² values above 0.75 across most cities. Our findings enhance the understanding of the linkages between background climate variables and the AUHI effect, providing insight that can help urban planners and policymakers develop strategies to mitigate the effects of atmospheric urban heat islands.

Thunderstorm asthma is an incompletely understood phenomenon with significant public health implications. Thunderstorm asthma has not previously been documented or scientifically investigated in South Australia. This study explored the association between severe thunderstorm activity and markers of asthma morbidity across six regions in South Australia over the period 2003 to 2017. The morbidity outcomes examined were ambulance callouts, emergency department presentations and hospital admissions for asthma among adults and children. Poisson regression analyses were used to explore the associations, adjusted for environmental covariates including daily weather variables, pollen counts and air pollutant concentrations (where available, noting that pollen count data were only available for a single location in Adelaide). Results demonstrate an increase in the risk of asthma in association with severe thunderstorm activity in the Adelaide Metropolitan and Hills region, particularly among children. Seasonal trends are apparent, with thunderstorms associated with an increase in the risk of childhood asthma in the warmer months in the Adelaide region. Interestingly, daily pollen count was not found to be a significant mediator in the relationship between thunderstorms and asthma in this study. Further research is needed to better understand the relationships between thunderstorms and asthma in South Australia and the potential role of aeroallergens and other environmental triggers.

Urban citizens are particularly exposed to heat stress during heatwaves due to the urban climate conditions. Introducing more trees, changing building density and surface cover and materials are examples of planning measures that can be used to mitigate heat stress. One challenge as an urban planner is to have knowledge on which mitigation measure to implement to achieve the highest cooling effect with regards to outdoor heat stress at different spatial scales. The aim of this high-resolution modelling of outdoor thermal comfort on city-wide domains is to examine how different real-world urban settings reduce or exacerbate heat stress with regards to building density (plan area index), tree fraction, and ground cover. Here, we exploit the open-source tool Urban Multi-scale Environmental Predictor (UMEP), to investigate how real-world data on building density, tree fraction, and ground cover influence thermal comfort in the three largest cities in Sweden. Mean radiant temperature (T_mrt) and two thermal comfort indices are calculated and compared: Physiological Equivalent Temperature (PET) and Universal Thermal Comfort Index (UTCI). Automated chain processes using Python scripting is demonstrated, making it possible to derive microscale outdoor thermal comfort information (2-meter resolution) using a standard personal computer and open data sources. Results show that tree fraction is the single most effective outdoor heat mitigation measure, especially in areas with low building density. Results also show that building fraction has a minor cooling effect. This is probably due to the fact that shadowing at street level is dominated by trees due their 3D characteristics including trunk zones. T_mrt shows very similar results compared with PET and UTCI, indicating that T_mrt can capture the spatial variations of heat stress during warm, clear and calm days. Since trees is the single most effective measure to mitigate heat stress, it should be incorporated when creating practical guidelines to resilient urban planning strategies against heat stress.

Elevated levels of tropospheric ozone (O₃) caused by anthropogenic emissions of nitrogen oxides (NOx) and volatile organic compounds (VOCs) have a negative impact on human health, crops, and ecosystems. Therefore, estimation of ground-level ozone trends is necessary to determine the burden of ozone on human health. Its formation undergoes complex photochemical reactions and processes that are non-linearly related to its precursors. Despite a relatively clear understanding of the O₃ formation mechanism, effectively characterizing and analyzing its source and distribution at precise spatio-temporal resolutions remains a significant challenge. The review summarizes the current knowledge of tropospheric O₃ in recent years, including its sources, trends and biological effects. It contributes to the understanding of how ozone and its precursors are affected and the impact they have on the environment, which contributes to the effective assessment and control of surface ozone.

Climate change negatively impacts İstanbul as a Mediterranean city. The observed trends of air temperature over the last decades shows an overall increase in air temperature and extreme events. İstanbul is also at an increased risk of heat stress due to the effect of increasing urbanization. Reliable estimates of air temperature’s health impacts in İstanbul are needed to understand the relationship between city’s climate and health of its residents. This study examined the relationship between ambient temperatures and respiratory, cardiovascular, and total (non-accidental) mortality to reveal the health effects of ambient temperatures between 2007 and 2012 in İstanbul. A distributed lag non-linear model (DLNM) paired with a quasi-Poisson regression was employed to analyze the city-specific lag effects of temperature on mortality. The temperature–mortality associations were modeled using a period of up to 21 days (lag 0–20) to examine the delayed and non-linear effects of cold and hot temperatures after the day of exposure. The findings of this study showed that extreme cold temperatures have the highest relative risk for cardiovascular mortality and extreme hot temperatures have the highest relative risks on respiratory and total mortality. Extreme hot days (above 97.5th percentile) and extreme cold days (below 2.5th percentile) accounted for 1.9 (95% CI [CI], 0–7.5) and 9.0 (95% CI, 3.1–21.0) excess deaths for every 1000 cardiovascular deaths, respectively.

Although studies have demonstrated the influence of meteorological factors on morbidity and mortality in type 2 diabetes mellitus (T2DM), research focusing specifically on their impact on hospitalization for T2DM with complications remains Limited.This study aimed to investigate the impact of meteorological factors on hospitalization for type 2 diabetes mellitus (T2DM) with complications. The distributed lag nonlinear modelling (DLNM) was used to investigate this effect of temperature and relative humidity (RH) on hospitalization for T2DM with complications. A total of 50,108 T2DM hospitalizations with complications were performed from 2014 to 2019 in Lanzhou, China. Compared to the reference temperature of 12.7 °C, low temperature (-4.1 °C) had harmful effects, with the maximum impact at lag0-1 (cumulative RR = 1.0265, 95% CI: 1.0024,1.0512). High RH (76.70%), compared to the reference of 51.17%, also had hazardous effects, with the maximum impact at lag0-21 (cumulative RR = 1.2307, 95% CI: 1.1265,1.3445). Subgroup analyses showed that low temperature and high RH particularly affected males and individuals aged < 65 years. Low temperature and high RH had a harmful impact on T2DM hospitalizations with complications.

