International Journal of Biometeorology最新文献

Climate change, particularly temperature fluctuations, profoundly impacts pest populations. This study focuses on the tomato, a crucial commercial crop in the Eastern Himalayan Region of India. The study examined the impact of varying thermal regimes on tomato fruit borers. Field experiments were conducted at three locations, with altitudes ranging from < 500 to > 1500 m. At lower altitudes, fruit borer incidence commenced earlier (5^th - 18^th March) and peaked higher (1.47 ± 0.34 to 1.73 ± 0.37 larvae/plant), causing more damage (26-29%) as compared to the highest location (~ 9%). The generalized linear mixed model (GLMM) analysis indicated that maximum temperature had significant positive impacts on the H. armigera incidence and fruit damage. Climatic datasets indicate an increase in the temperature of the region during the tomato growing season, thereby increasing the risk of fruit borer impact. As an adaptation option, we evaluated eight different tomato varieties/genotypes and studied biochemical parameters to understand their tolerance. Results showed a strong positive association of fruit borer incidence with total soluble solids whereas negative association with acidity. Cherry tomato (7.62%) and MT-2 (10.04%) had relatively lower fruit damage; MT-3 (50.92 t/ha) and MT-2 (50.57 t/ha) consistently yielded the highest across all locations. Hence, the selection of appropriate genotypes and the development of varieties with suitable characteristics hold the key to fruit borer management. This insight is crucial for developing effective pest management strategies and ensuring sustainable agricultural practices in the region.

Soybean (Glycine max) is the world's most cultivated legume; currently, most of its varieties are Bt. Spodoptera spp. (Lepidoptera: Noctuidae) are important pests of soybean. An artificial neural network (ANN) is an artificial intelligence tool that can be used in the study of spatiotemporal dynamics of pest populations. Thus, this work aims to determine ANN to identify population regulation factors of Spodoptera spp. and predict its density in Bt soybean. For two years, the density of Spodoptera spp. caterpillars, predators, and parasitoids, climate data, and plant age was evaluated in commercial soybean fields. The selected ANN was the one with the weather data from 25 days before the pest's density evaluation. ANN forecasting and pest densities in soybean fields presented a correlation of 0.863. It was found that higher densities of the pest occurred in dry seasons, with less wind, higher atmospheric pressure and with increasing plant age. Pest density increased with the increase in temperature until this curve reached its maximum value. ANN forecasting and pest densities in soybean fields in different years, seasons, and stages of plant development were similar. Therefore, this ANN is promising to be implemented into integrated pest management programs in soybean fields.

The application of innovative systems using low-cost microcontrollers in human biometeorology studies is a promising alternative to conventional monitoring devices, which are usually cost-intensive and provide measurements at specific points, as in stationary meteorological stations. A Portable Low-cost Environmental Monitoring System (PLEMS) aimed at the pedestrian scale is introduced. The backpack-type equipment consists of a microcontroller with attached sensors that assess environmental conditions in a broad sense, integrating measurements of air quality, lighting and noise levels alongside variables typically measured at meteorological stations. The application of the system took place in altogether 12 environmental walks carried out with questionnaire-surveys with concurrent environmental monitoring with the PLEMS in Curitiba, Brazil, a subtropical location characterized by a Cfb climate type. Results allowed us to test the equipment and method of data gathering within a limited period (approximately 50 min) and for a short walking circuit of 800 m. The equipment was successfully able to capture even slightest differences in environmental conditions among points of interest, whereas subjective responses (n= 3843 responses to a total of 11 questions) showed consistency with measured data. From a multi-domain perspective, relevant insights could be obtained for the measured variables.