This study investigated the association between temperature and hospitalizations for respiratory diseases (RD) among suburban farmers in Zhangye, Wuwei, Dingxi, and Tianshui in Gansu province. We collected the daily hospital admission data for RD in four cities from the local public hospitals, covering the period from January 1, 2018 to December 31, 2019. The association was estimated using a quasi-Poisson generalized additive model (GAM) and distributed lag nonlinear model (DLNM) to account for lagged and non-linear effects, and the association varies geographically. Our study found that both low and high temperatures were associated with RD morbidity, and had significant lag effects in four cities, the risk of temperature on RD morbidity increased significantly in Zhangye (low temperature: RR = 2.107, 95%CI: 1.749, 2.540; high temperature: RR = 2.407, 95%CI: 1.932, 2.998), Wuwei (low temperature: RR = 1.758, 95%CI: 1.134, 2.726; high temperature: RR = 1.936, 95%CI: 1.541, 2.431), Dingxi (low temperature: RR = 1.876, 95%CI: 1.593, 2.208; high temperature: RR = 2.432, 95%CI: 1.932, 3.061) and Tianshui (low temperature: RR = 1.083, 95%CI: 1.021, 1.150; high temperature: RR = 1.630, 95%CI: 1.191, 2.229). Susceptible demographics linked to RD morbidity differ by gender and age group in four cities. For Wuwei, Dingxi, and Tianshui, females exhibited higher adverse effects when exposed to both low and high temperatures than males. By contrast, males in Zhangye showed higher relative risks (RR) than females. Additionally, in Zhangye, Wuwei, and Tianshui, low temperature had a greater impact on patients aged < 65 years than on those aged ≥ 65 years. For high temperature, patients aged < 65 years in Zhangye, Wuwei, and Dingxi were more susceptible. These findings emphasize the need for region-tailored early warning systems and targeted preventive measures for vulnerable groups. The application of distributed lag non-linear modeling in a suburban agricultural population offers novel insights into environmental epidemiology in resource-constrained settings. Future research should prioritize refining temperature-health threshold definitions and leveraging micro-level exposure data to inform adaptive public health strategies.

That moonlight influence insect is a controversial question. Climatic factors also affect insect behaviors and population numbers. Short term researches were too many, but long term researches was lacking. Understanding insect population dynamics is helpful in integrated pest management. Cotton bollworm (Helicoverpa armigera) damages cotton, and thus, data from Bachu County, China, collected during the period of 1991–2015 were analyzed to assess the effects of climate factors and lunar phases on cotton bollworm adult moths. The results showed that the population number captured increased with a decrease in lunar light brightness; the greatest numbers appeared during the new moon phase and the smallest numbers occurred during the full moon phase. The effect of increasing lunar light brightness on the capture number was greater than that of decreasing lunar light brightness. Increased temperature enhanced the number of captured H. armigera moths. An increase in cloud cover also increased the number captured. Sun shine hours and wind speed had negative correlations with moths captured. Relative humidity and precipitation in this arid region had no correlation with the number of H. armigera moths captured. However, when the partial least squares regression was employed to assess the relative influence of each factor, the climate factors showed different extent effects during different moon phases. Thus, when analyze light trap moths, single factor might exaggerate itself impacts and ignored other potential affecting factors. As the moonlight influenced the number of H. armigera moths captured, this should be considered when predicting population dynamics.

Different geographical locations and climatic environments lead to different impacts of specific air pollutants on the relative risk (RR) of asthma morbidity (i.e., new-onset asthma, outpatient visits, emergency department visits, and hospital admissions) in the Northern Hemisphere. Therefore, it is necessary to integrate existing data to assess the impact of short-term exposure to pollutants on the RR of asthma morbidity in the Northern Hemisphere. We conducted a systematic review and meta-analysis to evaluate the effects of short-term exposure to particulate matter (PM_2.5, PM₁₀), sulfur dioxide (SO₂), nitrogen dioxide (NO₂), and ozone (O₃) on the RR of asthma morbidity. A comprehensive literature search was performed across three major databases: Web of Science, China National Knowledge Infrastructure (CNKI), and PubMed. Ultimately, 14 studies were included in the final analysis. Heterogeneity was evaluated using the Cochran Q test and I² statistics, and publication bias was assessed using Egger’s test. The meta-analysis revealed that short-term exposure to five types of air pollutants had a significant impact on the RR of asthma morbidity. Among them, NO₂ exhibited the most significant adverse health effects (RR = 1.02, 95% CI: 1.01–1.03). Stratified analysis showed that residents in the temperate regions were more affected by exposure to SO₂, NO₂, and O₃, whereas residents in the tropical regions were more affected by PM₁₀, and the regional differences in the impact of PM_2.5 on the health of residents in the two regions were not significant. The Egger’s test results suggested the presence of a potential publication bias for PM_2.5 and SO₂. In contrast, for PM₁₀, NO₂, and O₃, no publication bias was detected. Therefore, an efficient and resilient public health system should be established.