In India, where dairy production leads globally, infrared thermography (IRT) and short milking tube thermography specifically are vital for managing mastitis. Therefore, the present study focuses on thermal imaging of the udder and short milking tube (SMT) of the milking machine during the peak milking process of Sahiwal cows and Murrah buffaloes during winter, summer, rainy and autumn seasons to identify sub-clinical (SCM) and clinical mastitis (CM) cases using the Darvi DTL007 camera. The udder health was assessed using the California Mastitis Test, Somatic Cell Count (SCC) and IRT throughout the year. Log₁₀SCC and thermogram analysis revealed a difference (p < 0.01) between healthy, SCM, and CM cases during different seasons in both breeds. Further results showed an increase (p < 0.01) in SMT thermograms of SCM and CM cases compared to healthy quarters in Sahiwal cows during winter, summer, rainy, and autumn were 4.26 and 7.51, 2.37 and 4.47, 2.20 and 3.64, 2.90 and 4.94 ºC, respectively and for Murrah buffaloes were 3.56 and 5.55, 2.70 and 3.81, 1.72 and 3.10, 3.14 and 4.42ºC, respectively. The highest degree of increase in milking udder skin surface temperature and SMT of SCM and CM cases compared to healthy quarters was observed during the winter and the least during the rainy season. Thus, regardless of the seasons examined in this study, SMT thermograms effectively assessed SCM and CM.

Maternal exposure to extreme ambient temperature during pregnancy has been proposed as a potential risk factor for birth defects. Comprehensive investigations on this association remain limited, particularly in low- and middle-income countries. This study aims to examine the association between ambient temperature exposure during pregnancy and the risk of birth defects in Brazil, contributing to the broader understanding of environmental influences on birth outcomes. Using a large dataset of over 11 million live birth records, we analyzed 12 categories of birth defects, encompassing a time frame from 2001 to 2018. Ambient temperature data were assigned at the municipality level. For the exposure assessment, we considered two biologically driven pregnancy stages by dividing the gestational period into two specific windows: the first trimester (from week 1 to week 12) and the second trimester (from week 13 to week 28). We employed a two-stage case-control design. In the first stage, we applied a conditional logistic regression model to estimate the odds ratio (OR) for specific birth defects and each of the five Brazilian regions (North, Northeast, Midwest, Southeast, and South). The model was adjusted for potential confounding variables, including PM_2.5, relative humidity, and socioeconomic status. Temporal trends were addressed using time-stratified sampling. In the second stage, we used mixed-effects meta-analysis to pool region-specific estimates. Our analysis revealed a significant association between maternal exposure to higher ambient temperatures during the first trimester and an increased risk of specific birth defect categories, including those affecting the genital organs (OR = 1.08, 95% CI: 1.02; 1.14), digestive system (OR = 1.12, 95% CI: 1.06; 1.19); circulatory system (OR = 1.08, 95% CI: 1.01; 1.17); eyes, ears, face, and neck (OR = 1.08, 95% CI: 1.02; 1.15); benign neoplasms tumors (OR = 1.17, 95% CI: 1.03; 1.32), musculoskeletal system (OR = 1.03, 95% CI: 1.01; 1.05); and other congenital anomalies (OR = 1.22, 95% CI: 1.15; 1.29). The associations with respiratory system, nervous system, and chromosomal anomalies were null. These findings have significant implications for public health policies aimed at mitigating the impact of environmental factors on birth outcomes, both in Brazil and globally.

Long time series of vegetation monitoring can be carried out by remote sensing data, the level of urban greening is objectively described, and the spatial characteristics of plant pollen are indirectly understood. Pollen is the main allergen in patients with seasonal allergic rhinitis. Meteorological factors affect the release and diffusion of pollen. Therefore, studying of the complex relationship between meteorological factors and allergic rhinitis is essential for effective prevention and treatment of the disease. In this study, we leverage remote sensing data for a comprehensive decade-long analysis of urban greening in Tianjin, which exhibits an annual increase in vegetative cover of 0.51 per annum, focusing on its impact on allergic rhinitis through changes in pollen distribution. Utilizing high-resolution imagery, we quantify changes in urban Fractional Vegetation Coverage (FVC) and its correlation with pollen types and allergic rhinitis cases. Our analysis reveals a significant correlation between FVC trends and pollen concentrations, with a surprising value of 0.71, highlighting the influence of urban greenery on allergenic pollen levels. We establish a robust connection between the seasonal patterns of pollen outbreaks and allergic rhinitis consultations, with a noticeable increase in consultations during high pollen seasons. our findings indicate a higher allergenic potential of herbaceous compared to woody vegetation. This nuanced understanding underscores the importance of pollen sensitivity, alongside concentration, in driving allergic rhinitis incidents. Utilizing a Generalized Linear Model, significant features influencing the number of visits for allergic rhinitis (P < 0.05) were identified. Both GLM and LSTM models were employed to forecast the visitation volumes for rhinitis during the spring and summer-autumn of 2022. Upon validation, it was found that the R² values between the simulated and actual values for both GLM and LSTM models surpassed the 95% confidence threshold. Moreover, the R² values for the summer-autumn seasons (GLM: 0.56, LSTM: 0.72) were higher than those for spring (GLM: 0.22, LSTM: 0.47). Comparing the errors between the simulated and actual values of GLM and LSTM models, LSTM exhibited higher simulation precision in both spring and summer-autumn seasons, demonstrating superior simulation performance. Overall, our study pioneers the integration of remote sensing with meteorological and health data for allergic rhinitis forecasting. This integrative approach provides valuable insights for public health planning, particularly in urban settings, and lays the groundwork for advanced, location-specific allergenic pollen forecasting and mitigation strategies.

Temperature-related mortality is the leading cause of weather-related deaths in the United States. Herein, we explore the effect of air masses (AMs) - a relatively novel and holistic measure of environmental conditions - on human mortality across 61 cities in the United States. Geographic and seasonal differences in the effects of each AM on deseasonalized and detrended anomalous lagged mortality are examined using simple descriptive statistics, one-way analyses of variance, relative risks of excess mortality, and regression-based artificial neural network (ANN) models. Results show that AMs are significantly related to anomalous mortality in most US cities, and in most seasons. Of note, two of the three cool AMs (Cool and Dry-Cool) each show a strong, but delayed mortality response in all seasons, with peak mortality 2 to 4 days after they occur, with the Dry-Cool AM having nearly a 15% increased risk of excess mortality. Humid-Warm (HW) air masses are associated with increases in deaths in all seasons 0 to 1 days after they occur. In most seasons, these near-term mortality increases are offset by reduced mortality for 1-2 weeks afterwards; however, in summer, no such reduction is noted. The Warm and Dry-Warm AMs show slightly longer periods of increased mortality, albeit slightly less intensely as compared with HW, but with a similar lag structure by season. Meanwhile, the most seasonally consistent results are with transitional weather, whereby passing cold fronts are associated with a significant decrease in mortality 1 day after they occur, while warm fronts are associated with significant increases in mortality at that same lag time. Finally, ANN modeling reveals that AM-mortality relationships gleaned from a combined meta-analysis can actually lead to more skillful modeling of these relationships than models trained on some individual cities, especially in the cities where such relationships might be masked due to low average daily mortality.

The present study was conducted to understand transcriptional response of skin fibroblast of yak (Bos grunniens) and cows of Bos indicus origin to hypoxia stress. Six primary fibroblast cell lines derived from three individuals each of Ladakhi yak (Bos grunniens) and Sahiwal cows (Bos indicus) were exposed to low oxygen concentration for a period of 24 h, 48 h and 72 h. The expression of 10 important genes known to regulate hypoxia response such as HIF1A, VEGFA, EPAS1, ATP1A1, GLUT1, HMOX1, ECE1, TNF-A, GPx and SOD were evaluated in fibroblast cells of Ladakhi yak (LAY-Fb) and Sahiwal cows (SAC-Fb) during pre- and post-hypoxia stress. A panel of 10 reference genes (GAPDH, RPL4, EEF1A1, RPS9, HPRT1, UXT, RPS23, B2M, RPS15, ACTB) were also evaluated for their expression stability to perform accurate normalization. The expression of HIF1A was significantly (p < 0.05) induced in both LAY-Fb (2.29-fold) and SAC-Fb (2.07-fold) after 24 h of hypoxia stress. The angiogenic (VEGFA), metabolic (GLUT1) and antioxidant genes (SOD and GPx) were also induced after 24 h of hypoxia stress. However, EPAS1 and ATP1A1 induced significantly (p < 0.05) after 48 h whereas, ECE1 expression induced significantly (p < 0.05) at 72 h after exposure to hypoxia. The TNF-alpha which is a pro-inflammatory gene induced significantly (p < 0.05) at 24 h in SAC-Fb and at 72 h in LAY-Fb. The induction of hypoxia associated genes indicated the utility of skin derived fibroblast as cellular model to evaluate transcriptome signatures post hypoxia stress in populations adapted to diverse altitudes.

Intense urban development and high urban density cause the thermal environment in urban centers to deteriorate continuously, affecting the quality of the living environment. In this study, 707.49 hectares of land in the central area of Changsha were divided into 121 plots. 11 microclimate-related morphological indicators were comprehensively selected, and the K-means method was used for cluster analysis. Then, the relationship between morphological clusters and the thermal environment was explored by simulating the thermal environment of the study area with ENVI-met. First, five spatial types were found to characterize the area: high-level with high floor area ratio, low density, and low greenery; middle-level with high floor area ratio high density; medium-capacity with high density and small volume; low-level with low density and high greenery; and low floor area ratio, low density, and high greenery. Second, the building windward surface density, sky openness, building density, floor area ratio and green space rate affect the thermal environment. Third, Cluster3 had the highest average air temperature (Ta), followed by Cluster5, furthermore Clusters4, 1, and2 had relatively low Ta. The spatial vitality index and green space rate in Cluster1; the area-weighted building shape index, average building volume and sky openness in Cluster2; green space rate in Cluster3; indicators such as the floor area ratio and green space rate in Cluster4; indicators such as the impervious surface rate and green space rate in Cluster5 had greater influences on Ta. Fourthly, simply increasing the area of green space cannot maximize the cooling effect of green spaces. Instead, constructing an equalized greening network can better regulate the thermal environment. Fifthly, the results provide a scientific basis for the design and the regulation of urban centers.

Extreme heat alerts are the most common form of weather forecasting services used in Australia, yet very limited studies have documented their effectiveness in improving health outcomes. This study aimed to examine the temporal changes in temperature-related mortality in relation to the activation of the heat-health alert and response system (HARS) in the State of Victoria, Australia. We examined the relationship between temperatures and mortality using quasi-Poisson regression and the distributed lag non-linear model (dlnm) and compared the temperature-mortality association between the two periods: period 1- prior-HARS (1992-2009) and period 2- post-HARS (2010-2019). Since the HARS heavily weights heatwave effects, we also compared the main effects of heatwave events between the two periods. The heatwaves were defined for three levels, including 3 consecutive days at 97th, 98th, and 99th percentiles. We also controlled the potential confounding effect of seasonality by including a natural cubic B-spline of the day of the year with equally spaced knots and 8 degrees of freedom per year. The exposure-response curve reveals the temperature mortality was reduced in period 2 in comparison with period 1. The relative risk ratios (RRR) of Period 2 over Period 1 were all less than one and gradually decreased from 0.86 (95% CI, 0.72-1.03) to 0.64 (95% CI, 0.33-1.22), and the differences in attributable risk percent increased from 13.2 to 25.3%. The reduction in the risk of heatwave-related deaths decreased by 3.4% (RR_p1 1.068, 95% CI, 1.024-1.112 versus RR_p2 1.034, 95% CI, 0.986-1.082) and 10% (RR_p1 1.16, 95% CI, 1.10-1.22 versus RR_p2 1.06, 95% CI, 1.002-1.119) for all groups of people. The study indicated a decrease in heat-related mortality following the operation of HARS in Victoria under extreme heat and high-intensity heatwaves conditions. Further studies could investigate the extent of changes in mortality among populations of differing socio-economic groups during the operation of the heat-health alert system.